commit 344816a5d82d1b27c46c2a3c9ef047dcd83ccf1b Author: wehub-resource-sync Date: Mon Jul 13 12:35:17 2026 +0800 chore: import upstream snapshot with attribution diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..cfffba8 --- /dev/null +++ b/.gitignore @@ -0,0 +1,18 @@ +.idea +*.pyc +__pycache__/ +*.sh +local_tools/ +*.ckpt +*.pth +infer_out/ +*.onnx +/data/* +!/data/.gitkeep +/checkpoints/* +!/checkpoints/.gitkeep +/venv/ +/artifacts/ + +.vscode +.ipynb_checkpoints/ diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..58b657f --- /dev/null +++ b/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright 2023 Team OpenVPI + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/README.md b/README.md new file mode 100644 index 0000000..624673a --- /dev/null +++ b/README.md @@ -0,0 +1,77 @@ +# DiffSinger (OpenVPI maintained version) + +[![arXiv](https://img.shields.io/badge/arXiv-Paper-.svg)](https://arxiv.org/abs/2105.02446) +[![downloads](https://img.shields.io/github/downloads/openvpi/DiffSinger/total.svg)](https://github.com/openvpi/DiffSinger/releases) +[![Bilibili](https://img.shields.io/badge/Bilibili-Demo-blue)](https://www.bilibili.com/video/BV1be411N7JA/) +[![license](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/openvpi/DiffSinger/blob/main/LICENSE) + +This is a refactored and enhanced version of _DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism_ based on the original [paper](https://arxiv.org/abs/2105.02446) and [implementation](https://github.com/MoonInTheRiver/DiffSinger), which provides: + +- Cleaner code structure: useless and redundant files are removed and the others are re-organized. +- Better sound quality: the sampling rate of synthesized audio are adapted to 44.1 kHz instead of the original 24 kHz. +- Higher fidelity: improved acoustic models and diffusion sampling acceleration algorithms are integrated. +- More controllability: introduced variance models and parameters for prediction and control of pitch, energy, breathiness, etc. +- Production compatibility: functionalities are designed to match the requirements of production deployment and the SVS communities. + +| Overview | Variance Model | Acoustic Model | +|:-------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------:| +| arch-overview | arch-variance | arch-acoustic | + +## User Guidance + +> Navigation page including all tutorials and resources (Chinese): https://diffsinger.com + +- **Installation & basic usages**: See [Getting Started](docs/GettingStarted.md) +- **Dataset creation pipelines & tools**: See [MakeDiffSinger](https://github.com/openvpi/MakeDiffSinger) +- **Best practices & tutorials**: See [Best Practices](docs/BestPractices.md) +- **Editing configurations**: See [Configuration Schemas](docs/ConfigurationSchemas.md) +- **Deployment & production**: [OpenUTAU](https://github.com/stakira/OpenUtau), [DiffScope (under development)](https://github.com/diffscope/diffscope-project) +- **Communication groups**: [QQ Group](http://qm.qq.com/cgi-bin/qm/qr?_wv=1027&k=fibG_dxuPW5maUJwe9_ya5-zFcIwaoOR&authKey=ZgLCG5EqQVUGCID1nfKei8tCnlQHAmD9koxebFXv5WfUchhLwWxb52o1pimNai5A&noverify=0&group_code=907879266) (907879266), [Discord server](https://discord.gg/wwbu2JUMjj) + +## Progress & Roadmap + +- **Progress since we forked into this repository**: See [Releases](https://github.com/openvpi/DiffSinger/releases) +- **Roadmap for future releases**: See [Project Board](https://github.com/orgs/openvpi/projects/1) +- **Thoughts, proposals & ideas**: See [Discussions](https://github.com/openvpi/DiffSinger/discussions) + +## Architecture & Algorithms + +TBD + +## Development Resources + +TBD + +## References + +### Original Paper & Implementation + +- Paper: [DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism](https://arxiv.org/abs/2105.02446) +- Implementation: [MoonInTheRiver/DiffSinger](https://github.com/MoonInTheRiver/DiffSinger) + +### Generative Models & Algorithms + +- Denoising Diffusion Probabilistic Models (DDPM): [paper](https://arxiv.org/abs/2006.11239), [implementation](https://github.com/hojonathanho/diffusion) + - [DDIM](https://arxiv.org/abs/2010.02502) for diffusion sampling acceleration + - [PNDM](https://arxiv.org/abs/2202.09778) for diffusion sampling acceleration + - [DPM-Solver++](https://github.com/LuChengTHU/dpm-solver) for diffusion sampling acceleration + - [UniPC](https://github.com/wl-zhao/UniPC) for diffusion sampling acceleration +- Rectified Flow (RF): [paper](https://arxiv.org/abs/2209.03003), [implementation](https://github.com/gnobitab/RectifiedFlow) + +### Dependencies & Submodules + +- [RoPE](https://github.com/lucidrains/rotary-embedding-torch) for transformer encoder +- [Mix-LN](https://www.isca-archive.org/interspeech_2024/hwang24_interspeech.pdf) for cross-speaker/lingual learning +- [HiFi-GAN](https://github.com/jik876/hifi-gan) and [NSF](https://github.com/nii-yamagishilab/project-NN-Pytorch-scripts/tree/master/project/01-nsf) for waveform reconstruction +- [pc-ddsp](https://github.com/yxlllc/pc-ddsp) for waveform reconstruction +- [RMVPE](https://github.com/Dream-High/RMVPE) and yxlllc's [fork](https://github.com/yxlllc/RMVPE) for pitch extraction +- [Vocal Remover](https://github.com/tsurumeso/vocal-remover) and yxlllc's [fork](https://github.com/yxlllc/vocal-remover) for harmonic-noise separation + +## Disclaimer + +Any organization or individual is prohibited from using any functionalities included in this repository to generate someone's speech without his/her consent, including but not limited to government leaders, political figures, and celebrities. If you do not comply with this item, you could be in violation of copyright laws. + +## License + +This forked DiffSinger repository is licensed under the [Apache 2.0 License](LICENSE). + diff --git a/README.wehub.md b/README.wehub.md new file mode 100644 index 0000000..8cbd17a --- /dev/null +++ b/README.wehub.md @@ -0,0 +1,7 @@ +# WeHub 来源说明 + +- 原始项目：`openvpi/DiffSinger` +- 原始仓库：https://github.com/openvpi/DiffSinger +- 导入方式：上游默认分支的最新快照 +- 原作者、版权和许可证信息以原始仓库及本仓库 LICENSE 为准 +- 本文件仅用于记录来源，不代表 WeHub 是原项目作者 diff --git a/augmentation/spec_stretch.py b/augmentation/spec_stretch.py new file mode 100644 index 0000000..dd88758 --- /dev/null +++ b/augmentation/spec_stretch.py @@ -0,0 +1,92 @@ +from copy import deepcopy + +import librosa +import numpy as np +import torch + +from basics.base_augmentation import BaseAugmentation, require_same_keys +from basics.base_pe import BasePE +from modules.fastspeech.param_adaptor import VARIANCE_CHECKLIST +from modules.fastspeech.tts_modules import LengthRegulator +from utils.binarizer_utils import get_mel_torch, get_mel2ph_torch +from utils.hparams import hparams +from utils.infer_utils import resample_align_curve + + +class SpectrogramStretchAugmentation(BaseAugmentation): + """ + This class contains methods for frequency-domain and time-domain stretching augmentation. + """ + + def __init__(self, data_dirs: list, augmentation_args: dict, pe: BasePE = None): + super().__init__(data_dirs, augmentation_args) + self.device = 'cuda' if torch.cuda.is_available() else 'cpu' + self.lr = LengthRegulator().to(self.device) + self.pe = pe + + @require_same_keys + def process_item(self, item: dict, key_shift=0., speed=1., replace_spk_id=None) -> dict: + aug_item = deepcopy(item) + waveform, _ = librosa.load(aug_item['wav_fn'], sr=hparams['audio_sample_rate'], mono=True) + mel = get_mel_torch( + waveform, hparams['audio_sample_rate'], num_mel_bins=hparams['audio_num_mel_bins'], + hop_size=hparams['hop_size'], win_size=hparams['win_size'], fft_size=hparams['fft_size'], + fmin=hparams['fmin'], fmax=hparams['fmax'], + keyshift=key_shift, speed=speed, device=self.device + ) + + aug_item['mel'] = mel + + if speed != 1. or hparams['use_speed_embed']: + aug_item['length'] = mel.shape[0] + aug_item['speed'] = int(np.round(hparams['hop_size'] * speed)) / hparams['hop_size'] # real speed + aug_item['seconds'] /= aug_item['speed'] + aug_item['ph_dur'] /= aug_item['speed'] + aug_item['mel2ph'] = get_mel2ph_torch( + self.lr, torch.from_numpy(aug_item['ph_dur']), aug_item['length'], self.timestep, device=self.device + ).cpu().numpy() + + f0, _ = self.pe.get_pitch( + waveform, samplerate=hparams['audio_sample_rate'], length=aug_item['length'], + hop_size=hparams['hop_size'], f0_min=hparams['f0_min'], f0_max=hparams['f0_max'], + speed=speed, interp_uv=True + ) + aug_item['f0'] = f0.astype(np.float32) + + # NOTE: variance curves are directly resampled according to speed, + # despite how frequency-domain features change after the augmentation. + # For acoustic models, this can bring more (but not much) difficulty + # to learn how variance curves affect the mel spectrograms, since + # they must realize how the augmentation causes the mismatch. + # + # This is a simple way to combine augmentation and variances. However, + # dealing variance curves like this will decrease the accuracy of + # variance controls. In most situations, not being ~100% accurate + # will not ruin the user experience. For example, it does not matter + # if the energy does not exactly equal the RMS; it is just fine + # as long as higher energy can bring higher loudness and strength. + # The neural networks itself cannot be 100% accurate, though. + # + # There are yet other choices to simulate variance curves: + # 1. Re-extract the features from resampled waveforms; + # 2. Re-extract the features from re-constructed waveforms using + # the transformed mel spectrograms through the vocoder. + # But there are actually no perfect ways to make them all accurate + # and stable. + for v_name in VARIANCE_CHECKLIST: + if v_name in item: + aug_item[v_name] = resample_align_curve( + aug_item[v_name], + original_timestep=self.timestep, + target_timestep=self.timestep * aug_item['speed'], + align_length=aug_item['length'] + ) + + if key_shift != 0. or hparams['use_key_shift_embed']: + if replace_spk_id is None: + aug_item['key_shift'] = key_shift + else: + aug_item['spk_id'] = replace_spk_id + aug_item['f0'] *= 2 ** (key_shift / 12) + + return aug_item diff --git a/basics/base_augmentation.py b/basics/base_augmentation.py new file mode 100644 index 0000000..ac71f48 --- /dev/null +++ b/basics/base_augmentation.py @@ -0,0 +1,28 @@ +from utils.hparams import hparams + + +class BaseAugmentation: + """ + Base class for data augmentation. + All methods of this class should be thread-safe. + 1. *process_item*: + Apply augmentation to one piece of data. + """ + def __init__(self, data_dirs: list, augmentation_args: dict): + self.raw_data_dirs = data_dirs + self.augmentation_args = augmentation_args + self.timestep = hparams['hop_size'] / hparams['audio_sample_rate'] + + def process_item(self, item: dict, **kwargs) -> dict: + raise NotImplementedError() + + +def require_same_keys(func): + def run(*args, **kwargs): + item: dict = args[1] + res: dict = func(*args, **kwargs) + assert set(item.keys()) == set(res.keys()), 'Item keys mismatch after augmentation.\n' \ + f'Before: {sorted(item.keys())}\n' \ + f'After: {sorted(res.keys())}' + return res + return run diff --git a/basics/base_binarizer.py b/basics/base_binarizer.py new file mode 100644 index 0000000..397bd83 --- /dev/null +++ b/basics/base_binarizer.py @@ -0,0 +1,386 @@ +import json +import pathlib +import pickle +import random +import shutil +import warnings +from copy import deepcopy + +import numpy as np +import torch +from tqdm import tqdm + +from utils.hparams import hparams +from utils.indexed_datasets import IndexedDatasetBuilder +from utils.multiprocess_utils import chunked_multiprocess_run +from utils.phoneme_utils import load_phoneme_dictionary +from utils.plot import distribution_to_figure + + +class BinarizationError(Exception): + pass + + +class BaseBinarizer: + """ + Base class for data processing. + 1. *process* and *process_data_split*: + process entire data, generate the train-test split (support parallel processing); + 2. *process_item*: + process singe piece of data; + 3. *get_pitch*: + infer the pitch using some algorithm; + 4. *get_align*: + get the alignment using 'mel2ph' format (see https://arxiv.org/abs/1905.09263). + 5. phoneme encoder, voice encoder, etc. + + Subclasses should define: + 1. *load_metadata*: + how to read multiple datasets from files; + 2. *train_item_names*, *valid_item_names*, *test_item_names*: + how to split the dataset; + 3. load_ph_set: + the phoneme set. + """ + + def __init__(self, datasets=None, data_attrs=None): + if datasets is None: + datasets = hparams['datasets'] + self.datasets = datasets + self.raw_data_dirs = [pathlib.Path(ds['raw_data_dir']) for ds in self.datasets] + self.binary_data_dir = pathlib.Path(hparams['binary_data_dir']) + self.data_attrs = [] if data_attrs is None else data_attrs + + self.binarization_args = hparams['binarization_args'] + self.augmentation_args = hparams.get('augmentation_args', {}) + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + self.spk_map = {} + self.spk_ids = None + self.build_spk_map() + + self.lang_map = {} + self.dictionaries = hparams['dictionaries'] + self.build_lang_map() + + self.items = {} + self.item_names: list = None + self._train_item_names: list = None + self._valid_item_names: list = None + + self.phoneme_dictionary = load_phoneme_dictionary() + self.timestep = hparams['hop_size'] / hparams['audio_sample_rate'] + + def build_spk_map(self): + spk_ids = [ds.get('spk_id') for ds in self.datasets] + assigned_spk_ids = {spk_id for spk_id in spk_ids if spk_id is not None} + idx = 0 + for i in range(len(spk_ids)): + if spk_ids[i] is not None: + continue + while idx in assigned_spk_ids: + idx += 1 + spk_ids[i] = idx + assigned_spk_ids.add(idx) + assert max(spk_ids) < hparams['num_spk'], \ + f'Index in spk_id sequence {spk_ids} is out of range. All values should be smaller than num_spk.' + + for spk_id, dataset in zip(spk_ids, self.datasets): + spk_name = dataset['speaker'] + if spk_name in self.spk_map and self.spk_map[spk_name] != spk_id: + raise ValueError(f'Invalid speaker ID assignment. Name \'{spk_name}\' is assigned ' + f'with different speaker IDs: {self.spk_map[spk_name]} and {spk_id}.') + self.spk_map[spk_name] = spk_id + self.spk_ids = spk_ids + + print("| spk_map: ", self.spk_map) + + def build_lang_map(self): + assert len(self.dictionaries.keys()) <= hparams['num_lang'], \ + 'Number of languages must not be greater than num_lang!' + for dataset in self.datasets: + assert dataset['language'] in self.dictionaries, f'Unrecognized language name: {dataset["language"]}' + + for lang_id, lang_name in enumerate(sorted(self.dictionaries.keys()), start=1): + self.lang_map[lang_name] = lang_id + + print("| lang_map: ", self.lang_map) + + def load_meta_data(self, raw_data_dir: pathlib.Path, ds_id, spk, lang) -> dict: + raise NotImplementedError() + + def split_train_valid_set(self, prefixes: list): + """ + Split the dataset into training set and validation set. + :return: train_item_names, valid_item_names + """ + prefixes = {str(pr): 1 for pr in prefixes} + valid_item_names = {} + # Add prefixes that specified speaker index and matches exactly item name to test set + for prefix in deepcopy(prefixes): + if prefix in self.item_names: + valid_item_names[prefix] = 1 + prefixes.pop(prefix) + # Add prefixes that exactly matches item name without speaker id to test set + for prefix in deepcopy(prefixes): + matched = False + for name in self.item_names: + if name.split(':')[-1] == prefix: + valid_item_names[name] = 1 + matched = True + if matched: + prefixes.pop(prefix) + # Add names with one of the remaining prefixes to test set + for prefix in deepcopy(prefixes): + matched = False + for name in self.item_names: + if name.startswith(prefix): + valid_item_names[name] = 1 + matched = True + if matched: + prefixes.pop(prefix) + for prefix in deepcopy(prefixes): + matched = False + for name in self.item_names: + if name.split(':')[-1].startswith(prefix): + valid_item_names[name] = 1 + matched = True + if matched: + prefixes.pop(prefix) + + if len(prefixes) != 0: + warnings.warn( + f'The following rules in test_prefixes have no matching names in the dataset: {", ".join(prefixes.keys())}', + category=UserWarning + ) + warnings.filterwarnings('default') + + valid_item_names = list(valid_item_names.keys()) + assert len(valid_item_names) > 0, 'Validation set is empty!' + train_item_names = [x for x in self.item_names if x not in set(valid_item_names)] + assert len(train_item_names) > 0, 'Training set is empty!' + + return train_item_names, valid_item_names + + @property + def train_item_names(self): + return self._train_item_names + + @property + def valid_item_names(self): + return self._valid_item_names + + def meta_data_iterator(self, prefix): + if prefix == 'train': + item_names = self.train_item_names + else: + item_names = self.valid_item_names + for item_name in item_names: + meta_data = self.items[item_name] + yield item_name, meta_data + + def process(self): + # load each dataset + test_prefixes = [] + for ds_id, dataset in enumerate(self.datasets): + items = self.load_meta_data( + pathlib.Path(dataset['raw_data_dir']), + ds_id=ds_id, spk=dataset['speaker'], lang=dataset['language'] + ) + self.items.update(items) + test_prefixes.extend( + f'{ds_id}:{prefix}' + for prefix in dataset.get('test_prefixes', []) + ) + self.item_names = sorted(list(self.items.keys())) + self._train_item_names, self._valid_item_names = self.split_train_valid_set(test_prefixes) + + if self.binarization_args['shuffle']: + random.shuffle(self.item_names) + + self.binary_data_dir.mkdir(parents=True, exist_ok=True) + + # Copy spk_map, lang_map and dictionary to binary data dir + spk_map_fn = self.binary_data_dir / 'spk_map.json' + with open(spk_map_fn, 'w', encoding='utf-8') as f: + json.dump(self.spk_map, f, ensure_ascii=False) + lang_map_fn = self.binary_data_dir / 'lang_map.json' + with open(lang_map_fn, 'w', encoding='utf-8') as f: + json.dump(self.lang_map, f, ensure_ascii=False) + for lang, dict_path in hparams['dictionaries'].items(): + shutil.copy(dict_path, self.binary_data_dir / f'dictionary-{lang}.txt') + self.check_coverage() + + # Process valid set and train set + try: + self.process_dataset('valid') + self.process_dataset( + 'train', + num_workers=int(self.binarization_args['num_workers']), + apply_augmentation=any(args['enabled'] for args in self.augmentation_args.values()) + ) + except KeyboardInterrupt: + exit(-1) + + def check_coverage(self): + # Group by phonemes in the dictionary. + ph_idx_required = set(range(1, len(self.phoneme_dictionary))) + ph_idx_occurred = set() + ph_idx_count_map = { + idx: 0 + for idx in ph_idx_required + } + + # Load and count those phones that appear in the actual data + for item_name in self.items: + ph_idx_occurred.update(self.items[item_name]['ph_seq']) + for idx in self.items[item_name]['ph_seq']: + ph_idx_count_map[idx] += 1 + ph_count_map = { + self.phoneme_dictionary.decode_one(idx, scalar=False): count + for idx, count in ph_idx_count_map.items() + } + + def display_phoneme(phoneme): + if isinstance(phoneme, tuple): + return f'({", ".join(phoneme)})' + return phoneme + + print('===== Phoneme Distribution Summary =====') + keys = sorted(ph_count_map.keys(), key=lambda v: v[0] if isinstance(v, tuple) else v) + for i, key in enumerate(keys): + if i == len(ph_count_map) - 1: + end = '\n' + elif i % 10 == 9: + end = ',\n' + else: + end = ', ' + key_disp = display_phoneme(key) + print(f'{key_disp}: {ph_count_map[key]}', end=end) + + # Draw graph. + xs = [display_phoneme(k) for k in keys] + ys = [ph_count_map[k] for k in keys] + plt = distribution_to_figure( + title='Phoneme Distribution Summary', + x_label='Phoneme', y_label='Number of occurrences', + items=xs, values=ys, rotate=len(self.dictionaries) > 1 + ) + filename = self.binary_data_dir / 'phoneme_distribution.jpg' + plt.savefig(fname=filename, + bbox_inches='tight', + pad_inches=0.25) + print(f'| save summary to \'{filename}\'') + + # Check unrecognizable or missing phonemes + if ph_idx_occurred != ph_idx_required: + missing_phones = sorted({ + self.phoneme_dictionary.decode_one(idx, scalar=False) + for idx in ph_idx_required.difference(ph_idx_occurred) + }, key=lambda v: v[0] if isinstance(v, tuple) else v) + raise BinarizationError( + f'The following phonemes are not covered in transcriptions: {missing_phones}' + ) + + def process_dataset(self, prefix, num_workers=0, apply_augmentation=False): + args = [] + builder = IndexedDatasetBuilder(self.binary_data_dir, prefix=prefix, allowed_attr=self.data_attrs) + total_sec = {k: 0.0 for k in self.spk_map} + total_raw_sec = {k: 0.0 for k in self.spk_map} + extra_info = {'names': {}, 'ph_texts': {}, 'spk_ids': {}, 'spk_names': {}, 'lengths': {}} + max_no = -1 + + for item_name, meta_data in self.meta_data_iterator(prefix): + args.append([item_name, meta_data, self.binarization_args]) + + aug_map = self.arrange_data_augmentation(self.meta_data_iterator(prefix)) if apply_augmentation else {} + + def postprocess(_item): + nonlocal total_sec, total_raw_sec, extra_info, max_no + if _item is None: + return + item_no = builder.add_item(_item) + max_no = max(max_no, item_no) + for k, v in _item.items(): + if isinstance(v, np.ndarray): + if k not in extra_info: + extra_info[k] = {} + extra_info[k][item_no] = v.shape[0] + extra_info['names'][item_no] = _item['name'].split(':', 1)[-1] + extra_info['ph_texts'][item_no] = _item['ph_text'] + extra_info['spk_ids'][item_no] = _item['spk_id'] + extra_info['spk_names'][item_no] = _item['spk_name'] + extra_info['lengths'][item_no] = _item['length'] + total_raw_sec[_item['spk_name']] += _item['seconds'] + total_sec[_item['spk_name']] += _item['seconds'] + + for task in aug_map.get(_item['name'], []): + aug_item = task['func'](_item, **task['kwargs']) + aug_item_no = builder.add_item(aug_item) + max_no = max(max_no, aug_item_no) + for k, v in aug_item.items(): + if isinstance(v, np.ndarray): + if k not in extra_info: + extra_info[k] = {} + extra_info[k][aug_item_no] = v.shape[0] + extra_info['names'][aug_item_no] = aug_item['name'].split(':', 1)[-1] + extra_info['ph_texts'][aug_item_no] = aug_item['ph_text'] + extra_info['spk_ids'][aug_item_no] = aug_item['spk_id'] + extra_info['spk_names'][aug_item_no] = aug_item['spk_name'] + extra_info['lengths'][aug_item_no] = aug_item['length'] + total_sec[aug_item['spk_name']] += aug_item['seconds'] + + try: + if num_workers > 0: + # code for parallel processing + for item in tqdm( + chunked_multiprocess_run(self.process_item, args, num_workers=num_workers), + total=len(list(self.meta_data_iterator(prefix))) + ): + postprocess(item) + else: + # code for single cpu processing + for a in tqdm(args): + item = self.process_item(*a) + postprocess(item) + for k in extra_info: + assert set(extra_info[k]) == set(range(max_no + 1)), f'Item numbering is not consecutive.' + extra_info[k] = list(map(lambda x: x[1], sorted(extra_info[k].items(), key=lambda x: x[0]))) + except KeyboardInterrupt: + builder.finalize() + raise + + builder.finalize() + if prefix == "train": + extra_info.pop("names") + extra_info.pop('ph_texts') + extra_info.pop("spk_names") + with open(self.binary_data_dir / f"{prefix}.meta", "wb") as f: + # noinspection PyTypeChecker + pickle.dump(extra_info, f) + if apply_augmentation: + print(f"| {prefix} total duration (before augmentation): {sum(total_raw_sec.values()):.2f}s") + print( + f"| {prefix} respective duration (before augmentation): " + + ', '.join(f'{k}={v:.2f}s' for k, v in total_raw_sec.items()) + ) + print( + f"| {prefix} total duration (after augmentation): " + f"{sum(total_sec.values()):.2f}s ({sum(total_sec.values()) / sum(total_raw_sec.values()):.2f}x)" + ) + print( + f"| {prefix} respective duration (after augmentation): " + + ', '.join(f'{k}={v:.2f}s' for k, v in total_sec.items()) + ) + else: + print(f"| {prefix} total duration: {sum(total_raw_sec.values()):.2f}s") + print(f"| {prefix} respective duration: " + ', '.join(f'{k}={v:.2f}s' for k, v in total_raw_sec.items())) + + def arrange_data_augmentation(self, data_iterator): + """ + Code for all types of data augmentation should be added here. + """ + raise NotImplementedError() + + def process_item(self, item_name, meta_data, binarization_args): + raise NotImplementedError() diff --git a/basics/base_dataset.py b/basics/base_dataset.py new file mode 100644 index 0000000..72c64d7 --- /dev/null +++ b/basics/base_dataset.py @@ -0,0 +1,58 @@ +import os +import pickle + +import torch +from torch.utils.data import Dataset + +from utils.hparams import hparams +from utils.indexed_datasets import IndexedDataset + + +class BaseDataset(Dataset): + """ + Base class for datasets. + 1. *sizes*: + clipped length if "max_frames" is set; + 2. *num_frames*: + unclipped length. + + Subclasses should define: + 1. *collate*: + take the longest data, pad other data to the same length; + 2. *__getitem__*: + the index function. + """ + + def __init__(self, prefix, size_key='lengths', preload=False): + super().__init__() + self.prefix = prefix + self.data_dir = hparams['binary_data_dir'] + with open(os.path.join(self.data_dir, f'{self.prefix}.meta'), 'rb') as f: + self.metadata = pickle.load(f) + self.sizes = self.metadata[size_key] + self._indexed_ds = IndexedDataset(self.data_dir, self.prefix) + if preload: + self.indexed_ds = [self._indexed_ds[i] for i in range(len(self._indexed_ds))] + del self._indexed_ds + else: + self.indexed_ds = self._indexed_ds + + def __getitem__(self, index): + return {'_idx': index, **self.indexed_ds[index]} + + def __len__(self): + return len(self.sizes) + + def num_frames(self, index): + return self.sizes[index] + + def size(self, index): + """Return an example's size as a float or tuple. This value is used when + filtering a dataset with ``--max-positions``.""" + return self.sizes[index] + + def collater(self, samples): + return { + 'size': len(samples), + 'indices': torch.LongTensor([s['_idx'] for s in samples]) + } diff --git a/basics/base_exporter.py b/basics/base_exporter.py new file mode 100644 index 0000000..77e5805 --- /dev/null +++ b/basics/base_exporter.py @@ -0,0 +1,86 @@ +import json +import pathlib +import shutil +from pathlib import Path +from typing import Union + +import torch +import torch.nn as nn + +from utils.hparams import hparams + + +class BaseExporter: + def __init__( + self, + device: Union[str, torch.device] = None, + cache_dir: Path = None, + **kwargs + ): + self.device = device if device is not None else torch.device('cuda' if torch.cuda.is_available() else 'cpu') + self.cache_dir: Path = cache_dir.resolve() if cache_dir is not None \ + else Path(__file__).parent.parent / 'deployment' / 'cache' + self.cache_dir.mkdir(parents=True, exist_ok=True) + + # noinspection PyMethodMayBeStatic + def build_spk_map(self) -> dict: + if hparams['use_spk_id']: + with open(Path(hparams['work_dir']) / 'spk_map.json', 'r', encoding='utf8') as f: + spk_map = json.load(f) + assert isinstance(spk_map, dict) and len(spk_map) > 0, 'Invalid or empty speaker map!' + assert len(spk_map) == len(set(spk_map.values())), 'Duplicate speaker id in speaker map!' + return spk_map + else: + return {} + + # noinspection PyMethodMayBeStatic + def build_lang_map(self) -> dict: + lang_map_fn = pathlib.Path(hparams['work_dir']) / 'lang_map.json' + if lang_map_fn.exists(): + with open(lang_map_fn, 'r', encoding='utf8') as f: + lang_map = json.load(f) + assert isinstance(lang_map, dict) and len(lang_map) > 0, 'Invalid or empty language map!' + assert len(lang_map) == len(set(lang_map.values())), 'Duplicate language id in language map!' + return lang_map + else: + return {} + + def build_model(self) -> nn.Module: + """ + Creates an instance of nn.Module and load its state dict on the target device. + """ + raise NotImplementedError() + + def export_model(self, path: Path): + """ + Exports the model to ONNX format. + :param path: the target model path + """ + raise NotImplementedError() + + # noinspection PyMethodMayBeStatic + def export_dictionaries(self, path: Path): + dicts = hparams.get('dictionaries') + if dicts is not None: + for lang in dicts.keys(): + fn = f'dictionary-{lang}.txt' + shutil.copy(pathlib.Path(hparams['work_dir']) / fn, path) + print(f'| export dictionary => {path / fn}') + else: + fn = 'dictionary.txt' + shutil.copy(pathlib.Path(hparams['work_dir']) / fn, path) + print(f'| export dictionary => {path / fn}') + + def export_attachments(self, path: Path): + """ + Exports related files and configs (e.g. the dictionary) to the target directory. + :param path: the target directory + """ + raise NotImplementedError() + + def export(self, path: Path): + """ + Exports all the artifacts to the target directory. + :param path: the target directory + """ + raise NotImplementedError() diff --git a/basics/base_module.py b/basics/base_module.py new file mode 100644 index 0000000..6256191 --- /dev/null +++ b/basics/base_module.py @@ -0,0 +1,18 @@ +from torch import nn + + +class CategorizedModule(nn.Module): + @property + def category(self): + raise NotImplementedError() + + def check_category(self, category): + if category is None: + raise RuntimeError('Category is not specified in this checkpoint.\n' + 'If this is a checkpoint in the old format, please consider ' + 'migrating it to the new format via the following command:\n' + 'python scripts/migrate.py ckpt ') + elif category != self.category: + raise RuntimeError('Category mismatches!\n' + f'This checkpoint is of the category \'{category}\', ' + f'but a checkpoint of category \'{self.category}\' is required.') diff --git a/basics/base_pe.py b/basics/base_pe.py new file mode 100644 index 0000000..78179a0 --- /dev/null +++ b/basics/base_pe.py @@ -0,0 +1,7 @@ +class BasePE: + def get_pitch( + self, waveform, samplerate, length, + *, hop_size, f0_min=65, f0_max=1100, + speed=1, interp_uv=False + ): + raise NotImplementedError() diff --git a/basics/base_svs_infer.py b/basics/base_svs_infer.py new file mode 100644 index 0000000..2b23d01 --- /dev/null +++ b/basics/base_svs_infer.py @@ -0,0 +1,136 @@ +# coding=utf8 +import numpy as np +import torch +from torch import Tensor +from typing import Tuple, Dict + +from utils.hparams import hparams +from utils.infer_utils import resample_align_curve + + +class BaseSVSInfer: + """ + Base class for SVS inference models. + Subclasses should define: + 1. *build_model*: + how to build the model; + 2. *run_model*: + how to run the model (typically, generate a mel-spectrogram and + pass it to the pre-built vocoder); + 3. *preprocess_input*: + how to preprocess user input. + 4. *infer_once* + infer from raw inputs to the final outputs + """ + + def __init__(self, device=None): + if device is None: + device = 'cuda' if torch.cuda.is_available() else 'cpu' + self.device = device + self.timestep = hparams['hop_size'] / hparams['audio_sample_rate'] + self.spk_map = {} + self.lang_map = {} + self.model: torch.nn.Module = None + + def build_model(self, ckpt_steps=None) -> torch.nn.Module: + raise NotImplementedError() + + def load_speaker_mix(self, param_src: dict, summary_dst: dict, + mix_mode: str = 'frame', mix_length: int = None) -> Tuple[Tensor, Tensor]: + """ + + :param param_src: param dict + :param summary_dst: summary dict + :param mix_mode: 'token' or 'frame' + :param mix_length: total tokens or frames to mix + :return: spk_mix_id [B=1, 1, N], spk_mix_value [B=1, T, N] + """ + assert mix_mode == 'token' or mix_mode == 'frame' + param_key = 'spk_mix' if mix_mode == 'frame' else 'ph_spk_mix' + summary_solo_key = 'spk' if mix_mode == 'frame' else 'ph_spk' + spk_mix_map = param_src.get(param_key) # { spk_name: value } or { spk_name: "value value value ..." } + dynamic = False + if spk_mix_map is None: + assert len(self.spk_map) == 1, ( + "This is a multi-speaker model. " + "Please specify a speaker or speaker mix by --spk option." + ) + # Get the only speaker + for name in self.spk_map.keys(): + spk_mix_map = {name: 1.0} + break + else: + for name in spk_mix_map: + assert name in self.spk_map, f'Speaker \'{name}\' not found.' + if len(spk_mix_map) == 1: + summary_dst[summary_solo_key] = list(spk_mix_map.keys())[0] + elif any([isinstance(val, str) for val in spk_mix_map.values()]): + print_mix = '|'.join(spk_mix_map.keys()) + summary_dst[param_key] = f'dynamic({print_mix})' + dynamic = True + else: + print_mix = '|'.join([f'{n}:{"%.3f" % spk_mix_map[n]}' for n in spk_mix_map]) + summary_dst[param_key] = f'static({print_mix})' + spk_mix_id_list = [] + spk_mix_value_list = [] + if dynamic: + for name, values in spk_mix_map.items(): + spk_mix_id_list.append(self.spk_map[name]) + if isinstance(values, str): + # this speaker has a variable proportion + if mix_mode == 'token': + cur_spk_mix_value = values.split() + assert len(cur_spk_mix_value) == mix_length, \ + 'Speaker mix checks failed. In dynamic token-level mix, ' \ + 'number of proportion values must equal number of tokens.' + cur_spk_mix_value = torch.from_numpy( + np.array(cur_spk_mix_value, 'float32') + ).to(self.device)[None] # => [B=1, T] + else: + cur_spk_mix_value = torch.from_numpy(resample_align_curve( + np.array(values.split(), 'float32'), + original_timestep=float(param_src['spk_mix_timestep']), + target_timestep=self.timestep, + align_length=mix_length + )).to(self.device)[None] # => [B=1, T] + assert torch.all(cur_spk_mix_value >= 0.), \ + f'Speaker mix checks failed.\n' \ + f'Proportions of speaker \'{name}\' on some {mix_mode}s are negative.' + else: + # this speaker has a constant proportion + assert values >= 0., f'Speaker mix checks failed.\n' \ + f'Proportion of speaker \'{name}\' is negative.' + cur_spk_mix_value = torch.full( + (1, mix_length), fill_value=values, + dtype=torch.float32, device=self.device + ) + spk_mix_value_list.append(cur_spk_mix_value) + spk_mix_id = torch.LongTensor(spk_mix_id_list).to(self.device)[None, None] # => [B=1, 1, N] + spk_mix_value = torch.stack(spk_mix_value_list, dim=2) # [B=1, T] => [B=1, T, N] + spk_mix_value_sum = torch.sum(spk_mix_value, dim=2, keepdim=True) # => [B=1, T, 1] + assert torch.all(spk_mix_value_sum > 0.), \ + f'Speaker mix checks failed.\n' \ + f'Proportions of speaker mix on some frames sum to zero.' + spk_mix_value /= spk_mix_value_sum # normalize + else: + for name, value in spk_mix_map.items(): + spk_mix_id_list.append(self.spk_map[name]) + assert value >= 0., f'Speaker mix checks failed.\n' \ + f'Proportion of speaker \'{name}\' is negative.' + spk_mix_value_list.append(value) + spk_mix_id = torch.LongTensor(spk_mix_id_list).to(self.device)[None, None] # => [B=1, 1, N] + spk_mix_value = torch.FloatTensor(spk_mix_value_list).to(self.device)[None, None] # => [B=1, 1, N] + spk_mix_value_sum = spk_mix_value.sum() + assert spk_mix_value_sum > 0., f'Speaker mix checks failed.\n' \ + f'Proportions of speaker mix sum to zero.' + spk_mix_value /= spk_mix_value_sum # normalize + return spk_mix_id, spk_mix_value + + def preprocess_input(self, param: dict, idx=0) -> Dict[str, torch.Tensor]: + raise NotImplementedError() + + def forward_model(self, sample: Dict[str, torch.Tensor]): + raise NotImplementedError() + + def run_inference(self, params, **kwargs): + raise NotImplementedError() diff --git a/basics/base_task.py b/basics/base_task.py new file mode 100644 index 0000000..656893d --- /dev/null +++ b/basics/base_task.py @@ -0,0 +1,514 @@ +import logging +import os +import pathlib +import shutil +import sys +from typing import Dict + +import matplotlib + +import utils + +matplotlib.use('Agg') + +import torch.utils.data +from torchmetrics import Metric, MeanMetric +import lightning.pytorch as pl +from lightning.pytorch.utilities.rank_zero import rank_zero_debug, rank_zero_info, rank_zero_only + +from basics.base_module import CategorizedModule +from utils.hparams import hparams +from utils.training_utils import ( + DsModelCheckpoint, DsTQDMProgressBar, + DsBatchSampler, DsTensorBoardLogger, + get_latest_checkpoint_path, get_strategy +) +from utils.phoneme_utils import load_phoneme_dictionary + +torch.multiprocessing.set_sharing_strategy(os.getenv('TORCH_SHARE_STRATEGY', 'file_system')) + +log_format = '%(asctime)s %(message)s' +logging.basicConfig(stream=sys.stdout, level=logging.INFO, + format=log_format, datefmt='%m/%d %I:%M:%S %p') + + +class BaseTask(pl.LightningModule): + """ + Base class for training tasks. + 1. *load_ckpt*: + load checkpoint; + 2. *training_step*: + record and log the loss; + 3. *optimizer_step*: + run backwards step; + 4. *start*: + load training configs, backup code, log to tensorboard, start training; + 5. *configure_ddp* and *init_ddp_connection*: + start parallel training. + + Subclasses should define: + 1. *build_model*, *build_optimizer*, *build_scheduler*: + how to build the model, the optimizer and the training scheduler; + 2. *_training_step*: + one training step of the model; + 3. *on_validation_end* and *_on_validation_end*: + postprocess the validation output. + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.max_batch_frames = hparams['max_batch_frames'] + self.max_batch_size = hparams['max_batch_size'] + self.max_val_batch_frames = hparams['max_val_batch_frames'] + if self.max_val_batch_frames == -1: + hparams['max_val_batch_frames'] = self.max_val_batch_frames = self.max_batch_frames + self.max_val_batch_size = hparams['max_val_batch_size'] + if self.max_val_batch_size == -1: + hparams['max_val_batch_size'] = self.max_val_batch_size = self.max_batch_size + + self.training_sampler = None + self.skip_immediate_validation = False + self.skip_immediate_ckpt_save = False + + self.phoneme_dictionary = load_phoneme_dictionary() + self.build_model() + + self.valid_losses: Dict[str, Metric] = {} + self.valid_metrics: Dict[str, Metric] = {} + + def _finish_init(self): + self.register_validation_loss('total_loss') + self.build_losses_and_metrics() + assert len(self.valid_losses) > 0, "No validation loss registered. Please check your configuration file." + + ########### + # Training, validation and testing + ########### + def setup(self, stage): + self.train_dataset = self.dataset_cls('train') + self.valid_dataset = self.dataset_cls('valid') + self.num_replicas = (self.trainer.distributed_sampler_kwargs or {}).get('num_replicas', 1) + + def get_need_freeze_state_dict_key(self, model_state_dict) -> list: + key_list = [] + for i in hparams['frozen_params']: + for j in model_state_dict: + if j.startswith(i): + key_list.append(j) + return list(set(key_list)) + + def freeze_params(self) -> None: + model_state_dict = self.state_dict().keys() + freeze_key = self.get_need_freeze_state_dict_key(model_state_dict=model_state_dict) + + for i in freeze_key: + params=self.get_parameter(i) + + params.requires_grad = False + + def unfreeze_all_params(self) -> None: + for i in self.model.parameters(): + i.requires_grad = True + + def load_finetune_ckpt( + self, state_dict + ) -> None: + adapt_shapes = hparams['finetune_strict_shapes'] + if not adapt_shapes: + cur_model_state_dict = self.state_dict() + unmatched_keys = [] + for key, param in state_dict.items(): + if key in cur_model_state_dict: + new_param = cur_model_state_dict[key] + if new_param.shape != param.shape: + unmatched_keys.append(key) + print('| Unmatched keys: ', key, new_param.shape, param.shape) + for key in unmatched_keys: + del state_dict[key] + self.load_state_dict(state_dict, strict=False) + + def load_pre_train_model(self): + pre_train_ckpt_path = hparams['finetune_ckpt_path'] + blacklist = hparams['finetune_ignored_params'] + # whitelist=hparams['pre_train_whitelist'] + if blacklist is None: + blacklist = [] + # if whitelist is None: + # raise RuntimeError("") + + if pre_train_ckpt_path is not None: + ckpt = torch.load(pre_train_ckpt_path) + # if ckpt.get('category') is None: + # raise RuntimeError("") + + if isinstance(self.model, CategorizedModule): + self.model.check_category(ckpt.get('category')) + + state_dict = {} + for i in ckpt['state_dict']: + # if 'diffusion' in i: + # if i in rrrr: + # continue + skip = False + for b in blacklist: + if i.startswith(b): + skip = True + break + + if skip: + continue + + state_dict[i] = ckpt['state_dict'][i] + print(i) + return state_dict + else: + raise RuntimeError("") + + def _build_model(self): + raise NotImplementedError() + + def build_model(self): + self.model = self._build_model() + # utils.load_warp(self) + self.unfreeze_all_params() + if hparams['freezing_enabled']: + self.freeze_params() + if hparams['finetune_enabled'] and get_latest_checkpoint_path(pathlib.Path(hparams['work_dir'])) is None: + self.load_finetune_ckpt(self.load_pre_train_model()) + self.print_arch() + + @rank_zero_only + def print_arch(self): + utils.print_arch(self.model) + + def build_losses_and_metrics(self): + raise NotImplementedError() + + def register_validation_metric(self, name: str, metric: Metric): + assert isinstance(metric, Metric) + self.valid_metrics[name] = metric + + def register_validation_loss(self, name: str, Aggregator: Metric = MeanMetric): + assert issubclass(Aggregator, Metric) + self.valid_losses[name] = Aggregator() + + def run_model(self, sample, infer=False): + """ + steps: + 1. run the full model + 2. calculate losses if not infer + """ + raise NotImplementedError() + + def on_train_epoch_start(self): + if self.training_sampler is not None: + self.training_sampler.set_epoch(self.current_epoch) + + def _training_step(self, sample): + """ + :return: total loss: torch.Tensor, loss_log: dict, other_log: dict + """ + losses = self.run_model(sample) + total_loss = sum(losses.values()) + return total_loss, {**losses, 'batch_size': float(sample['size'])} + + def training_step(self, sample, batch_idx): + total_loss, log_outputs = self._training_step(sample) + + # logs to progress bar + self.log_dict(log_outputs, prog_bar=True, logger=False, on_step=True, on_epoch=False) + self.log('lr', self.lr_schedulers().get_last_lr()[0], prog_bar=True, logger=False, on_step=True, on_epoch=False) + # logs to tensorboard + if self.global_step % hparams['log_interval'] == 0: + tb_log = {f'training/{k}': v for k, v in log_outputs.items()} + tb_log['training/lr'] = self.lr_schedulers().get_last_lr()[0] + self.logger.log_metrics(tb_log, step=self.global_step) + + return total_loss + + # def on_before_optimizer_step(self, *args, **kwargs): + # self.log_dict(grad_norm(self, norm_type=2)) + + def _on_validation_start(self): + pass + + def on_validation_start(self): + if self.skip_immediate_validation: + rank_zero_debug("Skip validation") + return + self._on_validation_start() + for metric in self.valid_losses.values(): + metric.to(self.device) + metric.reset() + for metric in self.valid_metrics.values(): + metric.to(self.device) + metric.reset() + + def _validation_step(self, sample, batch_idx): + """ + + :param sample: + :param batch_idx: + :return: loss_log: dict, weight: int + """ + raise NotImplementedError() + + def validation_step(self, sample, batch_idx): + """ + + :param sample: + :param batch_idx: + """ + if self.skip_immediate_validation: + rank_zero_debug("Skip validation") + return + if sample['size'] > 0: + with torch.autocast(self.device.type, enabled=False): + losses, weight = self._validation_step(sample, batch_idx) + losses = { + 'total_loss': sum(losses.values()), + **losses + } + for k, v in losses.items(): + self.valid_losses[k].update(v, weight=weight) + + def _on_validation_epoch_end(self): + pass + + def on_validation_epoch_end(self): + if self.skip_immediate_validation: + self.skip_immediate_validation = False + self.skip_immediate_ckpt_save = True + return + self._on_validation_epoch_end() + loss_vals = {k: v.compute() for k, v in self.valid_losses.items()} + metric_vals = {k: v.compute() for k, v in self.valid_metrics.items()} + self.log('val_loss', loss_vals['total_loss'], on_epoch=True, prog_bar=True, logger=False, sync_dist=True) + self.logger.log_metrics({f'validation/{k}': v for k, v in loss_vals.items()}, step=self.global_step) + self.logger.log_metrics({f'metrics/{k}': v for k, v in metric_vals.items()}, step=self.global_step) + + # noinspection PyMethodMayBeStatic + def build_scheduler(self, optimizer): + from utils import build_lr_scheduler_from_config + + scheduler_args = hparams['lr_scheduler_args'] + assert scheduler_args['scheduler_cls'] != '' + scheduler = build_lr_scheduler_from_config(optimizer, scheduler_args) + return scheduler + + # noinspection PyMethodMayBeStatic + def build_optimizer(self, model): + from utils import build_object_from_class_name + + optimizer_args = hparams['optimizer_args'] + assert optimizer_args['optimizer_cls'] != '' + if 'beta1' in optimizer_args and 'beta2' in optimizer_args and 'betas' not in optimizer_args: + optimizer_args['betas'] = (optimizer_args['beta1'], optimizer_args['beta2']) + optimizer = build_object_from_class_name( + optimizer_args['optimizer_cls'], + torch.optim.Optimizer, + model if optimizer_args['optimizer_cls'] == 'modules.optimizer.muon.Muon_AdamW' else model.parameters(), + **optimizer_args + ) + return optimizer + + def configure_optimizers(self): + optm = self.build_optimizer(self.model) + scheduler = self.build_scheduler(optm) + if scheduler is None: + return optm + return { + "optimizer": optm, + "lr_scheduler": { + "scheduler": scheduler, + "interval": "step", + "frequency": 1 + } + } + + def train_dataloader(self): + self.training_sampler = DsBatchSampler( + self.train_dataset, + max_batch_frames=self.max_batch_frames, + max_batch_size=self.max_batch_size, + num_replicas=self.num_replicas, + rank=self.global_rank, + sort_by_similar_size=hparams['sort_by_len'], + size_reversed=True, + required_batch_count_multiple=hparams['accumulate_grad_batches'], + shuffle_sample=True, + shuffle_batch=True + ) + return torch.utils.data.DataLoader( + self.train_dataset, + collate_fn=self.train_dataset.collater, + batch_sampler=self.training_sampler, + num_workers=hparams['ds_workers'], + prefetch_factor=hparams['dataloader_prefetch_factor'], + pin_memory=True, + persistent_workers=True + ) + + def val_dataloader(self): + sampler = DsBatchSampler( + self.valid_dataset, + max_batch_frames=self.max_val_batch_frames, + max_batch_size=self.max_val_batch_size, + num_replicas=self.num_replicas, + rank=self.global_rank, + shuffle_sample=False, + shuffle_batch=False, + disallow_empty_batch=False, + pad_batch_assignment=False + ) + return torch.utils.data.DataLoader( + self.valid_dataset, + collate_fn=self.valid_dataset.collater, + batch_sampler=sampler, + num_workers=hparams['ds_workers'], + prefetch_factor=hparams['dataloader_prefetch_factor'], + persistent_workers=True + ) + + def test_dataloader(self): + return self.val_dataloader() + + def on_test_start(self): + self.on_validation_start() + + def test_step(self, sample, batch_idx): + return self.validation_step(sample, batch_idx) + + def on_test_end(self): + return self.on_validation_end() + + ########### + # Running configuration + ########### + + @classmethod + def start(cls): + task = cls() + + # if pre_train is not None: + # task.load_state_dict(pre_train,strict=False) + # print("load success-------------------------------------------------------------------") + + work_dir = pathlib.Path(hparams['work_dir']) + trainer = pl.Trainer( + accelerator=hparams['pl_trainer_accelerator'], + devices=hparams['pl_trainer_devices'], + num_nodes=hparams['pl_trainer_num_nodes'], + strategy=get_strategy( + hparams['pl_trainer_devices'], + hparams['pl_trainer_num_nodes'], + hparams['pl_trainer_accelerator'], + hparams['pl_trainer_strategy'], + hparams['pl_trainer_precision'], + ), + precision=hparams['pl_trainer_precision'], + callbacks=[ + DsModelCheckpoint( + dirpath=work_dir, + filename='model_ckpt_steps_{step}', + auto_insert_metric_name=False, + monitor='step', + mode='max', + save_last=False, + # every_n_train_steps=hparams['val_check_interval'], + save_top_k=hparams['num_ckpt_keep'], + permanent_ckpt_start=hparams['permanent_ckpt_start'], + permanent_ckpt_interval=hparams['permanent_ckpt_interval'], + verbose=True + ), + # LearningRateMonitor(logging_interval='step'), + DsTQDMProgressBar(), + ], + logger=DsTensorBoardLogger( + save_dir=str(work_dir), + name='lightning_logs', + version='latest' + ), + gradient_clip_val=hparams['clip_grad_norm'], + val_check_interval=hparams['val_check_interval'] * hparams['accumulate_grad_batches'], + # so this is global_steps + check_val_every_n_epoch=None, + log_every_n_steps=1, + max_steps=hparams['max_updates'], + use_distributed_sampler=False, + num_sanity_val_steps=hparams['num_sanity_val_steps'], + accumulate_grad_batches=hparams['accumulate_grad_batches'] + ) + if not hparams['infer']: # train + @rank_zero_only + def train_payload_copy(): + # Copy files to work_dir + binary_dir = pathlib.Path(hparams['binary_data_dir']) + spk_map_dst = work_dir / 'spk_map.json' + spk_map_src = binary_dir / 'spk_map.json' + shutil.copy(spk_map_src, spk_map_dst) + print(f'| Copied spk map to {spk_map_dst}.') + lang_map_dst = work_dir / 'lang_map.json' + lang_map_src = binary_dir / 'lang_map.json' + shutil.copy(lang_map_src, lang_map_dst) + print(f'| Copied lang map to {lang_map_dst}.') + for lang in hparams['dictionaries'].keys(): + dict_dst = work_dir / f'dictionary-{lang}.txt' + dict_src = binary_dir / f'dictionary-{lang}.txt' + shutil.copy(dict_src, dict_dst) + print(f'| Copied dictionary for language \'{lang}\' to {dict_dst}.') + + train_payload_copy() + trainer.fit(task, ckpt_path=get_latest_checkpoint_path(work_dir)) + else: + trainer.test(task) + + def on_save_checkpoint(self, checkpoint): + if isinstance(self.model, CategorizedModule): + checkpoint['category'] = self.model.category + checkpoint['trainer_stage'] = self.trainer.state.stage.value + + def on_load_checkpoint(self, checkpoint): + from lightning.pytorch.trainer.states import RunningStage + from utils import simulate_lr_scheduler + if checkpoint.get('trainer_stage', '') == RunningStage.VALIDATING.value: + self.skip_immediate_validation = True + + optimizer_args = hparams['optimizer_args'] + scheduler_args = hparams['lr_scheduler_args'] + + if 'beta1' in optimizer_args and 'beta2' in optimizer_args and 'betas' not in optimizer_args: + optimizer_args['betas'] = (optimizer_args['beta1'], optimizer_args['beta2']) + + if checkpoint.get('optimizer_states', None): + opt_states = checkpoint['optimizer_states'] + assert len(opt_states) == 1 # only support one optimizer + opt_state = opt_states[0] + for param_group in opt_state['param_groups']: + for k, v in optimizer_args.items(): + if k in param_group and param_group[k] != v: + if 'lr_schedulers' in checkpoint and checkpoint['lr_schedulers'] and k == 'lr': + continue + rank_zero_info(f'| Overriding optimizer parameter {k} from checkpoint: {param_group[k]} -> {v}') + param_group[k] = v + if 'initial_lr' in param_group and param_group['initial_lr'] != optimizer_args['lr']: + rank_zero_info( + f'| Overriding optimizer parameter initial_lr from checkpoint: {param_group["initial_lr"]} -> {optimizer_args["lr"]}' + ) + param_group['initial_lr'] = optimizer_args['lr'] + + if checkpoint.get('lr_schedulers', None): + assert checkpoint.get('optimizer_states', False) + assert len(checkpoint['lr_schedulers']) == 1 # only support one scheduler + checkpoint['lr_schedulers'][0] = simulate_lr_scheduler( + optimizer_args, scheduler_args, + step_count=checkpoint['global_step'], + num_param_groups=len(checkpoint['optimizer_states'][0]['param_groups']) + ) + for param_group, new_lr in zip( + checkpoint['optimizer_states'][0]['param_groups'], + checkpoint['lr_schedulers'][0]['_last_lr'], + ): + if param_group['lr'] != new_lr: + rank_zero_info(f'| Overriding optimizer parameter lr from checkpoint: {param_group["lr"]} -> {new_lr}') + param_group['lr'] = new_lr diff --git a/basics/base_vocoder.py b/basics/base_vocoder.py new file mode 100644 index 0000000..7100e0b --- /dev/null +++ b/basics/base_vocoder.py @@ -0,0 +1,23 @@ +class BaseVocoder: + def to_device(self, device): + """ + + :param device: torch.device or str + """ + raise NotImplementedError() + + def get_device(self): + """ + + :return: device: torch.device or str + """ + raise NotImplementedError() + + def spec2wav(self, mel, **kwargs): + """ + + :param mel: [T, 80] + :return: wav: [T'] + """ + + raise NotImplementedError() diff --git a/checkpoints/.gitkeep b/checkpoints/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/configs/acoustic.yaml b/configs/acoustic.yaml new file mode 100644 index 0000000..fad7560 --- /dev/null +++ b/configs/acoustic.yaml @@ -0,0 +1,144 @@ +base_config: + - configs/base.yaml + +task_cls: training.acoustic_task.AcousticTask + +dictionaries: {} +extra_phonemes: [] +merged_phoneme_groups: [] +datasets: [] + +vocoder: NsfHifiGAN +vocoder_ckpt: checkpoints/pc_nsf_hifigan_44.1k_hop512_128bin_2025.02/model.ckpt +audio_sample_rate: 44100 +audio_num_mel_bins: 128 +hop_size: 512 # Hop size. +fft_size: 2048 # FFT size. +win_size: 2048 # FFT size. +fmin: 40 +fmax: 16000 + +binarization_args: + shuffle: true + num_workers: 0 +augmentation_args: + random_pitch_shifting: + enabled: false + range: [-5., 5.] + scale: 0.75 + fixed_pitch_shifting: + enabled: false + targets: [-5., 5.] + scale: 0.5 + random_time_stretching: + enabled: false + range: [0.5, 2.] + scale: 0.75 + +binary_data_dir: 'data/opencpop/binary' +binarizer_cls: preprocessing.acoustic_binarizer.AcousticBinarizer +spec_min: [-12] +spec_max: [0] +mel_vmin: -14. +mel_vmax: 4. +mel_base: 'e' +energy_smooth_width: 0.06 +breathiness_smooth_width: 0.06 +voicing_smooth_width: 0.06 +tension_smooth_width: 0.06 + +use_lang_id: false +num_lang: 1 +use_spk_id: false +num_spk: 1 +use_mix_ln: false +mix_ln_layer: [0, 2] +use_energy_embed: false +use_breathiness_embed: false +use_voicing_embed: false +use_tension_embed: false +use_key_shift_embed: false +use_speed_embed: false + +diffusion_type: reflow +time_scale_factor: 1000 +timesteps: 1000 +max_beta: 0.02 +enc_ffn_kernel_size: 3 +use_rope: true +rope_interleaved: false +use_stretch_embed: true +use_variance_scaling: true +rel_pos: true +sampling_algorithm: euler +sampling_steps: 20 +diff_accelerator: ddim +diff_speedup: 10 +hidden_size: 384 +backbone_type: 'lynxnet2' +backbone_args: + num_channels: 1024 + num_layers: 6 + kernel_size: 31 + dropout_rate: 0.0 + use_conditioner_cache: true + glu_type: 'atanglu' +main_loss_type: l2 +main_loss_log_norm: false +schedule_type: 'linear' + +# shallow diffusion +use_shallow_diffusion: true +T_start: 0.4 +T_start_infer: 0.4 +K_step: 400 +K_step_infer: 400 + +shallow_diffusion_args: + train_aux_decoder: true + train_diffusion: true + val_gt_start: false + aux_decoder_arch: convnext + aux_decoder_args: + num_channels: 512 + num_layers: 6 + kernel_size: 7 + dropout_rate: 0.1 + aux_decoder_grad: 0.1 + +lambda_aux_mel_loss: 0.2 + +# train and eval +num_sanity_val_steps: 1 +optimizer_args: + optimizer_cls: modules.optimizer.muon.Muon_AdamW + lr: 0.0006 + muon_args: + weight_decay: 0.1 + adamw_args: + weight_decay: 0.0 +lr_scheduler_args: + step_size: 5000 + gamma: 0.8 +max_batch_frames: 50000 +max_batch_size: 64 +dataset_size_key: 'lengths' +val_with_vocoder: true +val_check_interval: 4000 +num_valid_plots: 10 +max_updates: 100000 +num_ckpt_keep: 8 +permanent_ckpt_start: 60000 +permanent_ckpt_interval: 10000 + +finetune_enabled: false +finetune_ckpt_path: null + +finetune_ignored_params: + - model.fs2.encoder.embed_tokens + - model.fs2.txt_embed + - model.fs2.spk_embed +finetune_strict_shapes: true + +freezing_enabled: false +frozen_params: [] diff --git a/configs/base.yaml b/configs/base.yaml new file mode 100644 index 0000000..72def32 --- /dev/null +++ b/configs/base.yaml @@ -0,0 +1,94 @@ +# task +task_cls: null + +############# +# dataset +############# +sort_by_len: true +datasets: [] +binary_data_dir: null +binarizer_cls: null +binarization_args: + shuffle: false + num_workers: 0 + +audio_sample_rate: 44100 +hop_size: 512 +win_size: 2048 +fft_size: 2048 # Extra window size is filled with 0 paddings to match this parameter +sampler_frame_count_grid: 6 +ds_workers: 4 +dataloader_prefetch_factor: 2 + +######### +# model +######### +hidden_size: 384 +dropout: 0.1 +use_pos_embed: true +enc_layers: 4 +num_heads: 2 +enc_ffn_kernel_size: 3 +ffn_act: gelu +use_spk_id: false + +########### +# optimization +########### +optimizer_args: + optimizer_cls: modules.optimizer.muon.Muon_AdamW + lr: 0.0006 + beta1: 0.9 + beta2: 0.98 + weight_decay: 0 +lr_scheduler_args: + scheduler_cls: torch.optim.lr_scheduler.StepLR + step_size: 5000 + gamma: 0.8 +clip_grad_norm: 1 + +########### +# train and eval +########### +num_ckpt_keep: 5 +accumulate_grad_batches: 1 +log_interval: 100 +num_sanity_val_steps: 1 # steps of validation at the beginning +val_check_interval: 4000 +max_updates: 100000 +max_batch_frames: 50000 +max_batch_size: 100000 +max_val_batch_frames: 60000 +max_val_batch_size: 1 +pe: parselmouth +pe_ckpt: 'checkpoints/rmvpe/model.pt' +hnsep: vr +hnsep_ckpt: 'checkpoints/vr/model.pt' +f0_min: 65 +f0_max: 1100 +num_valid_plots: 10 + +########### +# pytorch lightning +# Read https://lightning.ai/docs/pytorch/stable/common/trainer.html#trainer-class-api for possible values +########### +pl_trainer_accelerator: 'auto' +pl_trainer_devices: 'auto' +pl_trainer_precision: '16-mixed' +pl_trainer_num_nodes: 1 +pl_trainer_strategy: + name: auto + process_group_backend: nccl + find_unused_parameters: false +nccl_p2p: true + +########### +# finetune +########### +finetune_enabled: false +finetune_ckpt_path: null +finetune_ignored_params: [] +finetune_strict_shapes: true + +freezing_enabled: false +frozen_params: [] diff --git a/configs/templates/config_acoustic.yaml b/configs/templates/config_acoustic.yaml new file mode 100644 index 0000000..e344fb4 --- /dev/null +++ b/configs/templates/config_acoustic.yaml @@ -0,0 +1,128 @@ +base_config: + - configs/acoustic.yaml + +dictionaries: + zh: dictionaries/opencpop-extension.txt +extra_phonemes: [] +merged_phoneme_groups: [] + +datasets: + - raw_data_dir: data/xxx1/raw + speaker: speaker1 + spk_id: 0 + language: zh + test_prefixes: + - wav1 + - wav2 + - wav3 + - wav4 + - wav5 + - raw_data_dir: data/xxx2/raw + speaker: speaker2 + spk_id: 1 + language: zh + test_prefixes: + - wav1 + - wav2 + - wav3 + - wav4 + - wav5 + +binary_data_dir: data/xxx/binary +binarization_args: + num_workers: 0 +pe: parselmouth +pe_ckpt: 'checkpoints/rmvpe/model.pt' +hnsep: vr +hnsep_ckpt: 'checkpoints/vr/model.pt' +vocoder: NsfHifiGAN +vocoder_ckpt: checkpoints/nsf_hifigan_44.1k_hop512_128bin_2024.02/model.ckpt + +use_lang_id: false +num_lang: 1 +use_spk_id: false +num_spk: 1 + +use_mix_ln: false +mix_ln_layer: [0, 2] + +# NOTICE: before enabling variance embeddings, please read the docs at +# https://github.com/openvpi/DiffSinger/tree/main/docs/BestPractices.md#choosing-variance-parameters +use_energy_embed: false +use_breathiness_embed: false +use_voicing_embed: false +use_tension_embed: false + +use_key_shift_embed: true +use_speed_embed: true + +augmentation_args: + random_pitch_shifting: + enabled: true + range: [-5., 5.] + scale: 0.75 + fixed_pitch_shifting: + enabled: false + targets: [-5., 5.] + scale: 0.5 + random_time_stretching: + enabled: true + range: [0.5, 2.] + scale: 0.75 + +# diffusion and shallow diffusion +diffusion_type: reflow +enc_ffn_kernel_size: 3 +use_rope: true +rope_interleaved: false +use_stretch_embed: true +use_variance_scaling: true +use_shallow_diffusion: true +T_start: 0.4 +T_start_infer: 0.4 +K_step: 300 +K_step_infer: 300 +hidden_size: 384 +backbone_type: 'lynxnet2' +backbone_args: + num_channels: 1024 + num_layers: 6 + kernel_size: 31 + dropout_rate: 0.0 + use_conditioner_cache: true + glu_type: 'atanglu' +shallow_diffusion_args: + train_aux_decoder: true + train_diffusion: true + val_gt_start: false + aux_decoder_arch: convnext + aux_decoder_args: + num_channels: 512 + num_layers: 6 + kernel_size: 7 + dropout_rate: 0.1 + aux_decoder_grad: 0.1 +lambda_aux_mel_loss: 0.2 + +optimizer_args: + optimizer_cls: modules.optimizer.muon.Muon_AdamW + lr: 0.0006 + muon_args: + weight_decay: 0.1 + adamw_args: + weight_decay: 0.0 +lr_scheduler_args: + step_size: 5000 + gamma: 0.8 +max_batch_frames: 50000 +max_batch_size: 64 +max_updates: 100000 + +num_valid_plots: 10 +val_with_vocoder: true +val_check_interval: 4000 +num_ckpt_keep: 8 +permanent_ckpt_start: 60000 +permanent_ckpt_interval: 10000 +pl_trainer_devices: 'auto' +pl_trainer_precision: '16-mixed' diff --git a/configs/templates/config_duration.yaml b/configs/templates/config_duration.yaml new file mode 100644 index 0000000..1753364 --- /dev/null +++ b/configs/templates/config_duration.yaml @@ -0,0 +1,139 @@ +base_config: + - configs/variance.yaml + +dictionaries: + zh: dictionaries/opencpop-extension.txt +extra_phonemes: [] +merged_phoneme_groups: [] + +datasets: + - raw_data_dir: data/xxx1/raw + speaker: speaker1 + spk_id: 0 + language: zh + test_prefixes: + - wav1 + - wav2 + - wav3 + - wav4 + - wav5 + - raw_data_dir: data/xxx2/raw + speaker: speaker2 + spk_id: 1 + language: zh + test_prefixes: + - wav1 + - wav2 + - wav3 + - wav4 + - wav5 + +binary_data_dir: data/xxx/binary +binarization_args: + num_workers: 0 +pe: parselmouth +pe_ckpt: 'checkpoints/rmvpe/model.pt' +hnsep: vr +hnsep_ckpt: 'checkpoints/vr/model.pt' + +use_lang_id: false +num_lang: 1 +use_spk_id: false +num_spk: 1 +# NOTICE: before enabling variance modules, please read the docs at +# https://github.com/openvpi/DiffSinger/tree/main/docs/BestPractices.md#mutual-influence-between-variance-modules +predict_dur: true +predict_pitch: false +# NOTICE: before enabling variance predictions, please read the docs at +# https://github.com/openvpi/DiffSinger/tree/main/docs/BestPractices.md#choosing-variance-parameters +predict_energy: false +predict_breathiness: true +predict_voicing: true +predict_tension: false + +energy_db_min: -96.0 +energy_db_max: -12.0 + +breathiness_db_min: -96.0 +breathiness_db_max: -20.0 + +voicing_db_min: -96.0 +voicing_db_max: -12.0 + +tension_logit_min: -10.0 +tension_logit_max: 10.0 + +enc_ffn_kernel_size: 3 +use_rope: true +rope_interleaved: false +use_stretch_embed: false +use_variance_scaling: true +hidden_size: 384 +dur_prediction_args: + arch: resnet + hidden_size: 256 + dropout: 0.1 + num_layers: 5 + kernel_size: 3 + log_offset: 1.0 + loss_type: mse + lambda_pdur_loss: 0.3 + lambda_wdur_loss: 1.0 + lambda_sdur_loss: 3.0 + +use_melody_encoder: true +melody_encoder_args: + hidden_size: 128 + enc_layers: 4 +use_glide_embed: false +glide_types: [up, down] +glide_embed_scale: 11.313708498984760 # sqrt(128) + +diffusion_type: reflow + +pitch_prediction_args: + pitd_norm_min: -8.0 + pitd_norm_max: 8.0 + pitd_clip_min: -12.0 + pitd_clip_max: 12.0 + repeat_bins: 64 + backbone_type: 'lynxnet2' + backbone_args: + num_layers: 6 + num_channels: 512 + dropout_rate: 0.0 + use_conditioner_cache: true + glu_type: 'atanglu' + +variances_prediction_args: + total_repeat_bins: 72 + backbone_type: 'lynxnet2' + backbone_args: + num_layers: 6 + num_channels: 384 + dropout_rate: 0.0 + use_conditioner_cache: true + glu_type: 'atanglu' + +lambda_dur_loss: 1.0 +lambda_pitch_loss: 1.0 +lambda_var_loss: 1.0 + +optimizer_args: + optimizer_cls: torch.optim.AdamW + lr: 0.0006 +lr_scheduler_args: + scheduler_cls: torch.optim.lr_scheduler.StepLR + step_size: 5000 + gamma: 0.75 +max_batch_frames: 80000 +max_batch_size: 48 +max_updates: 60000 + +num_valid_plots: 10 +val_check_interval: 4000 +num_ckpt_keep: 8 +permanent_ckpt_start: 30000 +permanent_ckpt_interval: 10000 +pl_trainer_devices: 'auto' +pl_trainer_precision: '16-mixed' diff --git a/configs/templates/config_variance.yaml b/configs/templates/config_variance.yaml new file mode 100644 index 0000000..116154a --- /dev/null +++ b/configs/templates/config_variance.yaml @@ -0,0 +1,142 @@ +base_config: + - configs/variance.yaml + +dictionaries: + zh: dictionaries/opencpop-extension.txt +extra_phonemes: [] +merged_phoneme_groups: [] + +datasets: + - raw_data_dir: data/xxx1/raw + speaker: speaker1 + spk_id: 0 + language: zh + test_prefixes: + - wav1 + - wav2 + - wav3 + - wav4 + - wav5 + - raw_data_dir: data/xxx2/raw + speaker: speaker2 + spk_id: 1 + language: zh + test_prefixes: + - wav1 + - wav2 + - wav3 + - wav4 + - wav5 + +binary_data_dir: data/xxx/binary +binarization_args: + num_workers: 0 +pe: parselmouth +pe_ckpt: 'checkpoints/rmvpe/model.pt' +hnsep: vr +hnsep_ckpt: 'checkpoints/vr/model.pt' + +use_lang_id: false +num_lang: 1 +use_spk_id: false +num_spk: 1 +# NOTICE: before enabling variance modules, please read the docs at +# https://github.com/openvpi/DiffSinger/tree/main/docs/BestPractices.md#mutual-influence-between-variance-modules +predict_dur: false +predict_pitch: false +# NOTICE: before enabling variance predictions, please read the docs at +# https://github.com/openvpi/DiffSinger/tree/main/docs/BestPractices.md#choosing-variance-parameters +predict_energy: false +predict_breathiness: false +predict_voicing: false +predict_tension: false + +energy_db_min: -96.0 +energy_db_max: -12.0 + +breathiness_db_min: -96.0 +breathiness_db_max: -20.0 + +voicing_db_min: -96.0 +voicing_db_max: -12.0 + +tension_logit_min: -10.0 +tension_logit_max: 10.0 + +enc_ffn_kernel_size: 3 +use_rope: true +rope_interleaved: false +use_stretch_embed: false +use_variance_scaling: true +hidden_size: 384 +dur_prediction_args: + arch: resnet + hidden_size: 256 + dropout: 0.1 + num_layers: 5 + kernel_size: 3 + log_offset: 1.0 + loss_type: mse + lambda_pdur_loss: 0.3 + lambda_wdur_loss: 1.0 + lambda_sdur_loss: 3.0 + +use_melody_encoder: true +melody_encoder_args: + hidden_size: 128 + enc_layers: 4 +use_glide_embed: false +glide_types: [up, down] +glide_embed_scale: 11.313708498984760 # sqrt(128) + +diffusion_type: reflow + +pitch_prediction_args: + pitd_norm_min: -8.0 + pitd_norm_max: 8.0 + pitd_clip_min: -12.0 + pitd_clip_max: 12.0 + repeat_bins: 64 + backbone_type: 'lynxnet2' + backbone_args: + num_layers: 6 + num_channels: 512 + dropout_rate: 0.0 + use_conditioner_cache: true + glu_type: 'atanglu' + +variances_prediction_args: + total_repeat_bins: 72 + backbone_type: 'lynxnet2' + backbone_args: + num_layers: 6 + num_channels: 384 + dropout_rate: 0.0 + use_conditioner_cache: true + glu_type: 'atanglu' + +lambda_dur_loss: 1.0 +lambda_pitch_loss: 1.0 +lambda_var_loss: 1.0 + +optimizer_args: + optimizer_cls: modules.optimizer.muon.Muon_AdamW + lr: 0.0006 + muon_args: + weight_decay: 0.1 + adamw_args: + weight_decay: 0.0 +lr_scheduler_args: + step_size: 5000 + gamma: 0.8 +max_batch_frames: 80000 +max_batch_size: 48 +max_updates: 80000 + +num_valid_plots: 10 +val_check_interval: 2000 +num_ckpt_keep: 8 +permanent_ckpt_start: 30000 +permanent_ckpt_interval: 10000 +pl_trainer_devices: 'auto' +pl_trainer_precision: '16-mixed' diff --git a/configs/variance.yaml b/configs/variance.yaml new file mode 100644 index 0000000..d4e2036 --- /dev/null +++ b/configs/variance.yaml @@ -0,0 +1,152 @@ +base_config: + - configs/base.yaml + +task_cls: training.variance_task.VarianceTask + +dictionaries: {} +extra_phonemes: [] +merged_phoneme_groups: [] +datasets: [] + +audio_sample_rate: 44100 +hop_size: 512 # Hop size. +fft_size: 2048 # FFT size. +win_size: 2048 # FFT size. +midi_smooth_width: 0.06 # in seconds + +binarization_args: + shuffle: true + num_workers: 0 + prefer_ds: false + +binary_data_dir: 'data/opencpop_variance/binary' +binarizer_cls: preprocessing.variance_binarizer.VarianceBinarizer + +use_lang_id: false +num_lang: 1 +use_spk_id: false +num_spk: 1 + +predict_dur: true +predict_pitch: true +predict_energy: false +predict_breathiness: false +predict_voicing: false +predict_tension: false + +enc_ffn_kernel_size: 3 +use_rope: true +rope_interleaved: false +use_stretch_embed: false +use_variance_scaling: true +rel_pos: true +hidden_size: 384 + +dur_prediction_args: + arch: resnet + hidden_size: 256 + dropout: 0.1 + num_layers: 5 + kernel_size: 3 + log_offset: 1.0 + loss_type: mse + lambda_pdur_loss: 0.3 + lambda_wdur_loss: 1.0 + lambda_sdur_loss: 3.0 + +use_melody_encoder: true +melody_encoder_args: + hidden_size: 128 + enc_layers: 4 +use_glide_embed: false +glide_types: [up, down] +glide_embed_scale: 11.313708498984760 # sqrt(128) + +pitch_prediction_args: + pitd_norm_min: -8.0 + pitd_norm_max: 8.0 + pitd_clip_min: -12.0 + pitd_clip_max: 12.0 + repeat_bins: 64 + backbone_type: 'lynxnet2' + backbone_args: + num_layers: 6 + num_channels: 512 + dropout_rate: 0.0 + use_conditioner_cache: true + glu_type: 'atanglu' + +energy_db_min: -96.0 +energy_db_max: -12.0 +energy_smooth_width: 0.06 + +breathiness_db_min: -96.0 +breathiness_db_max: -20.0 +breathiness_smooth_width: 0.06 +voicing_db_min: -96.0 +voicing_db_max: -12.0 +voicing_smooth_width: 0.06 + +tension_logit_min: -10.0 +tension_logit_max: 10.0 +tension_smooth_width: 0.06 + +variances_prediction_args: + total_repeat_bins: 72 + backbone_type: 'lynxnet2' + backbone_args: + num_layers: 6 + num_channels: 384 + dropout_rate: 0.0 + use_conditioner_cache: true + glu_type: 'atanglu' + +lambda_dur_loss: 1.0 +lambda_pitch_loss: 1.0 +lambda_var_loss: 1.0 + +diffusion_type: reflow # ddpm +time_scale_factor: 1000 +schedule_type: 'linear' +K_step: 1000 +timesteps: 1000 +max_beta: 0.02 +main_loss_type: l2 +main_loss_log_norm: true +sampling_algorithm: euler +sampling_steps: 20 +diff_accelerator: ddim +diff_speedup: 10 + +# train and eval +num_sanity_val_steps: 1 +optimizer_args: + optimizer_cls: modules.optimizer.muon.Muon_AdamW + lr: 0.0006 + muon_args: + weight_decay: 0.1 + adamw_args: + weight_decay: 0.0 +lr_scheduler_args: + step_size: 5000 + gamma: 0.8 +max_batch_frames: 80000 +max_batch_size: 48 +dataset_size_key: 'lengths' +val_check_interval: 4000 +num_valid_plots: 10 +max_updates: 80000 +num_ckpt_keep: 5 +permanent_ckpt_start: 30000 +permanent_ckpt_interval: 10000 + +finetune_enabled: false +finetune_ckpt_path: null +finetune_ignored_params: + - model.spk_embed + - model.fs2.txt_embed + - model.fs2.encoder.embed_tokens +finetune_strict_shapes: true + +freezing_enabled: false +frozen_params: [] diff --git a/data/.gitkeep b/data/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/deployment/.gitignore b/deployment/.gitignore new file mode 100644 index 0000000..bab8dba --- /dev/null +++ b/deployment/.gitignore @@ -0,0 +1,7 @@ +*.ds +*.onnx +*.npy +*.wav +temp/ +cache/ +assets/ diff --git a/deployment/__init__.py b/deployment/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/deployment/benchmarks/infer_acoustic.py b/deployment/benchmarks/infer_acoustic.py new file mode 100644 index 0000000..588462a --- /dev/null +++ b/deployment/benchmarks/infer_acoustic.py @@ -0,0 +1,32 @@ +import numpy as np +import onnxruntime as ort +import tqdm + +n_tokens = 10 +n_frames = 100 +n_runs = 20 +speedup = 20 +provider = 'DmlExecutionProvider' + +tokens = np.array([[1] * n_tokens], dtype=np.int64) +durations = np.array([[n_frames // n_tokens] * n_tokens], dtype=np.int64) +f0 = np.array([[440.] * n_frames], dtype=np.float32) +speedup = np.array(speedup, dtype=np.int64) + +session = ort.InferenceSession('model1.onnx', providers=[provider]) +for _ in tqdm.tqdm(range(n_runs)): + session.run(['mel'], { + 'tokens': tokens, + 'durations': durations, + 'f0': f0, + 'speedup': speedup + }) + +session = ort.InferenceSession('model2.onnx', providers=[provider]) +for _ in tqdm.tqdm(range(n_runs)): + session.run(['mel'], { + 'tokens': tokens, + 'durations': durations, + 'f0': f0, + 'speedup': speedup + }) diff --git a/deployment/benchmarks/infer_nsf_hifigan.py b/deployment/benchmarks/infer_nsf_hifigan.py new file mode 100644 index 0000000..da348c8 --- /dev/null +++ b/deployment/benchmarks/infer_nsf_hifigan.py @@ -0,0 +1,16 @@ +import numpy as np +import onnxruntime as ort +import tqdm + +n_frames = 1000 +n_runs = 20 +mel = np.random.randn(1, n_frames, 128).astype(np.float32) +f0 = np.random.randn(1, n_frames).astype(np.float32) + 440. +provider = 'DmlExecutionProvider' + +session = ort.InferenceSession('nsf_hifigan.onnx', providers=[provider]) +for _ in tqdm.tqdm(range(n_runs)): + session.run(['waveform'], { + 'mel': mel, + 'f0': f0 + }) diff --git a/deployment/exporters/__init__.py b/deployment/exporters/__init__.py new file mode 100644 index 0000000..910d3e0 --- /dev/null +++ b/deployment/exporters/__init__.py @@ -0,0 +1,3 @@ +from .acoustic_exporter import DiffSingerAcousticExporter +from .variance_exporter import DiffSingerVarianceExporter +from .nsf_hifigan_exporter import NSFHiFiGANExporter diff --git a/deployment/exporters/acoustic_exporter.py b/deployment/exporters/acoustic_exporter.py new file mode 100644 index 0000000..1645c83 --- /dev/null +++ b/deployment/exporters/acoustic_exporter.py @@ -0,0 +1,424 @@ +import json +from pathlib import Path +from typing import Union, List, Tuple, Dict + +import onnx +import onnxsim +import torch +import yaml + +from basics.base_exporter import BaseExporter +from deployment.modules.toplevel import DiffSingerAcousticONNX +from modules.fastspeech.param_adaptor import VARIANCE_CHECKLIST +from utils import load_ckpt, onnx_helper, remove_suffix +from utils.hparams import hparams +from utils.phoneme_utils import load_phoneme_dictionary + + +class DiffSingerAcousticExporter(BaseExporter): + def __init__( + self, + device: Union[str, torch.device] = 'cpu', + cache_dir: Path = None, + ckpt_steps: int = None, + freeze_gender: float = None, + freeze_velocity: bool = False, + export_spk: List[Tuple[str, Dict[str, float]]] = None, + freeze_spk: Tuple[str, Dict[str, float]] = None + ): + super().__init__(device=device, cache_dir=cache_dir) + # Basic attributes + self.model_name: str = hparams['exp_name'] + self.ckpt_steps: int = ckpt_steps + self.spk_map: dict = self.build_spk_map() + self.lang_map: dict = self.build_lang_map() + self.phoneme_dictionary = load_phoneme_dictionary() + self.use_lang_id = hparams.get('use_lang_id', False) and len(self.phoneme_dictionary.cross_lingual_phonemes) > 0 + self.model = self.build_model() + self.fs2_aux_cache_path = self.cache_dir / ( + 'fs2_aux.onnx' if self.model.use_shallow_diffusion else 'fs2.onnx' + ) + self.diffusion_cache_path = self.cache_dir / 'diffusion.onnx' + + # Attributes for logging + self.model_class_name = remove_suffix(self.model.__class__.__name__, 'ONNX') + fs2_aux_cls_logging = [remove_suffix(self.model.fs2.__class__.__name__, 'ONNX')] + if self.model.use_shallow_diffusion: + fs2_aux_cls_logging.append(remove_suffix( + self.model.aux_decoder.decoder.__class__.__name__, 'ONNX' + )) + self.fs2_aux_class_name = ', '.join(fs2_aux_cls_logging) + self.aux_decoder_class_name = remove_suffix( + self.model.aux_decoder.decoder.__class__.__name__, 'ONNX' + ) if self.model.use_shallow_diffusion else None + self.backbone_class_name = remove_suffix(self.model.diffusion.backbone.__class__.__name__, 'ONNX') + self.diffusion_class_name = remove_suffix(self.model.diffusion.__class__.__name__, 'ONNX') + + # Attributes for exporting + self.expose_gender = freeze_gender is None + self.expose_velocity = not freeze_velocity + self.freeze_spk: Tuple[str, Dict[str, float]] = freeze_spk \ + if hparams['use_spk_id'] else None + self.export_spk: List[Tuple[str, Dict[str, float]]] = export_spk \ + if hparams['use_spk_id'] and export_spk is not None else [] + if hparams['use_key_shift_embed'] and not self.expose_gender: + shift_min, shift_max = hparams['augmentation_args']['random_pitch_shifting']['range'] + key_shift = freeze_gender * shift_max if freeze_gender >= 0. else freeze_gender * abs(shift_min) + key_shift = max(min(key_shift, shift_max), shift_min) # clip key shift + self.model.fs2.register_buffer('frozen_key_shift', torch.FloatTensor([key_shift]).to(self.device)) + if hparams['use_spk_id']: + if not self.export_spk and self.freeze_spk is None: + # In case the user did not specify any speaker settings: + if len(self.spk_map) == 1: + # If there is only one speaker, freeze him/her. + first_spk = next(iter(self.spk_map.keys())) + self.freeze_spk = (first_spk, {first_spk: 1.0}) + else: + # If there are multiple speakers, export them all. + self.export_spk = [(name, {name: 1.0}) for name in self.spk_map.keys()] + if self.freeze_spk is not None: + self.model.fs2.register_buffer('frozen_spk_embed', self._perform_spk_mix(self.freeze_spk[1])) + + def build_model(self) -> DiffSingerAcousticONNX: + model = DiffSingerAcousticONNX( + vocab_size=len(self.phoneme_dictionary), + out_dims=hparams['audio_num_mel_bins'], + cross_lingual_token_idx=sorted({ + self.phoneme_dictionary.encode_one(p) + for p in self.phoneme_dictionary.cross_lingual_phonemes + }) + ).eval().to(self.device) + load_ckpt(model, hparams['work_dir'], ckpt_steps=self.ckpt_steps, + prefix_in_ckpt='model', strict=True, device=self.device) + return model + + def export(self, path: Path): + path.mkdir(parents=True, exist_ok=True) + model_name = self.model_name + if self.freeze_spk is not None: + model_name += '.' + self.freeze_spk[0] + self.export_model(path / f'{model_name}.onnx') + self.export_attachments(path) + + def export_model(self, path: Path): + self._torch_export_model() + fs2_aux_onnx = self._optimize_fs2_aux_graph(onnx.load(self.fs2_aux_cache_path)) + diffusion_onnx = self._optimize_diffusion_graph(onnx.load(self.diffusion_cache_path)) + model_onnx = self._merge_fs2_aux_diffusion_graphs(fs2_aux_onnx, diffusion_onnx) + onnx.save(model_onnx, path) + self.fs2_aux_cache_path.unlink() + self.diffusion_cache_path.unlink() + print(f'| export model => {path}') + + def export_attachments(self, path: Path): + for spk in self.export_spk: + self._export_spk_embed( + path / f'{self.model_name}.{spk[0]}.emb', + self._perform_spk_mix(spk[1]) + ) + self.export_dictionaries(path) + self._export_phonemes(path) + + model_name = self.model_name + if self.freeze_spk is not None: + model_name += '.' + self.freeze_spk[0] + dsconfig = { + # basic configs + 'phonemes': f'{self.model_name}.phonemes.json', + 'languages': f'{self.model_name}.languages.json', + 'use_lang_id': self.use_lang_id, + 'acoustic': f'{model_name}.onnx', + 'hidden_size': hparams['hidden_size'], + 'vocoder': 'pc_nsf_hifigan_44.1k_hop512_128bin_2025.02', + } + # multi-speaker + if len(self.export_spk) > 0: + dsconfig['speakers'] = [f'{self.model_name}.{spk[0]}' for spk in self.export_spk] + # parameters + if self.expose_gender: + dsconfig['augmentation_args'] = { + 'random_pitch_shifting': { + 'range': hparams['augmentation_args']['random_pitch_shifting']['range'] + } + } + dsconfig['use_key_shift_embed'] = self.expose_gender + dsconfig['use_speed_embed'] = self.expose_velocity + for variance in VARIANCE_CHECKLIST: + dsconfig[f'use_{variance}_embed'] = (variance in self.model.fs2.variance_embed_list) + # sampling acceleration and shallow diffusion + dsconfig['use_continuous_acceleration'] = True + dsconfig['use_variable_depth'] = self.model.use_shallow_diffusion + dsconfig['max_depth'] = 1 - self.model.diffusion.t_start + # mel specification + dsconfig['sample_rate'] = hparams['audio_sample_rate'] + dsconfig['hop_size'] = hparams['hop_size'] + dsconfig['win_size'] = hparams['win_size'] + dsconfig['fft_size'] = hparams['fft_size'] + dsconfig['num_mel_bins'] = hparams['audio_num_mel_bins'] + dsconfig['mel_fmin'] = hparams['fmin'] + dsconfig['mel_fmax'] = hparams['fmax'] if hparams['fmax'] is not None else hparams['audio_sample_rate'] / 2 + dsconfig['mel_base'] = 'e' + dsconfig['mel_scale'] = 'slaney' + config_path = path / 'dsconfig.yaml' + with open(config_path, 'w', encoding='utf8') as fw: + yaml.safe_dump(dsconfig, fw, sort_keys=False) + print(f'| export configs => {config_path} **PLEASE EDIT BEFORE USE**') + + @torch.no_grad() + def _torch_export_model(self): + # Prepare inputs for FastSpeech2 and aux decoder tracing + n_frames = 10 + tokens = torch.LongTensor([[1]]).to(self.device) + durations = torch.LongTensor([[n_frames]]).to(self.device) + f0 = torch.FloatTensor([[440.] * n_frames]).to(self.device) + variances = { + v_name: torch.zeros(1, n_frames, dtype=torch.float32, device=self.device) + for v_name in self.model.fs2.variance_embed_list + } + kwargs: Dict[str, torch.Tensor] = {} + arguments = (tokens, durations, f0, variances, kwargs) + input_names = ['tokens', 'durations', 'f0'] + self.model.fs2.variance_embed_list + dynamic_axes = { + 'tokens': { + 1: 'n_tokens' + }, + 'durations': { + 1: 'n_tokens' + }, + 'f0': { + 1: 'n_frames' + }, + **{ + v_name: { + 1: 'n_frames' + } + for v_name in self.model.fs2.variance_embed_list + } + } + if hparams['use_key_shift_embed']: + if self.expose_gender: + kwargs['gender'] = torch.rand((1, n_frames), dtype=torch.float32, device=self.device) + input_names.append('gender') + dynamic_axes['gender'] = { + 1: 'n_frames' + } + if hparams['use_speed_embed']: + if self.expose_velocity: + kwargs['velocity'] = torch.rand((1, n_frames), dtype=torch.float32, device=self.device) + input_names.append('velocity') + dynamic_axes['velocity'] = { + 1: 'n_frames' + } + if hparams['use_spk_id'] and not self.freeze_spk: + kwargs['spk_embed'] = torch.rand( + (1, n_frames, hparams['hidden_size']), + dtype=torch.float32, device=self.device + ) + input_names.append('spk_embed') + dynamic_axes['spk_embed'] = { + 1: 'n_frames' + } + if self.use_lang_id: + kwargs['languages'] = torch.zeros_like(tokens) + input_names.append('languages') + dynamic_axes['languages'] = { + 1: 'n_tokens' + } + dynamic_axes['condition'] = { + 1: 'n_frames' + } + + # PyTorch ONNX export for FastSpeech2 and aux decoder + output_names = ['condition'] + if self.model.use_shallow_diffusion: + output_names.append('aux_mel') + dynamic_axes['aux_mel'] = { + 1: 'n_frames' + } + print(f'Exporting {self.fs2_aux_class_name}...') + torch.onnx.export( + self.model.view_as_fs2_aux(), + arguments, + self.fs2_aux_cache_path, + input_names=input_names, + output_names=output_names, + dynamic_axes=dynamic_axes, + opset_version=17, + **onnx_helper.TORCHSCRIPT_EXPORT_KWARGS + ) + + condition = torch.rand((1, n_frames, hparams['hidden_size']), device=self.device) + + # Prepare inputs for backbone tracing and GaussianDiffusion scripting + shape = (1, 1, hparams['audio_num_mel_bins'], n_frames) + noise = torch.randn(shape, device=self.device) + x_aux = torch.randn((1, n_frames, hparams['audio_num_mel_bins']), device=self.device) + dummy_time = (torch.rand((1,), device=self.device) * self.model.diffusion.time_scale_factor).float() + dummy_depth = torch.tensor(0.1, device=self.device) + dummy_steps = 5 + + print(f'Tracing {self.backbone_class_name} backbone...') + if self.model.diffusion_type == 'ddpm': + major_mel_decoder = self.model.view_as_diffusion() + elif self.model.diffusion_type == 'reflow': + major_mel_decoder = self.model.view_as_reflow() + else: + raise ValueError(f'Invalid diffusion type: {self.model.diffusion_type}') + major_mel_decoder.diffusion.set_backbone( + torch.jit.trace( + major_mel_decoder.diffusion.backbone, + ( + noise, + dummy_time, + condition.transpose(1, 2) + ) + ) + ) + + print(f'Scripting {self.diffusion_class_name}...') + diffusion_inputs = [ + condition, + *([x_aux, dummy_depth] if self.model.use_shallow_diffusion else []) + ] + major_mel_decoder = torch.jit.script( + major_mel_decoder, + example_inputs=[ + ( + *diffusion_inputs, + 1 # p_sample branch + ), + ( + *diffusion_inputs, + dummy_steps # p_sample_plms branch + ) + ] + ) + + # PyTorch ONNX export for GaussianDiffusion + print(f'Exporting {self.diffusion_class_name}...') + torch.onnx.export( + major_mel_decoder, + ( + *diffusion_inputs, + dummy_steps + ), + self.diffusion_cache_path, + input_names=[ + 'condition', + *(['x_aux', 'depth'] if self.model.use_shallow_diffusion else []), + 'steps' + ], + output_names=[ + 'mel' + ], + dynamic_axes={ + 'condition': { + 1: 'n_frames' + }, + **({'x_aux': {1: 'n_frames'}} if self.model.use_shallow_diffusion else {}), + 'mel': { + 1: 'n_frames' + } + }, + opset_version=17, + **onnx_helper.TORCHSCRIPT_EXPORT_KWARGS + ) + + @torch.no_grad() + def _perform_spk_mix(self, spk_mix: Dict[str, float]): + spk_mix_ids = [] + spk_mix_values = [] + for name, value in spk_mix.items(): + spk_mix_ids.append(self.spk_map[name]) + assert value >= 0., f'Speaker mix checks failed.\n' \ + f'Proportion of speaker \'{name}\' is negative.' + spk_mix_values.append(value) + spk_mix_id_N = torch.LongTensor(spk_mix_ids).to(self.device)[None] # => [1, N] + spk_mix_value_N = torch.FloatTensor(spk_mix_values).to(self.device)[None] # => [1, N] + spk_mix_value_sum = spk_mix_value_N.sum() + assert spk_mix_value_sum > 0., 'Speaker mix checks failed.\n' \ + 'Proportions of speaker mix sum to zero.' + spk_mix_value_N /= spk_mix_value_sum # normalize + spk_mix_embed = torch.sum( + self.model.fs2.spk_embed(spk_mix_id_N) * spk_mix_value_N.unsqueeze(2), # => [1, N, H] + dim=1, keepdim=True + ) # => [1, 1, H] + return spk_mix_embed + + def _optimize_fs2_aux_graph(self, fs2: onnx.ModelProto) -> onnx.ModelProto: + print(f'Running ONNX Simplifier on {self.fs2_aux_class_name}...') + fs2, check = onnxsim.simplify(fs2, include_subgraph=True) + assert check, 'Simplified ONNX model could not be validated' + onnx_helper.model_reorder_io_list( + fs2, 'input', + target_name='languages', insert_after_name='tokens' + ) + print(f'| optimize graph: {self.fs2_aux_class_name}') + return fs2 + + def _optimize_diffusion_graph(self, diffusion: onnx.ModelProto) -> onnx.ModelProto: + onnx_helper.model_override_io_shapes(diffusion, output_shapes={ + 'mel': (1, 'n_frames', hparams['audio_num_mel_bins']) + }) + print(f'Running ONNX Simplifier #1 on {self.diffusion_class_name}...') + diffusion, check = onnxsim.simplify(diffusion, include_subgraph=True) + assert check, 'Simplified ONNX model could not be validated' + onnx_helper.graph_fold_back_to_squeeze(diffusion.graph) + onnx_helper.graph_extract_conditioner_projections( + graph=diffusion.graph, op_type='Conv', + weight_pattern=r'diffusion\..*\.conditioner_projection\.weight', + alias_prefix='/diffusion/backbone/cache' + ) + onnx_helper.graph_remove_unused_values(diffusion.graph) + print(f'Running ONNX Simplifier #2 on {self.diffusion_class_name}...') + diffusion, check = onnxsim.simplify( + diffusion, + include_subgraph=True + ) + assert check, 'Simplified ONNX model could not be validated' + print(f'| optimize graph: {self.diffusion_class_name}') + return diffusion + + def _merge_fs2_aux_diffusion_graphs(self, fs2: onnx.ModelProto, diffusion: onnx.ModelProto) -> onnx.ModelProto: + onnx_helper.model_add_prefixes( + fs2, dim_prefix=('fs2aux.' if self.model.use_shallow_diffusion else 'fs2.'), + ignored_pattern=r'(n_tokens)|(n_frames)' + ) + onnx_helper.model_add_prefixes(diffusion, dim_prefix='diffusion.', ignored_pattern='n_frames') + print(f'Merging {self.fs2_aux_class_name} and {self.diffusion_class_name} ' + f'back into {self.model_class_name}...') + merged = onnx.compose.merge_models( + fs2, diffusion, io_map=[ + ('condition', 'condition'), + *([('aux_mel', 'x_aux')] if self.model.use_shallow_diffusion else []), + ], + prefix1='', prefix2='', doc_string='', + producer_name=fs2.producer_name, producer_version=fs2.producer_version, + domain=fs2.domain, model_version=fs2.model_version + ) + merged.graph.name = fs2.graph.name + + print(f'Running ONNX Simplifier on {self.model_class_name}...') + merged, check = onnxsim.simplify( + merged, + include_subgraph=True + ) + assert check, 'Simplified ONNX model could not be validated' + print(f'| optimize graph: {self.model_class_name}') + + return merged + + # noinspection PyMethodMayBeStatic + def _export_spk_embed(self, path: Path, spk_embed: torch.Tensor): + with open(path, 'wb') as f: + f.write(spk_embed.cpu().numpy().tobytes()) + print(f'| export spk embed => {path}') + + def _export_phonemes(self, path: Path): + ph_path = path / f'{self.model_name}.phonemes.json' + self.phoneme_dictionary.dump(ph_path) + print(f'| export phonemes => {ph_path}') + lang_path = path / f'{self.model_name}.languages.json' + with open(lang_path, 'w', encoding='utf8') as f: + json.dump(self.lang_map, f, ensure_ascii=False, indent=2) + print(f'| export languages => {lang_path}') diff --git a/deployment/exporters/nsf_hifigan_exporter.py b/deployment/exporters/nsf_hifigan_exporter.py new file mode 100644 index 0000000..26f2aa4 --- /dev/null +++ b/deployment/exporters/nsf_hifigan_exporter.py @@ -0,0 +1,127 @@ +import json +from pathlib import Path +from typing import Union + +import onnx +import onnxsim +import torch +import yaml +from torch import nn + +from basics.base_exporter import BaseExporter +from deployment.modules.nsf_hifigan import NSFHiFiGANONNX +from utils import load_ckpt, onnx_helper, remove_suffix +from utils.hparams import hparams + + +class NSFHiFiGANExporter(BaseExporter): + def __init__( + self, + device: Union[str, torch.device] = 'cpu', + cache_dir: Path = None, + model_path: Path = None, + model_name: str = 'nsf_hifigan' + ): + super().__init__(device=device, cache_dir=cache_dir) + self.model_path = model_path + self.model_name = model_name + self.vocoder_pitch_controllable = False + self.model = self.build_model() + self.model_class_name = remove_suffix(self.model.__class__.__name__, 'ONNX') + self.model_cache_path = (self.cache_dir / self.model_name).with_suffix('.onnx') + + def build_model(self) -> nn.Module: + config_path = self.model_path.with_name('config.json') + with open(config_path, 'r', encoding='utf8') as f: + config = json.load(f) + assert hparams.get('mel_base') == 'e', ( + "Mel base must be set to \'e\' according to 2nd stage of the migration plan. " + "See https://github.com/openvpi/DiffSinger/releases/tag/v2.3.0 for more details." + ) + model = NSFHiFiGANONNX(config).eval().to(self.device) + self.vocoder_pitch_controllable = config.get("pc_aug", False) + load_ckpt(model.generator, str(self.model_path), + prefix_in_ckpt=None, key_in_ckpt='generator', + strict=True, device=self.device) + model.generator.remove_weight_norm() + return model + + def export(self, path: Path): + path.mkdir(parents=True, exist_ok=True) + self.export_model(path / self.model_cache_path.name) + self.export_attachments(path) + + def export_model(self, path: Path): + self._torch_export_model() + model_onnx = self._optimize_model_graph(onnx.load(self.model_cache_path)) + onnx.save(model_onnx, path) + self.model_cache_path.unlink() + print(f'| export model => {path}') + + def export_attachments(self, path: Path): + config_path = path / 'vocoder.yaml' + with open(config_path, 'w', encoding='utf8') as fw: + yaml.safe_dump({ + # basic configs + 'name': self.model_name, + 'model': self.model_cache_path.name, + # mel specifications + 'sample_rate': hparams['audio_sample_rate'], + 'hop_size': hparams['hop_size'], + 'win_size': hparams['win_size'], + 'fft_size': hparams['fft_size'], + 'num_mel_bins': hparams['audio_num_mel_bins'], + 'mel_fmin': hparams['fmin'], + 'mel_fmax': hparams['fmax'] if hparams['fmax'] is not None else hparams['audio_sample_rate'] / 2, + 'mel_base': 'e', + 'mel_scale': 'slaney', + 'pitch_controllable': self.vocoder_pitch_controllable, + # Some old vocoder versions may have severe performance issues on CUDA; + # the issues were fixed in newer versions, and this flag is to distinguish them + 'force_on_cpu': False, + }, fw, sort_keys=False) + print(f'| export configs => {config_path} **PLEASE EDIT BEFORE USE**') + + @torch.no_grad() + def _torch_export_model(self): + # Prepare inputs for NSFHiFiGAN + n_frames = 10 + mel = torch.randn((1, n_frames, hparams['audio_num_mel_bins']), dtype=torch.float32, device=self.device) + f0 = torch.randn((1, n_frames), dtype=torch.float32, device=self.device) + 440. + + # PyTorch ONNX export for NSFHiFiGAN + print(f'Exporting {self.model_class_name}...') + torch.onnx.export( + self.model, + ( + mel, + f0 + ), + self.model_cache_path, + input_names=[ + 'mel', + 'f0' + ], + output_names=[ + 'waveform' + ], + dynamic_axes={ + 'mel': { + 1: 'n_frames' + }, + 'f0': { + 1: 'n_frames' + }, + 'waveform': { + 1: 'n_samples' + } + }, + opset_version=17, + **onnx_helper.TORCHSCRIPT_EXPORT_KWARGS + ) + + def _optimize_model_graph(self, model: onnx.ModelProto) -> onnx.ModelProto: + print(f'Running ONNX simplifier for {self.model_class_name}...') + model, check = onnxsim.simplify(model, include_subgraph=True) + assert check, 'Simplified ONNX model could not be validated' + return model diff --git a/deployment/exporters/variance_exporter.py b/deployment/exporters/variance_exporter.py new file mode 100644 index 0000000..e8832ff --- /dev/null +++ b/deployment/exporters/variance_exporter.py @@ -0,0 +1,808 @@ +import json +from pathlib import Path +from typing import Union, List, Tuple, Dict + +import onnx +import onnxsim +import torch +import yaml + +from basics.base_exporter import BaseExporter +from deployment.modules.toplevel import DiffSingerVarianceONNX +from modules.fastspeech.param_adaptor import VARIANCE_CHECKLIST +from utils import load_ckpt, onnx_helper, remove_suffix +from utils.hparams import hparams +from utils.phoneme_utils import load_phoneme_dictionary + + +class DiffSingerVarianceExporter(BaseExporter): + def __init__( + self, + device: Union[str, torch.device] = 'cpu', + cache_dir: Path = None, + ckpt_steps: int = None, + freeze_glide: bool = False, + freeze_expr: bool = False, + export_spk: List[Tuple[str, Dict[str, float]]] = None, + freeze_spk: Tuple[str, Dict[str, float]] = None + ): + super().__init__(device=device, cache_dir=cache_dir) + # Basic attributes + self.model_name: str = hparams['exp_name'] + self.ckpt_steps: int = ckpt_steps + self.spk_map: dict = self.build_spk_map() + self.lang_map: dict = self.build_lang_map() + self.phoneme_dictionary = load_phoneme_dictionary() + self.use_lang_id = hparams.get('use_lang_id', False) and len(self.phoneme_dictionary.cross_lingual_phonemes) > 0 + self.model = self.build_model() + self.linguistic_encoder_cache_path = self.cache_dir / 'linguistic.onnx' + self.dur_predictor_cache_path = self.cache_dir / 'dur.onnx' + self.pitch_preprocess_cache_path = self.cache_dir / 'pitch_pre.onnx' + self.pitch_predictor_cache_path = self.cache_dir / 'pitch.onnx' + self.pitch_postprocess_cache_path = self.cache_dir / 'pitch_post.onnx' + self.variance_preprocess_cache_path = self.cache_dir / 'variance_pre.onnx' + self.multi_var_predictor_cache_path = self.cache_dir / 'variance.onnx' + self.variance_postprocess_cache_path = self.cache_dir / 'variance_post.onnx' + + # Attributes for logging + self.fs2_class_name = remove_suffix(self.model.fs2.__class__.__name__, 'ONNX') + self.dur_predictor_class_name = \ + remove_suffix(self.model.fs2.dur_predictor.__class__.__name__, 'ONNX') \ + if self.model.predict_dur else None + self.pitch_backbone_class_name = \ + remove_suffix(self.model.pitch_predictor.backbone.__class__.__name__, 'ONNX') \ + if self.model.predict_pitch else None + self.pitch_predictor_class_name = \ + remove_suffix(self.model.pitch_predictor.__class__.__name__, 'ONNX') \ + if self.model.predict_pitch else None + self.variance_backbone_class_name = \ + remove_suffix(self.model.variance_predictor.backbone.__class__.__name__, 'ONNX') \ + if self.model.predict_variances else None + self.multi_var_predictor_class_name = \ + remove_suffix(self.model.variance_predictor.__class__.__name__, 'ONNX') \ + if self.model.predict_variances else None + + # Attributes for exporting + self.expose_expr = not freeze_expr + self.freeze_glide = freeze_glide + self.freeze_spk: Tuple[str, Dict[str, float]] = freeze_spk \ + if hparams['use_spk_id'] else None + self.export_spk: List[Tuple[str, Dict[str, float]]] = export_spk \ + if hparams['use_spk_id'] and export_spk is not None else [] + if hparams['use_spk_id']: + if not self.export_spk and self.freeze_spk is None: + # In case the user did not specify any speaker settings: + if len(self.spk_map) == 1: + # If there is only one speaker, freeze him/her. + first_spk = next(iter(self.spk_map.keys())) + self.freeze_spk = (first_spk, {first_spk: 1.0}) + else: + # If there are multiple speakers, export them all. + self.export_spk = [(name, {name: 1.0}) for name in self.spk_map.keys()] + if self.freeze_spk is not None: + self.model.register_buffer('frozen_spk_embed', self._perform_spk_mix(self.freeze_spk[1])) + + def build_model(self) -> DiffSingerVarianceONNX: + model = DiffSingerVarianceONNX( + vocab_size=len(self.phoneme_dictionary), + cross_lingual_token_idx=sorted({ + self.phoneme_dictionary.encode_one(p) + for p in self.phoneme_dictionary.cross_lingual_phonemes + }) + ).eval().to(self.device) + load_ckpt(model, hparams['work_dir'], ckpt_steps=self.ckpt_steps, + prefix_in_ckpt='model', strict=True, device=self.device) + model.build_smooth_op(self.device) + return model + + def export(self, path: Path): + path.mkdir(parents=True, exist_ok=True) + model_name = self.model_name + if self.freeze_spk is not None: + model_name += '.' + self.freeze_spk[0] + self.export_model(path, model_name) + self.export_attachments(path) + + def export_model(self, path: Path, model_name: str = None): + self._torch_export_model() + linguistic_onnx = self._optimize_linguistic_graph(onnx.load(self.linguistic_encoder_cache_path)) + linguistic_path = path / f'{model_name}.linguistic.onnx' + onnx.save(linguistic_onnx, linguistic_path) + print(f'| export linguistic encoder => {linguistic_path}') + self.linguistic_encoder_cache_path.unlink() + if self.model.predict_dur: + dur_predictor_onnx = self._optimize_dur_predictor_graph(onnx.load(self.dur_predictor_cache_path)) + dur_predictor_path = path / f'{model_name}.dur.onnx' + onnx.save(dur_predictor_onnx, dur_predictor_path) + self.dur_predictor_cache_path.unlink() + print(f'| export dur predictor => {dur_predictor_path}') + if self.model.predict_pitch: + pitch_predictor_onnx = self._optimize_merge_pitch_predictor_graph( + onnx.load(self.pitch_preprocess_cache_path), + onnx.load(self.pitch_predictor_cache_path), + onnx.load(self.pitch_postprocess_cache_path) + ) + pitch_predictor_path = path / f'{model_name}.pitch.onnx' + onnx.save(pitch_predictor_onnx, pitch_predictor_path) + self.pitch_preprocess_cache_path.unlink() + self.pitch_predictor_cache_path.unlink() + self.pitch_postprocess_cache_path.unlink() + print(f'| export pitch predictor => {pitch_predictor_path}') + if self.model.predict_variances: + variance_predictor_onnx = self._optimize_merge_variance_predictor_graph( + onnx.load(self.variance_preprocess_cache_path), + onnx.load(self.multi_var_predictor_cache_path), + onnx.load(self.variance_postprocess_cache_path) + ) + variance_predictor_path = path / f'{model_name}.variance.onnx' + onnx.save(variance_predictor_onnx, variance_predictor_path) + self.variance_preprocess_cache_path.unlink() + self.multi_var_predictor_cache_path.unlink() + self.variance_postprocess_cache_path.unlink() + print(f'| export variance predictor => {variance_predictor_path}') + + def export_attachments(self, path: Path): + for spk in self.export_spk: + self._export_spk_embed( + path / f'{self.model_name}.{spk[0]}.emb', + self._perform_spk_mix(spk[1]) + ) + self.export_dictionaries(path) + self._export_phonemes(path) + + model_name = self.model_name + if self.freeze_spk is not None: + model_name += '.' + self.freeze_spk[0] + dsconfig = { + # basic configs + 'phonemes': f'{self.model_name}.phonemes.json', + 'languages': f'{self.model_name}.languages.json', + 'use_lang_id': self.use_lang_id, + 'linguistic': f'{model_name}.linguistic.onnx', + 'hidden_size': self.model.hidden_size, + 'predict_dur': self.model.predict_dur, + } + # multi-speaker + if len(self.export_spk) > 0: + dsconfig['speakers'] = [f'{self.model_name}.{spk[0]}' for spk in self.export_spk] + # functionalities + if self.model.predict_dur: + dsconfig['dur'] = f'{model_name}.dur.onnx' + if self.model.predict_pitch: + dsconfig['pitch'] = f'{model_name}.pitch.onnx' + dsconfig['use_expr'] = self.expose_expr + dsconfig['use_note_rest'] = self.model.use_melody_encoder + if self.model.predict_variances: + dsconfig['variance'] = f'{model_name}.variance.onnx' + for variance in VARIANCE_CHECKLIST: + dsconfig[f'predict_{variance}'] = (variance in self.model.variance_prediction_list) + # sampling acceleration + dsconfig['use_continuous_acceleration'] = True + # frame specifications + dsconfig['sample_rate'] = hparams['audio_sample_rate'] + dsconfig['hop_size'] = hparams['hop_size'] + config_path = path / 'dsconfig.yaml' + with open(config_path, 'w', encoding='utf8') as fw: + yaml.safe_dump(dsconfig, fw, sort_keys=False) + print(f'| export configs => {config_path} **PLEASE EDIT BEFORE USE**') + + @torch.no_grad() + def _torch_export_model(self): + # Prepare inputs for FastSpeech2 and dur predictor tracing + tokens = torch.LongTensor([[1] * 5]).to(self.device) + ph_dur = torch.LongTensor([[3, 5, 2, 1, 4]]).to(self.device) + word_div = torch.LongTensor([[2, 2, 1]]).to(self.device) + word_dur = torch.LongTensor([[8, 3, 4]]).to(self.device) + languages = torch.LongTensor([[0] * 5]).to(self.device) + encoder_out = torch.rand(1, 5, hparams['hidden_size'], dtype=torch.float32, device=self.device) + x_masks = tokens == 0 + ph_midi = torch.LongTensor([[60] * 5]).to(self.device) + encoder_output_names = ['encoder_out', 'x_masks'] + encoder_common_axes = { + 'encoder_out': { + 1: 'n_tokens' + }, + 'x_masks': { + 1: 'n_tokens' + } + } + input_lang_id = self.use_lang_id + input_spk_embed = hparams['use_spk_id'] and not self.freeze_spk + + print(f'Exporting {self.fs2_class_name}...') + if self.model.predict_dur: + torch.onnx.export( + self.model.view_as_linguistic_encoder(), + ( + tokens, + word_div, + word_dur, + *([languages] if input_lang_id else []) + ), + self.linguistic_encoder_cache_path, + input_names=[ + 'tokens', + 'word_div', + 'word_dur', + *(['languages'] if input_lang_id else []) + ], + output_names=encoder_output_names, + dynamic_axes={ + 'tokens': { + 1: 'n_tokens' + }, + 'word_div': { + 1: 'n_words' + }, + 'word_dur': { + 1: 'n_words' + }, + **encoder_common_axes, + **({'languages': {1: 'n_tokens'}} if input_lang_id else {}) + }, + opset_version=17, + **onnx_helper.TORCHSCRIPT_EXPORT_KWARGS + ) + + print(f'Exporting {self.dur_predictor_class_name}...') + torch.onnx.export( + self.model.view_as_dur_predictor(), + ( + encoder_out, + x_masks, + ph_midi, + *([torch.rand( + 1, 5, hparams['hidden_size'], + dtype=torch.float32, device=self.device + )] if input_spk_embed else []) + ), + self.dur_predictor_cache_path, + input_names=[ + 'encoder_out', + 'x_masks', + 'ph_midi', + *(['spk_embed'] if input_spk_embed else []) + ], + output_names=[ + 'ph_dur_pred' + ], + dynamic_axes={ + 'ph_midi': { + 1: 'n_tokens' + }, + 'ph_dur_pred': { + 1: 'n_tokens' + }, + **({'spk_embed': {1: 'n_tokens'}} if input_spk_embed else {}), + **encoder_common_axes + }, + opset_version=17, + **onnx_helper.TORCHSCRIPT_EXPORT_KWARGS + ) + else: + torch.onnx.export( + self.model.view_as_linguistic_encoder(), + ( + tokens, + ph_dur, + *([languages] if input_lang_id else []) + ), + self.linguistic_encoder_cache_path, + input_names=[ + 'tokens', + 'ph_dur', + *(['languages'] if input_lang_id else []) + ], + output_names=encoder_output_names, + dynamic_axes={ + 'tokens': { + 1: 'n_tokens' + }, + 'ph_dur': { + 1: 'n_tokens' + }, + **encoder_common_axes, + **({'languages': {1: 'n_tokens'}} if input_lang_id else {}) + }, + opset_version=17, + **onnx_helper.TORCHSCRIPT_EXPORT_KWARGS + ) + + # Common dummy inputs + dummy_time = (torch.rand((1,), device=self.device) * hparams.get('time_scale_factor', 1.0)).float() + dummy_steps = 5 + + if self.model.predict_pitch: + use_melody_encoder = hparams.get('use_melody_encoder', False) + use_glide_embed = use_melody_encoder and hparams['use_glide_embed'] and not self.freeze_glide + # Prepare inputs for preprocessor of the pitch predictor + note_midi = torch.FloatTensor([[60.] * 4]).to(self.device) + note_dur = torch.LongTensor([[2, 6, 3, 4]]).to(self.device) + pitch = torch.FloatTensor([[60.] * 15]).to(self.device) + retake = torch.ones_like(pitch, dtype=torch.bool) + pitch_input_args = ( + encoder_out, + ph_dur, + { + 'note_midi': note_midi, + **({'note_rest': note_midi >= 0} if use_melody_encoder else {}), + 'note_dur': note_dur, + **({'note_glide': torch.zeros_like(note_midi, dtype=torch.long)} if use_glide_embed else {}), + 'pitch': pitch, + **({'expr': torch.ones_like(pitch)} if self.expose_expr else {}), + 'retake': retake, + **({'spk_embed': torch.rand( + 1, 15, hparams['hidden_size'], dtype=torch.float32, device=self.device + )} if input_spk_embed else {}) + } + ) + torch.onnx.export( + self.model.view_as_pitch_preprocess(), + pitch_input_args, + self.pitch_preprocess_cache_path, + input_names=[ + 'encoder_out', 'ph_dur', 'note_midi', + *(['note_rest'] if use_melody_encoder else []), + 'note_dur', + *(['note_glide'] if use_glide_embed else []), + 'pitch', + *(['expr'] if self.expose_expr else []), + 'retake', + *(['spk_embed'] if input_spk_embed else []) + ], + output_names=[ + 'pitch_cond', 'base_pitch' + ], + dynamic_axes={ + 'encoder_out': { + 1: 'n_tokens' + }, + 'ph_dur': { + 1: 'n_tokens' + }, + 'note_midi': { + 1: 'n_notes' + }, + **({'note_rest': {1: 'n_notes'}} if use_melody_encoder else {}), + 'note_dur': { + 1: 'n_notes' + }, + **({'note_glide': {1: 'n_notes'}} if use_glide_embed else {}), + 'pitch': { + 1: 'n_frames' + }, + **({'expr': {1: 'n_frames'}} if self.expose_expr else {}), + 'retake': { + 1: 'n_frames' + }, + 'pitch_cond': { + 1: 'n_frames' + }, + 'base_pitch': { + 1: 'n_frames' + }, + **({'spk_embed': {1: 'n_frames'}} if input_spk_embed else {}) + }, + opset_version=17, + **onnx_helper.TORCHSCRIPT_EXPORT_KWARGS + ) + + # Prepare inputs for backbone tracing and pitch predictor scripting + shape = (1, 1, hparams['pitch_prediction_args']['repeat_bins'], 15) + noise = torch.randn(shape, device=self.device) + condition = torch.rand((1, hparams['hidden_size'], 15), device=self.device) + + print(f'Tracing {self.pitch_backbone_class_name} backbone...') + pitch_predictor = self.model.view_as_pitch_predictor() + pitch_predictor.pitch_predictor.set_backbone( + torch.jit.trace( + pitch_predictor.pitch_predictor.backbone, + ( + noise, + dummy_time, + condition + ) + ) + ) + + print(f'Scripting {self.pitch_predictor_class_name}...') + pitch_predictor = torch.jit.script( + pitch_predictor, + example_inputs=[ + ( + condition.transpose(1, 2), + 1 # p_sample branch + ), + ( + condition.transpose(1, 2), + dummy_steps # p_sample_plms branch + ) + ] + ) + + print(f'Exporting {self.pitch_predictor_class_name}...') + torch.onnx.export( + pitch_predictor, + ( + condition.transpose(1, 2), + dummy_steps + ), + self.pitch_predictor_cache_path, + input_names=[ + 'pitch_cond', + 'steps' + ], + output_names=[ + 'x_pred' + ], + dynamic_axes={ + 'pitch_cond': { + 1: 'n_frames' + }, + 'x_pred': { + 1: 'n_frames' + } + }, + opset_version=17, + **onnx_helper.TORCHSCRIPT_EXPORT_KWARGS + ) + + # Prepare inputs for postprocessor of the multi-variance predictor + torch.onnx.export( + self.model.view_as_pitch_postprocess(), + ( + pitch, + pitch + ), + self.pitch_postprocess_cache_path, + input_names=[ + 'x_pred', + 'base_pitch' + ], + output_names=[ + 'pitch_pred' + ], + dynamic_axes={ + 'x_pred': { + 1: 'n_frames' + }, + 'base_pitch': { + 1: 'n_frames' + }, + 'pitch_pred': { + 1: 'n_frames' + } + }, + opset_version=17, + **onnx_helper.TORCHSCRIPT_EXPORT_KWARGS + ) + + if self.model.predict_variances: + total_repeat_bins = hparams['variances_prediction_args']['total_repeat_bins'] + repeat_bins = total_repeat_bins // len(self.model.variance_prediction_list) + + # Prepare inputs for preprocessor of the multi-variance predictor + pitch = torch.FloatTensor([[60.] * 15]).to(self.device) + variances = { + v_name: torch.FloatTensor([[0.] * 15]).to(self.device) + for v_name in self.model.variance_prediction_list + } + retake = torch.ones_like(pitch, dtype=torch.bool)[..., None].tile(len(self.model.variance_prediction_list)) + torch.onnx.export( + self.model.view_as_variance_preprocess(), + ( + encoder_out, + ph_dur, + pitch, + variances, + retake, + *([torch.rand( + 1, 15, hparams['hidden_size'], + dtype=torch.float32, device=self.device + )] if input_spk_embed else []) + ), + self.variance_preprocess_cache_path, + input_names=[ + 'encoder_out', 'ph_dur', 'pitch', + *self.model.variance_prediction_list, + 'retake', + *(['spk_embed'] if input_spk_embed else []) + ], + output_names=[ + 'variance_cond' + ], + dynamic_axes={ + 'encoder_out': { + 1: 'n_tokens' + }, + 'ph_dur': { + 1: 'n_tokens' + }, + 'pitch': { + 1: 'n_frames' + }, + **{ + v_name: { + 1: 'n_frames' + } + for v_name in self.model.variance_prediction_list + }, + 'retake': { + 1: 'n_frames' + }, + **({'spk_embed': {1: 'n_frames'}} if input_spk_embed else {}) + }, + opset_version=17, + **onnx_helper.TORCHSCRIPT_EXPORT_KWARGS + ) + + # Prepare inputs for backbone tracing and multi-variance predictor scripting + shape = (1, len(self.model.variance_prediction_list), repeat_bins, 15) + noise = torch.randn(shape, device=self.device) + condition = torch.rand((1, hparams['hidden_size'], 15), device=self.device) + step = (torch.rand((1,), device=self.device) * hparams.get('time_scale_factor', hparams['K_step'])) + + print(f'Tracing {self.variance_backbone_class_name} backbone...') + multi_var_predictor = self.model.view_as_variance_predictor() + multi_var_predictor.variance_predictor.set_backbone( + torch.jit.trace( + multi_var_predictor.variance_predictor.backbone, + ( + noise, + step, + condition + ) + ) + ) + + print(f'Scripting {self.multi_var_predictor_class_name}...') + multi_var_predictor = torch.jit.script( + multi_var_predictor, + example_inputs=[ + ( + condition.transpose(1, 2), + 1 # p_sample branch + ), + ( + condition.transpose(1, 2), + dummy_steps # p_sample_plms branch + ) + ] + ) + + print(f'Exporting {self.multi_var_predictor_class_name}...') + torch.onnx.export( + multi_var_predictor, + ( + condition.transpose(1, 2), + dummy_steps + ), + self.multi_var_predictor_cache_path, + input_names=[ + 'variance_cond', + 'steps' + ], + output_names=[ + 'xs_pred' + ], + dynamic_axes={ + 'variance_cond': { + 1: 'n_frames' + }, + 'xs_pred': { + (1 if len(self.model.variance_prediction_list) == 1 else 2): 'n_frames' + } + }, + opset_version=17, + **onnx_helper.TORCHSCRIPT_EXPORT_KWARGS + ) + + # Prepare inputs for postprocessor of the multi-variance predictor + xs_shape = (1, 15) \ + if len(self.model.variance_prediction_list) == 1 \ + else (1, len(self.model.variance_prediction_list), 15) + xs_pred = torch.randn(xs_shape, dtype=torch.float32, device=self.device) + torch.onnx.export( + self.model.view_as_variance_postprocess(), + ( + xs_pred + ), + self.variance_postprocess_cache_path, + input_names=[ + 'xs_pred' + ], + output_names=[ + f'{v_name}_pred' + for v_name in self.model.variance_prediction_list + ], + dynamic_axes={ + 'xs_pred': { + (1 if len(self.model.variance_prediction_list) == 1 else 2): 'n_frames' + }, + **{ + f'{v_name}_pred': { + 1: 'n_frames' + } + for v_name in self.model.variance_prediction_list + } + }, + opset_version=17, + **onnx_helper.TORCHSCRIPT_EXPORT_KWARGS + ) + + @torch.no_grad() + def _perform_spk_mix(self, spk_mix: Dict[str, float]): + spk_mix_ids = [] + spk_mix_values = [] + for name, value in spk_mix.items(): + spk_mix_ids.append(self.spk_map[name]) + assert value >= 0., f'Speaker mix checks failed.\n' \ + f'Proportion of speaker \'{name}\' is negative.' + spk_mix_values.append(value) + spk_mix_id_N = torch.LongTensor(spk_mix_ids).to(self.device)[None] # => [1, N] + spk_mix_value_N = torch.FloatTensor(spk_mix_values).to(self.device)[None] # => [1, N] + spk_mix_value_sum = spk_mix_value_N.sum() + assert spk_mix_value_sum > 0., 'Speaker mix checks failed.\n' \ + 'Proportions of speaker mix sum to zero.' + spk_mix_value_N /= spk_mix_value_sum # normalize + spk_mix_embed = torch.sum( + self.model.spk_embed(spk_mix_id_N) * spk_mix_value_N.unsqueeze(2), # => [1, N, H] + dim=1, keepdim=True + ) # => [1, 1, H] + return spk_mix_embed + + def _optimize_linguistic_graph(self, linguistic: onnx.ModelProto) -> onnx.ModelProto: + onnx_helper.model_override_io_shapes( + linguistic, + output_shapes={ + 'encoder_out': (1, 'n_tokens', hparams['hidden_size']) + } + ) + print(f'Running ONNX Simplifier on {self.fs2_class_name}...') + linguistic, check = onnxsim.simplify(linguistic, include_subgraph=True) + assert check, 'Simplified ONNX model could not be validated' + onnx_helper.model_reorder_io_list( + linguistic, 'input', + target_name='languages', insert_after_name='tokens' + ) + print(f'| optimize graph: {self.fs2_class_name}') + return linguistic + + def _optimize_dur_predictor_graph(self, dur_predictor: onnx.ModelProto) -> onnx.ModelProto: + onnx_helper.model_override_io_shapes( + dur_predictor, + output_shapes={ + 'ph_dur_pred': (1, 'n_tokens') + } + ) + print(f'Running ONNX Simplifier on {self.dur_predictor_class_name}...') + dur_predictor, check = onnxsim.simplify(dur_predictor, include_subgraph=True) + assert check, 'Simplified ONNX model could not be validated' + print(f'| optimize graph: {self.dur_predictor_class_name}') + return dur_predictor + + def _optimize_merge_pitch_predictor_graph( + self, pitch_pre: onnx.ModelProto, pitch_predictor: onnx.ModelProto, pitch_post: onnx.ModelProto + ) -> onnx.ModelProto: + onnx_helper.model_override_io_shapes( + pitch_pre, output_shapes={'pitch_cond': (1, 'n_frames', hparams['hidden_size'])} + ) + pitch_pre, check = onnxsim.simplify(pitch_pre, include_subgraph=True) + assert check, 'Simplified ONNX model could not be validated' + + onnx_helper.model_override_io_shapes( + pitch_predictor, output_shapes={'pitch_pred': (1, 'n_frames')} + ) + print(f'Running ONNX Simplifier #1 on {self.pitch_predictor_class_name}...') + pitch_predictor, check = onnxsim.simplify(pitch_predictor, include_subgraph=True) + assert check, 'Simplified ONNX model could not be validated' + onnx_helper.graph_fold_back_to_squeeze(pitch_predictor.graph) + onnx_helper.graph_extract_conditioner_projections( + graph=pitch_predictor.graph, op_type='Conv', + weight_pattern=r'pitch_predictor\..*\.conditioner_projection\.weight', + alias_prefix='/pitch_predictor/backbone/cache' + ) + onnx_helper.graph_remove_unused_values(pitch_predictor.graph) + print(f'Running ONNX Simplifier #2 on {self.pitch_predictor_class_name}...') + pitch_predictor, check = onnxsim.simplify(pitch_predictor, include_subgraph=True) + assert check, 'Simplified ONNX model could not be validated' + + onnx_helper.model_add_prefixes(pitch_pre, node_prefix='/pre', ignored_pattern=r'.*embed.*') + onnx_helper.model_add_prefixes(pitch_pre, dim_prefix='pre.', ignored_pattern='(n_tokens)|(n_notes)|(n_frames)') + onnx_helper.model_add_prefixes(pitch_post, node_prefix='/post', ignored_pattern=None) + onnx_helper.model_add_prefixes(pitch_post, dim_prefix='post.', ignored_pattern='n_frames') + pitch_pre_diffusion = onnx.compose.merge_models( + pitch_pre, pitch_predictor, io_map=[('pitch_cond', 'pitch_cond')], + prefix1='', prefix2='', doc_string='', + producer_name=pitch_pre.producer_name, producer_version=pitch_pre.producer_version, + domain=pitch_pre.domain, model_version=pitch_pre.model_version + ) + pitch_pre_diffusion.graph.name = pitch_pre.graph.name + pitch_predictor = onnx.compose.merge_models( + pitch_pre_diffusion, pitch_post, io_map=[ + ('x_pred', 'x_pred'), ('base_pitch', 'base_pitch') + ], prefix1='', prefix2='', doc_string='', + producer_name=pitch_pre.producer_name, producer_version=pitch_pre.producer_version, + domain=pitch_pre.domain, model_version=pitch_pre.model_version + ) + pitch_predictor.graph.name = pitch_pre.graph.name + + print(f'| optimize graph: {self.pitch_predictor_class_name}') + return pitch_predictor + + def _optimize_merge_variance_predictor_graph( + self, var_pre: onnx.ModelProto, var_diffusion: onnx.ModelProto, var_post: onnx.ModelProto + ): + onnx_helper.model_override_io_shapes( + var_pre, output_shapes={'variance_cond': (1, 'n_frames', hparams['hidden_size'])} + ) + var_pre, check = onnxsim.simplify(var_pre, include_subgraph=True) + assert check, 'Simplified ONNX model could not be validated' + + onnx_helper.model_override_io_shapes( + var_diffusion, output_shapes={ + 'xs_pred': (1, 'n_frames') + if len(self.model.variance_prediction_list) == 1 + else (1, len(self.model.variance_prediction_list), 'n_frames') + } + ) + print(f'Running ONNX Simplifier #1 on {self.multi_var_predictor_class_name}...') + var_diffusion, check = onnxsim.simplify(var_diffusion, include_subgraph=True) + assert check, 'Simplified ONNX model could not be validated' + onnx_helper.graph_fold_back_to_squeeze(var_diffusion.graph) + onnx_helper.graph_extract_conditioner_projections( + graph=var_diffusion.graph, op_type='Conv', + weight_pattern=r'variance_predictor\..*\.conditioner_projection\.weight', + alias_prefix='/variance_predictor/backbone/cache' + ) + onnx_helper.graph_remove_unused_values(var_diffusion.graph) + print(f'Running ONNX Simplifier #2 on {self.multi_var_predictor_class_name}...') + var_diffusion, check = onnxsim.simplify(var_diffusion, include_subgraph=True) + assert check, 'Simplified ONNX model could not be validated' + + var_post, check = onnxsim.simplify(var_post, include_subgraph=True) + assert check, 'Simplified ONNX model could not be validated' + + ignored_variance_names = '|'.join([f'({v_name})' for v_name in self.model.variance_prediction_list]) + onnx_helper.model_add_prefixes( + var_pre, node_prefix='/pre', value_info_prefix='/pre', initializer_prefix='/pre', + ignored_pattern=fr'.*((embed)|{ignored_variance_names}).*' + ) + onnx_helper.model_add_prefixes(var_pre, dim_prefix='pre.', ignored_pattern='(n_tokens)|(n_frames)') + onnx_helper.model_add_prefixes( + var_post, node_prefix='/post', value_info_prefix='/post', initializer_prefix='/post', + ignored_pattern=None + ) + onnx_helper.model_add_prefixes(var_post, dim_prefix='post.', ignored_pattern='n_frames') + + print(f'Merging {self.multi_var_predictor_class_name} subroutines...') + var_pre_diffusion = onnx.compose.merge_models( + var_pre, var_diffusion, io_map=[('variance_cond', 'variance_cond')], + prefix1='', prefix2='', doc_string='', + producer_name=var_pre.producer_name, producer_version=var_pre.producer_version, + domain=var_pre.domain, model_version=var_pre.model_version + ) + var_pre_diffusion.graph.name = var_pre.graph.name + var_predictor = onnx.compose.merge_models( + var_pre_diffusion, var_post, io_map=[('xs_pred', 'xs_pred')], + prefix1='', prefix2='', doc_string='', + producer_name=var_pre.producer_name, producer_version=var_pre.producer_version, + domain=var_pre.domain, model_version=var_pre.model_version + ) + var_predictor.graph.name = var_pre.graph.name + return var_predictor + + # noinspection PyMethodMayBeStatic + def _export_spk_embed(self, path: Path, spk_embed: torch.Tensor): + with open(path, 'wb') as f: + f.write(spk_embed.cpu().numpy().tobytes()) + print(f'| export spk embed => {path}') + + def _export_phonemes(self, path: Path): + ph_path = path / f'{self.model_name}.phonemes.json' + self.phoneme_dictionary.dump(ph_path) + print(f'| export phonemes => {ph_path}') + lang_path = path / f'{self.model_name}.languages.json' + with open(lang_path, 'w', encoding='utf8') as fw: + json.dump(self.lang_map, fw, ensure_ascii=False, indent=2) + print(f'| export languages => {lang_path}') diff --git a/deployment/modules/__init__.py b/deployment/modules/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/deployment/modules/diffusion.py b/deployment/modules/diffusion.py new file mode 100644 index 0000000..9c4198f --- /dev/null +++ b/deployment/modules/diffusion.py @@ -0,0 +1,220 @@ +from __future__ import annotations + +from typing import List, Tuple + +import torch +from torch import Tensor + +from modules.core import ( + GaussianDiffusion, PitchDiffusion, MultiVarianceDiffusion +) + + +def extract(a, t): + return a[t].reshape((1, 1, 1, 1)) + + +# noinspection PyMethodOverriding +class GaussianDiffusionONNX(GaussianDiffusion): + @property + def backbone(self): + return self.denoise_fn + + # We give up the setter for the property `backbone` because this will cause TorchScript to fail + # @backbone.setter + @torch.jit.unused + def set_backbone(self, value): + self.denoise_fn = value + + def q_sample(self, x_start, t, noise): + return ( + extract(self.sqrt_alphas_cumprod, t) * x_start + + extract(self.sqrt_one_minus_alphas_cumprod, t) * noise + ) + + def p_sample(self, x, t, cond): + x_pred = self.denoise_fn(x, t, cond) + x_recon = ( + extract(self.sqrt_recip_alphas_cumprod, t) * x - + extract(self.sqrt_recipm1_alphas_cumprod, t) * x_pred + ) + # This is previously inherited from original DiffSinger repository + # and disabled due to some loudness issues when speedup = 1. + # x_recon = torch.clamp(x_recon, min=-1., max=1.) + + model_mean = ( + extract(self.posterior_mean_coef1, t) * x_recon + + extract(self.posterior_mean_coef2, t) * x + ) + model_log_variance = extract(self.posterior_log_variance_clipped, t) + noise = torch.randn_like(x) + # no noise when t == 0 + nonzero_mask = ((t > 0).float()).reshape(1, 1, 1, 1) + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise + + def p_sample_ddim(self, x, t, interval: int, cond): + a_t = extract(self.alphas_cumprod, t) + t_prev = t - interval + a_prev = extract(self.alphas_cumprod, t_prev * (t_prev > 0)) + + noise_pred = self.denoise_fn(x, t, cond=cond) + x_prev = a_prev.sqrt() * ( + x / a_t.sqrt() + (((1 - a_prev) / a_prev).sqrt() - ((1 - a_t) / a_t).sqrt()) * noise_pred + ) + return x_prev + + def plms_get_x_pred(self, x, noise_t, t, t_prev): + a_t = extract(self.alphas_cumprod, t) + a_prev = extract(self.alphas_cumprod, t_prev) + a_t_sq, a_prev_sq = a_t.sqrt(), a_prev.sqrt() + + x_delta = (a_prev - a_t) * ((1 / (a_t_sq * (a_t_sq + a_prev_sq))) * x - 1 / ( + a_t_sq * (((1 - a_prev) * a_t).sqrt() + ((1 - a_t) * a_prev).sqrt())) * noise_t) + x_pred = x + x_delta + + return x_pred + + def p_sample_plms(self, x_prev, t, interval: int, cond, noise_list: List[Tensor], stage: int): + noise_pred = self.denoise_fn(x_prev, t, cond) + t_prev = t - interval + t_prev = t_prev * (t_prev > 0) + if stage == 0: + x_pred = self.plms_get_x_pred(x_prev, noise_pred, t, t_prev) + noise_pred_prev = self.denoise_fn(x_pred, t_prev, cond) + noise_pred_prime = (noise_pred + noise_pred_prev) / 2. + elif stage == 1: + noise_pred_prime = (3. * noise_pred - noise_list[-1]) / 2. + elif stage == 2: + noise_pred_prime = (23. * noise_pred - 16. * noise_list[-1] + 5. * noise_list[-2]) / 12. + else: + noise_pred_prime = (55. * noise_pred - 59. * noise_list[-1] + 37. + * noise_list[-2] - 9. * noise_list[-3]) / 24. + x_prev = self.plms_get_x_pred(x_prev, noise_pred_prime, t, t_prev) + return noise_pred, x_prev + + def norm_spec(self, x): + k = (self.spec_max - self.spec_min) / 2. + b = (self.spec_max + self.spec_min) / 2. + return (x - b) / k + + def denorm_spec(self, x): + k = (self.spec_max - self.spec_min) / 2. + b = (self.spec_max + self.spec_min) / 2. + return x * k + b + + def forward(self, condition, x_start=None, depth=None, steps: int = 10): + condition = condition.transpose(1, 2) # [1, T, H] => [1, H, T] + device = condition.device + n_frames = condition.shape[2] + + noise = torch.randn((1, self.num_feats, self.out_dims, n_frames), device=device) + if x_start is None: + speedup = max(1, self.timesteps // steps) + speedup = self.timestep_factors[torch.sum(self.timestep_factors <= speedup) - 1] + step_range = torch.arange(0, self.k_step, speedup, dtype=torch.long, device=device).flip(0)[:, None] + x = noise + else: + depth_int64 = min(torch.round(depth * self.timesteps).long(), self.k_step) + speedup = max(1, depth_int64 // steps) + depth_int64 = depth_int64 // speedup * speedup # make depth_int64 a multiple of speedup + step_range = torch.arange(0, depth_int64, speedup, dtype=torch.long, device=device).flip(0)[:, None] + x_start = self.norm_spec(x_start).transpose(-2, -1) + if self.num_feats == 1: + x_start = x_start[:, None, :, :] + if depth_int64 >= self.timesteps: + x = noise + elif depth_int64 > 0: + x = self.q_sample( + x_start, torch.full((1,), depth_int64 - 1, device=device, dtype=torch.long), noise + ) + else: + x = x_start + + if speedup > 1: + for t in step_range: + x = self.p_sample_ddim(x, t, interval=speedup, cond=condition) + # plms_noise_stage: int = 0 + # noise_list: List[Tensor] = [] + # for t in step_range: + # noise_pred, x = self.p_sample_plms( + # x, t, interval=speedup, cond=condition, + # noise_list=noise_list, stage=plms_noise_stage + # ) + # if plms_noise_stage == 0: + # noise_list = [noise_pred] + # plms_noise_stage = plms_noise_stage + 1 + # else: + # if plms_noise_stage >= 3: + # noise_list.pop(0) + # else: + # plms_noise_stage = plms_noise_stage + 1 + # noise_list.append(noise_pred) + else: + for t in step_range: + x = self.p_sample(x, t, cond=condition) + + if self.num_feats == 1: + x = x.squeeze(1).permute(0, 2, 1) # [B, 1, M, T] => [B, T, M] + else: + x = x.permute(0, 1, 3, 2) # [B, F, M, T] => [B, F, T, M] + x = self.denorm_spec(x) + return x + + +class PitchDiffusionONNX(GaussianDiffusionONNX, PitchDiffusion): + def __init__(self, vmin: float, vmax: float, + cmin: float, cmax: float, repeat_bins, + timesteps=1000, k_step=1000, + backbone_type=None, backbone_args=None, + betas=None): + self.vmin = vmin + self.vmax = vmax + self.cmin = cmin + self.cmax = cmax + super(PitchDiffusion, self).__init__( + vmin=vmin, vmax=vmax, repeat_bins=repeat_bins, + timesteps=timesteps, k_step=k_step, + backbone_type=backbone_type, backbone_args=backbone_args, + betas=betas + ) + + def clamp_spec(self, x): + return x.clamp(min=self.cmin, max=self.cmax) + + def denorm_spec(self, x): + d = (self.spec_max - self.spec_min) / 2. + m = (self.spec_max + self.spec_min) / 2. + x = x * d + m + x = x.mean(dim=-1) + return x + + +class MultiVarianceDiffusionONNX(GaussianDiffusionONNX, MultiVarianceDiffusion): + def __init__( + self, ranges: List[Tuple[float, float]], + clamps: List[Tuple[float | None, float | None] | None], + repeat_bins, timesteps=1000, k_step=1000, + backbone_type=None, backbone_args=None, + betas=None + ): + assert len(ranges) == len(clamps) + self.clamps = clamps + vmin = [r[0] for r in ranges] + vmax = [r[1] for r in ranges] + if len(vmin) == 1: + vmin = vmin[0] + if len(vmax) == 1: + vmax = vmax[0] + super(MultiVarianceDiffusion, self).__init__( + vmin=vmin, vmax=vmax, repeat_bins=repeat_bins, + timesteps=timesteps, k_step=k_step, + backbone_type=backbone_type, backbone_args=backbone_args, + betas=betas + ) + + def denorm_spec(self, x): + d = (self.spec_max - self.spec_min) / 2. + m = (self.spec_max + self.spec_min) / 2. + x = x * d + m + x = x.mean(dim=-1) + return x diff --git a/deployment/modules/fastspeech2.py b/deployment/modules/fastspeech2.py new file mode 100644 index 0000000..55f4b28 --- /dev/null +++ b/deployment/modules/fastspeech2.py @@ -0,0 +1,235 @@ +import copy + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +from modules.commons.common_layers import NormalInitEmbedding as Embedding +from modules.fastspeech.acoustic_encoder import FastSpeech2Acoustic +from modules.fastspeech.variance_encoder import FastSpeech2Variance +from utils.hparams import hparams +from utils.phoneme_utils import PAD_INDEX + +f0_bin = 256 +f0_max = 1100.0 +f0_min = 50.0 +f0_mel_min = 1127 * np.log(1 + f0_min / 700) +f0_mel_max = 1127 * np.log(1 + f0_max / 700) + + +def uniform_attention_pooling(spk_embed, durations): + _, T_mel, _ = spk_embed.shape + ph_starts = torch.cumsum(torch.cat([torch.zeros_like(durations[:, :1]), durations[:, :-1]], dim=1), dim=1) + ph_ends = ph_starts + durations + mel_indices = torch.arange(T_mel, device=spk_embed.device).view(1, 1, T_mel) + phoneme_to_mel_mask = (mel_indices >= ph_starts.unsqueeze(-1)) & (mel_indices < ph_ends.unsqueeze(-1)) + uniform_scores = phoneme_to_mel_mask.float() + sum_scores = uniform_scores.sum(dim=2, keepdim=True) + attn_weights = uniform_scores / (sum_scores + (sum_scores == 0).float()) # [B, T_ph, T_mel] + ph_spk_embed = torch.bmm(attn_weights, spk_embed) + + return ph_spk_embed + + +def f0_to_coarse(f0): + f0_mel = 1127 * (1 + f0 / 700).log() + a = (f0_bin - 2) / (f0_mel_max - f0_mel_min) + b = f0_mel_min * a - 1. + f0_mel = torch.where(f0_mel > 0, f0_mel * a - b, f0_mel) + torch.clip_(f0_mel, min=1., max=float(f0_bin - 1)) + f0_coarse = torch.round(f0_mel).long() + return f0_coarse + + +class LengthRegulator(nn.Module): + # noinspection PyMethodMayBeStatic + def forward(self, dur): + token_idx = torch.arange(1, dur.shape[1] + 1, device=dur.device)[None, :, None] + dur_cumsum = torch.cumsum(dur, dim=1) + dur_cumsum_prev = F.pad(dur_cumsum, (1, -1), mode='constant', value=0) + pos_idx = torch.arange(dur.sum(dim=1).max(), device=dur.device)[None, None] + token_mask = (pos_idx >= dur_cumsum_prev[:, :, None]) & (pos_idx < dur_cumsum[:, :, None]) + mel2ph = (token_idx * token_mask).sum(dim=1) + return mel2ph + + +class FastSpeech2AcousticONNX(FastSpeech2Acoustic): + def __init__(self, vocab_size, cross_lingual_token_idx=None): + super().__init__(vocab_size=vocab_size) + self.register_buffer( + 'cross_lingual_token_idx', + torch.LongTensor(cross_lingual_token_idx), + persistent=False + ) # [N,] + if len(cross_lingual_token_idx) == 0: + self.use_lang_id = False + + # for temporary compatibility; will be completely removed in the future + self.f0_embed_type = hparams.get('f0_embed_type', 'continuous') + if self.f0_embed_type == 'discrete': + self.pitch_embed = Embedding(300, hparams['hidden_size'], PAD_INDEX) + + self.lr = LengthRegulator() + if hparams['use_key_shift_embed']: + self.shift_min, self.shift_max = hparams['augmentation_args']['random_pitch_shifting']['range'] + if hparams['use_speed_embed']: + self.speed_min, self.speed_max = hparams['augmentation_args']['random_time_stretching']['range'] + + # noinspection PyMethodOverriding + def forward( + self, tokens, durations, + f0, variances: dict, + gender=None, velocity=None, + spk_embed=None, + languages=None + ): + txt_embed = self.txt_embed(tokens) + durations = durations * (tokens > 0) + mel2ph = self.lr(durations) + _mel2ph = mel2ph + f0 = f0 * (mel2ph > 0) + mel2ph = mel2ph[..., None].repeat((1, 1, hparams['hidden_size'])) + if self.use_variance_scaling: + dur_embed = self.dur_embed(torch.log(1 + durations.float())[:, :, None]) + else: + dur_embed = self.dur_embed(durations.float()[:, :, None]) + if self.use_lang_id: + lang_mask = torch.any( + tokens[..., None] == self.cross_lingual_token_idx[None, None], + dim=-1 + ) + lang_embed = self.lang_embed(languages * lang_mask) + extra_embed = dur_embed + lang_embed + else: + extra_embed = dur_embed + if hparams.get('use_mix_ln', False): + if hasattr(self, 'frozen_spk_embed'): + ph_spk_embed = self.frozen_spk_embed.repeat(1, tokens.shape[1], 1) + else: + ph_spk_embed = uniform_attention_pooling(spk_embed, durations) + else: + ph_spk_embed = None + encoded = self.encoder(txt_embed, extra_embed, tokens == PAD_INDEX, spk_embed=ph_spk_embed) + encoded = F.pad(encoded, (0, 0, 1, 0)) + condition = torch.gather(encoded, 1, mel2ph) + + if self.use_stretch_embed: + stretch = torch.round(1000 * self.sr(_mel2ph, durations)) + table = self.stretch_embed(torch.arange(0, 1001, device=stretch.device)) + stretch_embed = torch.index_select(table, 0, stretch.view(-1).long()).view_as(condition) + condition += stretch_embed + stretch_embed_rnn_out, _ = self.stretch_embed_rnn(condition) + condition += stretch_embed_rnn_out + + if self.f0_embed_type == 'discrete': + pitch = f0_to_coarse(f0) + pitch_embed = self.pitch_embed(pitch) + else: + f0_mel = (1 + f0 / 700).log() + pitch_embed = self.pitch_embed(f0_mel[:, :, None]) + condition += pitch_embed + + if self.use_variance_embeds: + variance_embeds = torch.stack([ + self.variance_embeds[v_name](variances[v_name][:, :, None] * self.variance_scaling_factor[v_name]) + for v_name in self.variance_embed_list + ], dim=-1).sum(-1) + condition += variance_embeds + + if hparams['use_key_shift_embed']: + if hasattr(self, 'frozen_key_shift'): + key_shift_embed = self.key_shift_embed(self.frozen_key_shift[:, None, None] * self.variance_scaling_factor['key_shift']) + else: + gender = torch.clip(gender, min=-1., max=1.) + gender_mask = (gender < 0.).float() + key_shift = gender * ((1. - gender_mask) * self.shift_max + gender_mask * abs(self.shift_min)) + key_shift_embed = self.key_shift_embed(key_shift[:, :, None] * self.variance_scaling_factor['key_shift']) + condition += key_shift_embed + + if hparams['use_speed_embed']: + if velocity is not None: + velocity = torch.clip(velocity, min=self.speed_min, max=self.speed_max) + speed_embed = self.speed_embed(velocity[:, :, None] * self.variance_scaling_factor['speed']) + else: + speed_embed = self.speed_embed(torch.FloatTensor([1.]).to(condition.device)[:, None, None] * self.variance_scaling_factor['speed']) + condition += speed_embed + + if hparams['use_spk_id']: + if hasattr(self, 'frozen_spk_embed'): + condition += self.frozen_spk_embed + else: + condition += spk_embed + return condition + + +class FastSpeech2VarianceONNX(FastSpeech2Variance): + def __init__(self, vocab_size, cross_lingual_token_idx=None): + super().__init__(vocab_size=vocab_size) + self.register_buffer( + 'cross_lingual_token_idx', + torch.LongTensor(cross_lingual_token_idx), + persistent=False + ) + if len(cross_lingual_token_idx) == 0: + self.use_lang_id = False + self.lr = LengthRegulator() + + def forward_encoder_word(self, tokens, word_div, word_dur, languages=None): + txt_embed = self.txt_embed(tokens) + ph2word = self.lr(word_div) + onset = ph2word > F.pad(ph2word, [1, -1]) + onset_embed = self.onset_embed(onset.long()) + ph_word_dur = torch.gather(F.pad(word_dur, [1, 0]), 1, ph2word) + word_dur_embed = self.word_dur_embed(ph_word_dur.float()[:, :, None]) + extra_embed = onset_embed + word_dur_embed + if self.use_lang_id: + lang_mask = torch.any( + tokens[..., None] == self.cross_lingual_token_idx[None, None], + dim=-1 + ) + lang_embed = self.lang_embed(languages * lang_mask) + extra_embed += lang_embed + x_masks = tokens == PAD_INDEX + return self.encoder(txt_embed, extra_embed, x_masks), x_masks + + def forward_encoder_phoneme(self, tokens, ph_dur, languages=None): + txt_embed = self.txt_embed(tokens) + if self.use_variance_scaling: + ph_dur_embed = self.ph_dur_embed(torch.log(1 + ph_dur.float())[:, :, None]) + else: + ph_dur_embed = self.ph_dur_embed(ph_dur.float()[:, :, None]) + if self.use_lang_id: + lang_mask = torch.any( + tokens[..., None] == self.cross_lingual_token_idx[None, None], + dim=-1 + ) + lang_embed = self.lang_embed(languages * lang_mask) + extra_embed = ph_dur_embed + lang_embed + else: + extra_embed = ph_dur_embed + x_masks = tokens == PAD_INDEX + return self.encoder(txt_embed, extra_embed, x_masks), x_masks + + def forward_dur_predictor(self, encoder_out, x_masks, ph_midi, spk_embed=None): + midi_embed = self.midi_embed(ph_midi) + dur_cond = encoder_out + midi_embed + if hparams['use_spk_id'] and spk_embed is not None: + dur_cond += spk_embed + ph_dur = self.dur_predictor(dur_cond, x_masks=x_masks) + return ph_dur + + def view_as_encoder(self): + model = copy.deepcopy(self) + if self.predict_dur: + del model.dur_predictor + model.forward = model.forward_encoder_word + else: + model.forward = model.forward_encoder_phoneme + return model + + def view_as_dur_predictor(self): + model = copy.deepcopy(self) + del model.encoder + model.forward = model.forward_dur_predictor + return model diff --git a/deployment/modules/nsf_hifigan.py b/deployment/modules/nsf_hifigan.py new file mode 100644 index 0000000..4e2f296 --- /dev/null +++ b/deployment/modules/nsf_hifigan.py @@ -0,0 +1,16 @@ +import torch + +from modules.nsf_hifigan.env import AttrDict +from modules.nsf_hifigan.models import Generator + + +# noinspection SpellCheckingInspection +class NSFHiFiGANONNX(torch.nn.Module): + def __init__(self, attrs: dict): + super().__init__() + self.generator = Generator(AttrDict(attrs)) + + def forward(self, mel: torch.Tensor, f0: torch.Tensor): + mel = mel.transpose(1, 2) + wav = self.generator(mel, f0) + return wav.squeeze(1) diff --git a/deployment/modules/rectified_flow.py b/deployment/modules/rectified_flow.py new file mode 100644 index 0000000..230ce54 --- /dev/null +++ b/deployment/modules/rectified_flow.py @@ -0,0 +1,123 @@ +from __future__ import annotations + +from typing import List, Tuple + +import torch + +from modules.core import ( + RectifiedFlow, PitchRectifiedFlow, MultiVarianceRectifiedFlow +) + + +class RectifiedFlowONNX(RectifiedFlow): + @property + def backbone(self): + return self.velocity_fn + + # We give up the setter for the property `backbone` because this will cause TorchScript to fail + # @backbone.setter + @torch.jit.unused + def set_backbone(self, value): + self.velocity_fn = value + + def sample_euler(self, x, t, dt: float, cond): + x += self.velocity_fn(x, t * self.time_scale_factor, cond) * dt + return x + + def norm_spec(self, x): + k = (self.spec_max - self.spec_min) / 2. + b = (self.spec_max + self.spec_min) / 2. + return (x - b) / k + + def denorm_spec(self, x): + k = (self.spec_max - self.spec_min) / 2. + b = (self.spec_max + self.spec_min) / 2. + return x * k + b + + def forward(self, condition, x_end=None, depth=None, steps: int = 10): + condition = condition.transpose(1, 2) # [1, T, H] => [1, H, T] + device = condition.device + n_frames = condition.shape[2] + noise = torch.randn((1, self.num_feats, self.out_dims, n_frames), device=device) + if x_end is None: + t_start = 0. + x = noise + else: + t_start = torch.max(1 - depth, torch.tensor(self.t_start, dtype=torch.float32, device=device)) + x_end = self.norm_spec(x_end).transpose(-2, -1) + if self.num_feats == 1: + x_end = x_end[:, None, :, :] + if t_start <= 0.: + x = noise + elif t_start >= 1.: + x = x_end + else: + x = t_start * x_end + (1 - t_start) * noise + + t_width = 1. - t_start + if t_width >= 0.: + dt = t_width / max(1, steps) + for t in torch.arange(steps, dtype=torch.long, device=device)[:, None].float() * dt + t_start: + x = self.sample_euler(x, t, dt, condition) + + if self.num_feats == 1: + x = x.squeeze(1).permute(0, 2, 1) # [B, 1, M, T] => [B, T, M] + else: + x = x.permute(0, 1, 3, 2) # [B, F, M, T] => [B, F, T, M] + x = self.denorm_spec(x) + return x + + +class PitchRectifiedFlowONNX(RectifiedFlowONNX, PitchRectifiedFlow): + def __init__(self, vmin: float, vmax: float, + cmin: float, cmax: float, repeat_bins, + time_scale_factor=1000, + backbone_type=None, backbone_args=None): + self.vmin = vmin + self.vmax = vmax + self.cmin = cmin + self.cmax = cmax + super(PitchRectifiedFlow, self).__init__( + vmin=vmin, vmax=vmax, repeat_bins=repeat_bins, + time_scale_factor=time_scale_factor, + backbone_type=backbone_type, backbone_args=backbone_args + ) + + def clamp_spec(self, x): + return x.clamp(min=self.cmin, max=self.cmax) + + def denorm_spec(self, x): + d = (self.spec_max - self.spec_min) / 2. + m = (self.spec_max + self.spec_min) / 2. + x = x * d + m + x = x.mean(dim=-1) + return x + + +class MultiVarianceRectifiedFlowONNX(RectifiedFlowONNX, MultiVarianceRectifiedFlow): + def __init__( + self, ranges: List[Tuple[float, float]], + clamps: List[Tuple[float | None, float | None] | None], + repeat_bins, time_scale_factor=1000, + backbone_type=None, backbone_args=None + ): + assert len(ranges) == len(clamps) + self.clamps = clamps + vmin = [r[0] for r in ranges] + vmax = [r[1] for r in ranges] + if len(vmin) == 1: + vmin = vmin[0] + if len(vmax) == 1: + vmax = vmax[0] + super(MultiVarianceRectifiedFlow, self).__init__( + vmin=vmin, vmax=vmax, repeat_bins=repeat_bins, + time_scale_factor=time_scale_factor, + backbone_type=backbone_type, backbone_args=backbone_args + ) + + def denorm_spec(self, x): + d = (self.spec_max - self.spec_min) / 2. + m = (self.spec_max + self.spec_min) / 2. + x = x * d + m + x = x.mean(dim=-1) + return x diff --git a/deployment/modules/toplevel.py b/deployment/modules/toplevel.py new file mode 100644 index 0000000..3043168 --- /dev/null +++ b/deployment/modules/toplevel.py @@ -0,0 +1,413 @@ +import copy + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import Tensor + +from deployment.modules.diffusion import ( + GaussianDiffusionONNX, PitchDiffusionONNX, MultiVarianceDiffusionONNX +) +from deployment.modules.rectified_flow import ( + RectifiedFlowONNX, PitchRectifiedFlowONNX, MultiVarianceRectifiedFlowONNX +) +from deployment.modules.fastspeech2 import FastSpeech2AcousticONNX, FastSpeech2VarianceONNX +from modules.toplevel import DiffSingerAcoustic, DiffSingerVariance +from utils.hparams import hparams + + +class DiffSingerAcousticONNX(DiffSingerAcoustic): + def __init__(self, vocab_size, out_dims, cross_lingual_token_idx=None): + super().__init__(vocab_size, out_dims) + del self.fs2 + del self.diffusion + self.fs2 = FastSpeech2AcousticONNX( + vocab_size=vocab_size, + cross_lingual_token_idx=cross_lingual_token_idx + ) + if self.diffusion_type == 'ddpm': + self.diffusion = GaussianDiffusionONNX( + out_dims=out_dims, + num_feats=1, + timesteps=hparams['timesteps'], + k_step=hparams['K_step'], + backbone_type=self.backbone_type, + backbone_args=self.backbone_args, + spec_min=hparams['spec_min'], + spec_max=hparams['spec_max'] + ) + elif self.diffusion_type == 'reflow': + self.diffusion = RectifiedFlowONNX( + out_dims=out_dims, + num_feats=1, + t_start=hparams['T_start'], + time_scale_factor=hparams['time_scale_factor'], + backbone_type=self.backbone_type, + backbone_args=self.backbone_args, + spec_min=hparams['spec_min'], + spec_max=hparams['spec_max'] + ) + else: + raise ValueError(f"Invalid diffusion type: {self.diffusion_type}") + self.mel_base = hparams.get('mel_base', '10') + + def ensure_mel_base(self, mel): + if self.mel_base != 'e': + # log10 mel to log mel + mel = mel * 2.30259 + return mel + + def forward_fs2_aux( + self, + tokens: Tensor, + durations: Tensor, + f0: Tensor, + variances: dict, + gender: Tensor = None, + velocity: Tensor = None, + spk_embed: Tensor = None, + languages: Tensor = None + ): + condition = self.fs2( + tokens, durations, f0, variances=variances, + gender=gender, velocity=velocity, spk_embed=spk_embed, + languages=languages + ) + if self.use_shallow_diffusion: + aux_mel_pred = self.aux_decoder(condition, infer=True) + return condition, aux_mel_pred + else: + return condition + + def forward_shallow_diffusion( + self, condition: Tensor, x_start: Tensor, + depth, steps: int + ) -> Tensor: + mel_pred = self.diffusion(condition, x_start=x_start, depth=depth, steps=steps) + return self.ensure_mel_base(mel_pred) + + def forward_diffusion(self, condition: Tensor, steps: int): + mel_pred = self.diffusion(condition, steps=steps) + return self.ensure_mel_base(mel_pred) + + def forward_shallow_reflow( + self, condition: Tensor, x_end: Tensor, + depth, steps: int + ): + mel_pred = self.diffusion(condition, x_end=x_end, depth=depth, steps=steps) + return self.ensure_mel_base(mel_pred) + + def forward_reflow(self, condition: Tensor, steps: int): + mel_pred = self.diffusion(condition, steps=steps) + return self.ensure_mel_base(mel_pred) + + def view_as_fs2_aux(self) -> nn.Module: + model = copy.deepcopy(self) + del model.diffusion + model.forward = model.forward_fs2_aux + return model + + def view_as_diffusion(self) -> nn.Module: + model = copy.deepcopy(self) + del model.fs2 + if self.use_shallow_diffusion: + del model.aux_decoder + model.forward = model.forward_shallow_diffusion + else: + model.forward = model.forward_diffusion + return model + + def view_as_reflow(self) -> nn.Module: + model = copy.deepcopy(self) + del model.fs2 + if self.use_shallow_diffusion: + del model.aux_decoder + model.forward = model.forward_shallow_reflow + else: + model.forward = model.forward_reflow + return model + + +class DiffSingerVarianceONNX(DiffSingerVariance): + def __init__(self, vocab_size, cross_lingual_token_idx=None): + super().__init__(vocab_size=vocab_size) + del self.fs2 + self.fs2 = FastSpeech2VarianceONNX( + vocab_size=vocab_size, + cross_lingual_token_idx=cross_lingual_token_idx + ) + self.hidden_size = hparams['hidden_size'] + if self.predict_pitch: + del self.pitch_predictor + self.smooth: nn.Conv1d = None + pitch_hparams = hparams['pitch_prediction_args'] + if self.diffusion_type == 'ddpm': + self.pitch_predictor = PitchDiffusionONNX( + vmin=pitch_hparams['pitd_norm_min'], + vmax=pitch_hparams['pitd_norm_max'], + cmin=pitch_hparams['pitd_clip_min'], + cmax=pitch_hparams['pitd_clip_max'], + repeat_bins=pitch_hparams['repeat_bins'], + timesteps=hparams['timesteps'], + k_step=hparams['K_step'], + backbone_type=self.pitch_backbone_type, + backbone_args=self.pitch_backbone_args + ) + elif self.diffusion_type == 'reflow': + self.pitch_predictor = PitchRectifiedFlowONNX( + vmin=pitch_hparams['pitd_norm_min'], + vmax=pitch_hparams['pitd_norm_max'], + cmin=pitch_hparams['pitd_clip_min'], + cmax=pitch_hparams['pitd_clip_max'], + repeat_bins=pitch_hparams['repeat_bins'], + time_scale_factor=hparams['time_scale_factor'], + backbone_type=self.pitch_backbone_type, + backbone_args=self.pitch_backbone_args + ) + else: + raise ValueError(f"Invalid diffusion type: {self.diffusion_type}") + if self.predict_variances: + del self.variance_predictor + if self.diffusion_type == 'ddpm': + self.variance_predictor = self.build_adaptor(cls=MultiVarianceDiffusionONNX) + elif self.diffusion_type == 'reflow': + self.variance_predictor = self.build_adaptor(cls=MultiVarianceRectifiedFlowONNX) + else: + raise NotImplementedError(self.diffusion_type) + + def build_smooth_op(self, device): + smooth_kernel_size = round(hparams['midi_smooth_width'] * hparams['audio_sample_rate'] / hparams['hop_size']) + smooth = nn.Conv1d( + in_channels=1, + out_channels=1, + kernel_size=smooth_kernel_size, + bias=False, + padding='same', + padding_mode='replicate' + ).eval() + smooth_kernel = torch.sin(torch.from_numpy( + np.linspace(0, 1, smooth_kernel_size).astype(np.float32) * np.pi + )) + smooth_kernel /= smooth_kernel.sum() + smooth.weight.data = smooth_kernel[None, None] + self.smooth = smooth.to(device) + + def embed_frozen_spk(self, encoder_out): + if hparams['use_spk_id'] and hasattr(self, 'frozen_spk_embed'): + encoder_out += self.frozen_spk_embed + return encoder_out + + def forward_linguistic_encoder_word(self, tokens, word_div, word_dur, languages=None): + encoder_out, x_masks = self.fs2.forward_encoder_word(tokens, word_div, word_dur, languages=languages) + encoder_out = self.embed_frozen_spk(encoder_out) + return encoder_out, x_masks + + def forward_linguistic_encoder_phoneme(self, tokens, ph_dur, languages=None): + encoder_out, x_masks = self.fs2.forward_encoder_phoneme(tokens, ph_dur, languages=languages) + encoder_out = self.embed_frozen_spk(encoder_out) + return encoder_out, x_masks + + def forward_dur_predictor(self, encoder_out, x_masks, ph_midi, spk_embed=None): + return self.fs2.forward_dur_predictor(encoder_out, x_masks, ph_midi, spk_embed=spk_embed) + + def forward_mel2x_gather(self, x_src, x_dur, x_dim=None, check_stretch_embed=False): + mel2x = self.lr(x_dur) + _mel2x = mel2x + if x_dim is not None: + x_src = F.pad(x_src, [0, 0, 1, 0]) + mel2x = mel2x[..., None].repeat([1, 1, x_dim]) + else: + x_src = F.pad(x_src, [1, 0]) + x_cond = torch.gather(x_src, 1, mel2x) + if self.use_stretch_embed and check_stretch_embed: + stretch = torch.round(1000 * self.sr(_mel2x, x_dur)) + table = self.stretch_embed(torch.arange(0, 1001, device=stretch.device)) + stretch_embed = torch.index_select(table, 0, stretch.view(-1).long()).view_as(x_cond) + x_cond += stretch_embed + stretch_embed_rnn_out, _ = self.stretch_embed_rnn(x_cond) + x_cond += stretch_embed_rnn_out + return x_cond + + def forward_pitch_preprocess( + self, encoder_out, ph_dur, + note_midi=None, note_rest=None, note_dur=None, note_glide=None, + pitch=None, expr=None, retake=None, spk_embed=None + ): + condition = self.forward_mel2x_gather(encoder_out, ph_dur, x_dim=self.hidden_size, check_stretch_embed=True) + if self.use_melody_encoder: + if self.melody_encoder.use_glide_embed and note_glide is None: + note_glide = torch.LongTensor([[0]]).to(encoder_out.device) + melody_encoder_out = self.melody_encoder( + note_midi, note_rest, note_dur, + glide=note_glide + ) + melody_encoder_out = self.forward_mel2x_gather(melody_encoder_out, note_dur, x_dim=self.hidden_size) + condition += melody_encoder_out + if expr is None: + retake_embed = self.pitch_retake_embed(retake.long()) + else: + retake_true_embed = self.pitch_retake_embed( + torch.ones(1, 1, dtype=torch.long, device=encoder_out.device) + ) # [B=1, T=1] => [B=1, T=1, H] + retake_false_embed = self.pitch_retake_embed( + torch.zeros(1, 1, dtype=torch.long, device=encoder_out.device) + ) # [B=1, T=1] => [B=1, T=1, H] + expr = (expr * retake)[:, :, None] # [B, T, 1] + retake_embed = expr * retake_true_embed + (1. - expr) * retake_false_embed + pitch_cond = condition + retake_embed + frame_midi_pitch = self.forward_mel2x_gather(note_midi, note_dur, x_dim=None) + base_pitch = self.smooth(frame_midi_pitch) + if self.use_melody_encoder: + delta_pitch = (pitch - base_pitch) * ~retake + if self.use_variance_scaling: + pitch_cond += self.delta_pitch_embed(delta_pitch[:, :, None] / 12) + else: + pitch_cond += self.delta_pitch_embed(delta_pitch[:, :, None]) + else: + base_pitch = base_pitch * retake + pitch * ~retake + if self.use_variance_scaling: + pitch_cond += self.base_pitch_embed(base_pitch[:, :, None] / 128) + else: + pitch_cond += self.base_pitch_embed(base_pitch[:, :, None]) + if hparams['use_spk_id'] and spk_embed is not None: + pitch_cond += spk_embed + return pitch_cond, base_pitch + + def forward_pitch_reflow( + self, pitch_cond, steps: int = 10 + ): + x_pred = self.pitch_predictor(pitch_cond, steps=steps) + return x_pred + + def forward_pitch_postprocess(self, x_pred, base_pitch): + pitch_pred = self.pitch_predictor.clamp_spec(x_pred) + base_pitch + return pitch_pred + + def forward_variance_preprocess( + self, encoder_out, ph_dur, pitch, + variances: dict = None, retake=None, spk_embed=None + ): + condition = self.forward_mel2x_gather(encoder_out, ph_dur, x_dim=self.hidden_size, check_stretch_embed=True) + if self.use_variance_scaling: + variance_cond = condition + self.pitch_embed(pitch[:, :, None] / 12) + else: + variance_cond = condition + self.pitch_embed(pitch[:, :, None]) + non_retake_masks = [ + v_retake.float() # [B, T, 1] + for v_retake in (~retake).split(1, dim=2) + ] + variance_embeds = [ + self.variance_embeds[v_name](variances[v_name][:, :, None] * self.variance_retake_scaling[v_name]) * v_masks + for v_name, v_masks in zip(self.variance_prediction_list, non_retake_masks) + ] + variance_cond += torch.stack(variance_embeds, dim=-1).sum(-1) + if hparams['use_spk_id'] and spk_embed is not None: + variance_cond += spk_embed + return variance_cond + + def forward_variance_reflow(self, variance_cond, steps: int = 10): + xs_pred = self.variance_predictor(variance_cond, steps=steps) + return xs_pred + + def forward_variance_postprocess(self, xs_pred): + if self.variance_predictor.num_feats == 1: + xs_pred = [xs_pred] + else: + xs_pred = xs_pred.unbind(dim=1) + variance_pred = self.variance_predictor.clamp_spec(xs_pred) + return tuple(variance_pred) + + def view_as_linguistic_encoder(self): + model = copy.deepcopy(self) + if self.predict_pitch: + del model.pitch_predictor + if self.use_melody_encoder: + del model.melody_encoder + if self.predict_variances: + del model.variance_predictor + model.fs2 = model.fs2.view_as_encoder() + if self.predict_dur: + model.forward = model.forward_linguistic_encoder_word + else: + model.forward = model.forward_linguistic_encoder_phoneme + return model + + def view_as_dur_predictor(self): + assert self.predict_dur + model = copy.deepcopy(self) + if self.predict_pitch: + del model.pitch_predictor + if self.use_melody_encoder: + del model.melody_encoder + if self.predict_variances: + del model.variance_predictor + model.fs2 = model.fs2.view_as_dur_predictor() + model.forward = model.forward_dur_predictor + return model + + def view_as_pitch_preprocess(self): + model = copy.deepcopy(self) + del model.fs2 + if self.predict_pitch: + del model.pitch_predictor + if self.predict_variances: + del model.variance_predictor + model.forward = model.forward_pitch_preprocess + return model + + def view_as_pitch_predictor(self): + assert self.predict_pitch + model = copy.deepcopy(self) + del model.fs2 + del model.lr + if self.use_melody_encoder: + del model.melody_encoder + if self.predict_variances: + del model.variance_predictor + model.forward = model.forward_pitch_reflow + return model + + def view_as_pitch_postprocess(self): + model = copy.deepcopy(self) + del model.fs2 + if self.use_melody_encoder: + del model.melody_encoder + if self.predict_variances: + del model.variance_predictor + model.forward = model.forward_pitch_postprocess + return model + + def view_as_variance_preprocess(self): + model = copy.deepcopy(self) + del model.fs2 + if self.predict_pitch: + del model.pitch_predictor + if self.use_melody_encoder: + del model.melody_encoder + if self.predict_variances: + del model.variance_predictor + model.forward = model.forward_variance_preprocess + return model + + def view_as_variance_predictor(self): + assert self.predict_variances + model = copy.deepcopy(self) + del model.fs2 + del model.lr + if self.predict_pitch: + del model.pitch_predictor + if self.use_melody_encoder: + del model.melody_encoder + model.forward = model.forward_variance_reflow + return model + + def view_as_variance_postprocess(self): + model = copy.deepcopy(self) + del model.fs2 + if self.predict_pitch: + del model.pitch_predictor + if self.use_melody_encoder: + del model.melody_encoder + model.forward = model.forward_variance_postprocess + return model diff --git a/dictionaries/.gitignore b/dictionaries/.gitignore new file mode 100644 index 0000000..307afed --- /dev/null +++ b/dictionaries/.gitignore @@ -0,0 +1,3 @@ +*.py +*.txt +!opencpop* diff --git a/dictionaries/opencpop-extension.txt b/dictionaries/opencpop-extension.txt new file mode 100644 index 0000000..32785f7 --- /dev/null +++ b/dictionaries/opencpop-extension.txt @@ -0,0 +1,601 @@ +a a +ai ai +an an +ang ang +ao ao +ba b a +bai b ai +ban b an +bang b ang +bao b ao +be b e +bei b ei +ben b en +beng b eng +ber b er +bi b i +bia b ia +bian b ian +biang b iang +biao b iao +bie b ie +bin b in +bing b ing +biong b iong +biu b iu +bo b o +bong b ong +bou b ou +bu b u +bua b ua +buai b uai +buan b uan +buang b uang +bui b ui +bun b un +bv b v +bve b ve +ca c a +cai c ai +can c an +cang c ang +cao c ao +ce c e +cei c ei +cen c en +ceng c eng +cer c er +cha ch a +chai ch ai +chan ch an +chang ch ang +chao ch ao +che ch e +chei ch ei +chen ch en +cheng ch eng +cher ch er +chi ch ir +chong ch ong +chou ch ou +chu ch u +chua ch ua +chuai ch uai +chuan ch uan +chuang ch uang +chui ch ui +chun ch un +chuo ch uo +chv ch v +chyi ch i +ci c i0 +cong c ong +cou c ou +cu c u +cua c ua +cuai c uai +cuan c uan +cuang c uang +cui c ui +cun c un +cuo c uo +cv c v +cyi c i +da d a +dai d ai +dan d an +dang d ang +dao d ao +de d e +dei d ei +den d en +deng d eng +der d er +di d i +dia d ia +dian d ian +diang d iang +diao d iao +die d ie +din d in +ding d ing +diong d iong +diu d iu +dong d ong +dou d ou +du d u +dua d ua +duai d uai +duan d uan +duang d uang +dui d ui +dun d un +duo d uo +dv d v +dve d ve +e e +ei ei +en en +eng eng +er er +fa f a +fai f ai +fan f an +fang f ang +fao f ao +fe f e +fei f ei +fen f en +feng f eng +fer f er +fi f i +fia f ia +fian f ian +fiang f iang +fiao f iao +fie f ie +fin f in +fing f ing +fiong f iong +fiu f iu +fo f o +fong f ong +fou f ou +fu f u +fua f ua +fuai f uai +fuan f uan +fuang f uang +fui f ui +fun f un +fv f v +fve f ve +ga g a +gai g ai +gan g an +gang g ang +gao g ao +ge g e +gei g ei +gen g en +geng g eng +ger g er +gi g i +gia g ia +gian g ian +giang g iang +giao g iao +gie g ie +gin g in +ging g ing +giong g iong +giu g iu +gong g ong +gou g ou +gu g u +gua g ua +guai g uai +guan g uan +guang g uang +gui g ui +gun g un +guo g uo +gv g v +gve g ve +ha h a +hai h ai +han h an +hang h ang +hao h ao +he h e +hei h ei +hen h en +heng h eng +her h er +hi h i +hia h ia +hian h ian +hiang h iang +hiao h iao +hie h ie +hin h in +hing h ing +hiong h iong +hiu h iu +hong h ong +hou h ou +hu h u +hua h ua +huai h uai +huan h uan +huang h uang +hui h ui +hun h un +huo h uo +hv h v +hve h ve +ji j i +jia j ia +jian j ian +jiang j iang +jiao j iao +jie j ie +jin j in +jing j ing +jiong j iong +jiu j iu +ju j v +juan j van +jue j ve +jun j vn +ka k a +kai k ai +kan k an +kang k ang +kao k ao +ke k e +kei k ei +ken k en +keng k eng +ker k er +ki k i +kia k ia +kian k ian +kiang k iang +kiao k iao +kie k ie +kin k in +king k ing +kiong k iong +kiu k iu +kong k ong +kou k ou +ku k u +kua k ua +kuai k uai +kuan k uan +kuang k uang +kui k ui +kun k un +kuo k uo +kv k v +kve k ve +la l a +lai l ai +lan l an +lang l ang +lao l ao +le l e +lei l ei +len l en +leng l eng +ler l er +li l i +lia l ia +lian l ian +liang l iang +liao l iao +lie l ie +lin l in +ling l ing +liong l iong +liu l iu +lo l o +long l ong +lou l ou +lu l u +lua l ua +luai l uai +luan l uan +luang l uang +lui l ui +lun l un +luo l uo +lv l v +lve l ve +ma m a +mai m ai +man m an +mang m ang +mao m ao +me m e +mei m ei +men m en +meng m eng +mer m er +mi m i +mia m ia +mian m ian +miang m iang +miao m iao +mie m ie +min m in +ming m ing +miong m iong +miu m iu +mo m o +mong m ong +mou m ou +mu m u +mua m ua +muai m uai +muan m uan +muang m uang +mui m ui +mun m un +mv m v +mve m ve +na n a +nai n ai +nan n an +nang n ang +nao n ao +ne n e +nei n ei +nen n en +neng n eng +ner n er +ni n i +nia n ia +nian n ian +niang n iang +niao n iao +nie n ie +nin n in +ning n ing +niong n iong +niu n iu +nong n ong +nou n ou +nu n u +nua n ua +nuai n uai +nuan n uan +nuang n uang +nui n ui +nun n un +nuo n uo +nv n v +nve n ve +o o +ong ong +ou ou +pa p a +pai p ai +pan p an +pang p ang +pao p ao +pe p e +pei p ei +pen p en +peng p eng +per p er +pi p i +pia p ia +pian p ian +piang p iang +piao p iao +pie p ie +pin p in +ping p ing +piong p iong +piu p iu +po p o +pong p ong +pou p ou +pu p u +pua p ua +puai p uai +puan p uan +puang p uang +pui p ui +pun p un +pv p v +pve p ve +qi q i +qia q ia +qian q ian +qiang q iang +qiao q iao +qie q ie +qin q in +qing q ing +qiong q iong +qiu q iu +qu q v +quan q van +que q ve +qun q vn +ra r a +rai r ai +ran r an +rang r ang +rao r ao +re r e +rei r ei +ren r en +reng r eng +rer r er +ri r ir +rong r ong +rou r ou +ru r u +rua r ua +ruai r uai +ruan r uan +ruang r uang +rui r ui +run r un +ruo r uo +rv r v +ryi r i +sa s a +sai s ai +san s an +sang s ang +sao s ao +se s e +sei s ei +sen s en +seng s eng +ser s er +sha sh a +shai sh ai +shan sh an +shang sh ang +shao sh ao +she sh e +shei sh ei +shen sh en +sheng sh eng +sher sh er +shi sh ir +shong sh ong +shou sh ou +shu sh u +shua sh ua +shuai sh uai +shuan sh uan +shuang sh uang +shui sh ui +shun sh un +shuo sh uo +shv sh v +shyi sh i +si s i0 +song s ong +sou s ou +su s u +sua s ua +suai s uai +suan s uan +suang s uang +sui s ui +sun s un +suo s uo +sv s v +syi s i +ta t a +tai t ai +tan t an +tang t ang +tao t ao +te t e +tei t ei +ten t en +teng t eng +ter t er +ti t i +tia t ia +tian t ian +tiang t iang +tiao t iao +tie t ie +tin t in +ting t ing +tiong t iong +tong t ong +tou t ou +tu t u +tua t ua +tuai t uai +tuan t uan +tuang t uang +tui t ui +tun t un +tuo t uo +tv t v +tve t ve +wa w a +wai w ai +wan w an +wang w ang +wao w ao +we w e +wei w ei +wen w en +weng w eng +wer w er +wi w i +wo w o +wong w ong +wou w ou +wu w u +xi x i +xia x ia +xian x ian +xiang x iang +xiao x iao +xie x ie +xin x in +xing x ing +xiong x iong +xiu x iu +xu x v +xuan x van +xue x ve +xun x vn +ya y a +yai y ai +yan y En +yang y ang +yao y ao +ye y E +yei y ei +yi y i +yin y in +ying y ing +yo y o +yong y ong +you y ou +yu y v +yuan y van +yue y ve +yun y vn +ywu y u +za z a +zai z ai +zan z an +zang z ang +zao z ao +ze z e +zei z ei +zen z en +zeng z eng +zer z er +zha zh a +zhai zh ai +zhan zh an +zhang zh ang +zhao zh ao +zhe zh e +zhei zh ei +zhen zh en +zheng zh eng +zher zh er +zhi zh ir +zhong zh ong +zhou zh ou +zhu zh u +zhua zh ua +zhuai zh uai +zhuan zh uan +zhuang zh uang +zhui zh ui +zhun zh un +zhuo zh uo +zhv zh v +zhyi zh i +zi z i0 +zong z ong +zou z ou +zu z u +zua z ua +zuai z uai +zuan z uan +zuang z uang +zui z ui +zun z un +zuo z uo +zv z v +zyi z i diff --git a/docs/BestPractices.md b/docs/BestPractices.md new file mode 100644 index 0000000..cc9c26d --- /dev/null +++ b/docs/BestPractices.md @@ -0,0 +1,651 @@ +# Best Practices + +## Fundamental concepts and materials + +### Configuration files + +A configuration file is a YAML file that defines enabled features, model hyperparameters and controls the behavior of the binarizer, trainer and inference. Almost all settings and controls in this repository, including the practices in this guidance, are achieved through configuration files. + +For more information of the configuration system and configurable attributes, see [Configuration Schemas](ConfigurationSchemas.md). + +### Languages + +Each language you are dealing with should have a unique tag in the configuration file. **We highly recommend using ISO 639 language codes as language tags.** For example, `zh` and `zho` stands for Chinese (`cmn` specifically for Mandarin Chinese), `ja` and `jpn` for Japanese, `en` and `eng` for English, `yue` for Cantonese (Yue). You can download a complete language code table from https://iso639-3.sil.org/code_tables/download_tables. + +### Phonemes + +Phonemes are the fundamental part of dictionaries and labels. There are two types of phonemes: language-specific phonemes and global phonemes. + +**Language-specific phonemes:** If there are multiple languages, all language-specific phonemes will be prefixed with its language name. For example: `zh/a`, `ja/o`, `en/eh`. These are called the **full name** of the phonemes, while `a`, `o`, `eh` are called the **short name** which has definite meaning only in a specific language context. If there is only one language, the short names can be used to determine each phoneme. + +**Global phonemes:** Some phonemes do not belong to any language. There are two reserved global phoneme tags: `SP` for space, and `AP` for aspiration. There can also be other user-defined tags (`EP`, `GS`, `VF`, etc.). These tags will not be prefixed with language, and are prior when identifying phoneme names. + +Extra phonemes, including user-defined global phonemes and additional language-specific phonemes that are not present in the dictionaries, can be defined in a list in the configuration file (full names should be used): + +```yaml +extra_phonemes: ['EP', 'ja/cl'] +``` + +The phoneme set expands rapidly with the number of languages. There are actually many similar phonemes that can be merged. Define the merging groups in your configuration file (full names should be used): + +```yaml +merged_phoneme_groups: + - [zh/i, ja/i, en/iy] + - [zh/s, ja/s, en/s] + - [ja/cl, SP] # global phonemes can also be merged + # ... (other groups omitted for brevity) +use_lang_id: true # whether to use language embedding; only take effects if there are cross-lingual phonemes +``` + +Merging phonemes does not mean that they are exactly the same for the dictionary. For those cross-lingual merged phonemes, Setting `use_lang_id` to true will still distinguish them by language IDs. + +#### Phoneme naming principles + +- Short names of language-specific phonemes should not conflict with global phoneme names, including reserved ones. +- `/` cannot be used because it is already used for splitting the language tag and the short name. +- `-` and `+` cannot be used because they are defined as slur tags in most singing voice synthesis editors. +- Other special characters, including but not limited to `@`, `#`, `&`, `|`, `<`, `>`, is not recommended because they may be used as special tags in the future format changes. +- ASCII characters are preferred for the best encoding compatibility, but all UTF-8 characters are acceptable. + +### Dictionaries + +Each language should have a corresponding dictionary. Define languages and dictionaries in your configuration file: + +```yaml +dictionaries: + zh: dictionaries/opencpop-extension.txt + ja: dictionaries/japanese_dict_full.txt + en: dictionaries/ds_cmudict-07b.txt +num_lang: 3 # number of languages; should be >= number of defined languages +``` + +Each dictionary is a *.txt* file, in which each line represents a mapping rule from one syllable to its phoneme sequence. The syllable and the phonemes are split by `tab`, and the phonemes are split by `space`: + +``` + ... +``` + +#### Syllable naming principles + +- Try to use a standard writing or pronouncing system. For example, pinyin for Mandarin Chinese, romaji for Japanese and English words for English. +- `AP` and `SP` cannot be used because they are reserved tags when using DiffSinger in editors. +- `/` cannot be used because it is already used for splitting the language tag and the short name. +- `-` and `+` cannot be used because they are defined as slur tags in most singing voice synthesis editors. +- Syllable names is not recommended to start with `.` because this may have special meanings in the future editors. +- Other special characters, including but not limited to `@`, `#`, `&`, `|`, `<`, `>`, is not recommended because they may be used as special tags in the future format changes. +- ASCII characters are preferred for the best encoding compatibility, but all UTF-8 characters are acceptable. + +There are some example dictionaries in the [dictionaries/](../dictionaries) folder. + +### Datasets + +A dataset mainly includes recordings and transcriptions, which is called a _raw dataset_. Raw datasets should be organized as the following folder structure: + +- my_raw_data/ + - wavs/ + - 001.wav + - 002.wav + - ... (more recording files) + - transcriptions.csv + +In the example above, the _my_raw_data_ directory is the root directory of a raw dataset. + +The _transcriptions.csv_ file contains all labels of the recordings. The common column of the CSV file is `name`, which represents all recording items by their filenames **without extension**. Elements of sequence attributes should be split by `space`. Other required columns may vary according to the category of the model you are training, and will be introduced in the following sections. + +Each dataset should have a main language. If you have many recordings in multiple languages, it is recommended to separate them by language (you can merge their speaker IDs in the configuration). In each dataset, the main language is set as the language context, and phoneme labels in transcriptions.csv do not need a prefix (short name). It is also valid if there are phonemes from other languages, but all of them should be prefixed with their actual language (full name). Global phonemes should not be prefixed in any datasets. + +You can define your datasets in the configuration file like this: + +```yaml +datasets: # define all raw datasets + - raw_data_dir: data/spk1-zh/raw # path to the root of a raw dataset + speaker: speaker1 # speaker name + spk_id: 0 # optional; use this to merge two datasets; otherwise automatically assigned + language: zh # language tag (main language) of this dataset + test_prefixes: # optional; validation samples from this dataset + - wav1 + - wav2 + - raw_data_dir: data/spk1-en/raw + speaker: speaker1 + spk_id: 0 # specify the same speaker ID to merge into the previous one + language: en + test_prefixes: + - wav1 + - wav2 + - raw_data_dir: data/spk2/raw + speaker: speaker2 + language: ja + test_prefixes: + - wav1 + - wav2 + # ... (other datasets omitted for brevity) +num_spk: 2 # number of languages; should be > maximum speaker ID +``` + +### DS files + +DS files are JSON files with _.ds_ suffix that contains phoneme sequence, phoneme durations, music scores or curve parameters. They are mainly used to run inference on models for test and evaluation purposes, and they can be used as training data in some cases. There are some example DS files in the [samples/](../samples) folder. + +The current recommended way of using a model for production purposes is to use [OpenUTAU for DiffSinger](https://github.com/xunmengshe/OpenUtau). It can export DS files as well. + +### Other fundamental assets + +#### Vocoders + +A vocoder is a model that can reconstruct the audio waveform given the low-dimensional mel-spectrogram. The vocoder is the essential dependency if you want to train an acoustic model and hear the voice on the TensorBoard. + +The [DiffSinger Community Vocoders Project](https://openvpi.github.io/vocoders) provides a universal pre-trained NSF-HiFiGAN vocoder that can be used for starters of this repository. To use it, download the model (~50 MB size) from its releases and unzip it into the `checkpoints/` folder. + +The pre-trained vocoder can be fine-tuned on your target dataset. It is highly recommended to do so because fine-tuned vocoder can generate much better results on specific (seen) datasets while does not need much computing resources. See the [vocoder training and fine-tuning repository](https://github.com/openvpi/SingingVocoders) for detailed instructions. After you get the fine-tuned vocoder checkpoint, you can configure it by `vocoder_ckpt` key in your configuration file. The fine-tuned NSF-HiFiGAN vocoder checkpoints can be exported to ONNX format like other DiffSinger user models for further production purposes. + +Another unrecommended option: train an ultra-lightweight [DDSP vocoder](https://github.com/yxlllc/pc-ddsp) first by yourself, then configure it according to the relevant [instructions](https://github.com/yxlllc/pc-ddsp/blob/master/DiffSinger.md). + +#### Feature extractors or auxiliary models + +RMVPE is the recommended pitch extractor of this repository, which is an NN-based algorithm and requires a pre-trained model. For more information about pitch extractors and how to configure them, see [feature extraction](#pitch-extraction). + +Vocal Remover (VR) is the recommended harmonic-noise separator of this repository, which is an NN-based algorithm and requires a pre-trained model. For more information about harmonic-noise separators and how to configure them, see [feature extraction](#harmonic-noise-separation). + +## Overview: training acoustic models + +An acoustic model takes low-level singing information as input, including (but not limited to) phoneme sequence, phoneme durations and F0 sequence. The only output of an acoustic model is the mel-spectrogram, which can be converted to waveform (the final audio) through the vocoder. Briefly speaking, an acoustic model takes in all features that are explicitly given, and produces the singing voice. + +### Datasets + +To train an acoustic model, you must have three columns in your transcriptions.csv: `name`, `ph_seq` and `ph_dur`, where `ph_seq` is the phoneme sequence and `ph_dur` is the phoneme duration sequence in seconds. You must have all corresponding recordings declared by the `name` column in mono, WAV format. + +Training from multiple datasets in one model (so that the model is a multi-speaker model) is supported. See `speakers`, `spk_ids` and `use_spk_id` in the configuration schemas. + +### Functionalities + +Functionalities of acoustic models are defined by their inputs. Acoustic models have three basic and fixed inputs: phoneme sequence, phoneme duration sequence and F0 (pitch) sequence. There are three categories of additional inputs (control parameters): + +- speaker IDs: if your acoustic model is a multi-speaker model, you can use different speaker in the same model, or mix their timbre and style. +- variance parameters: these curve parameters are features extracted from the recordings, and can control the timbre and style of the singing voice. See `use_energy_embed` and `use_breathiness_embed` in the configuration schemas. Please note that variance parameters **do not have default values**, so they are usually obtained from the variance model at inference time. +- transition parameters: these values represent the transition of the mel-spectrogram, and are obtained by enabling data augmentation. They are scalars at training time and sequences at inference time. See `augmentation_args`, `use_key_shift_embed` and `use_speed_embed` in the configuration schemas. + +## Overview: training variance models + +A variance model takes high-level music information as input, including phoneme sequence, word division, word durations and music scores. The outputs of a variance model may include phoneme durations, pitch curve and other control parameters that will be consumed by acoustic models. Briefly speaking, a variance model works as an auxiliary tool (so-called _automatic parameter generator_) for the acoustic models. + +### Datasets + +To train a variance model, you must have all the required attributes listed in the following table in your transcriptions.csv according to the functionalities enabled. + +| | name | ph_seq | ph_dur | ph_num | note_seq | note_dur | +|:------------------------------:|:----:|:------:|:------:|:------:|:--------:|:--------:| +| phoneme duration prediction | ✓ | ✓ | ✓ | ✓ | | | +| pitch prediction | ✓ | ✓ | ✓ | | ✓ | ✓ | +| variance parameters prediction | ✓ | ✓ | ✓ | | | | + +The recommended way of building a variance dataset is to extend an acoustic dataset. You may have all the recordings prepared like the acoustic dataset as well, or [use DS files in your variance datasets](#build-variance-datasets-with-ds-files). + +Variance models support multi-speaker settings like acoustic models do. + +### Functionalities + +Functionalities of variance models are defined by their outputs. There are three main prediction modules that can be enabled/disable independently: + +- Duration Predictor: predicts the phoneme durations. See `predict_dur` in the configuration schemas. +- Pitch Predictor: predicts the pitch curve. See `predict_pitch` in the configuration schemas. +- Multi-Variance Predictor: jointly predicts other variance parameters. See `predict_energy` and `predict_breathiness` in the configuration schemas. + +There may be some mutual influence between the modules above when they are enabled together. See [mutual influence between variance modules](#mutual-influence-between-variance-modules) for more details. + +## Build variance datasets with DS files + +By default, the variance binarizer loads attributes from transcriptions.csv and searches for recording files (*.wav) to extract features and parameters. These attributes and parameters also exist in DS files, which are normally used for inference. This section introduces the required settings and important notes to build a variance dataset from DS files. + +First of all, you should edit your configuration file to enable loading from DS files: + +```yaml +binarization_args: + prefer_ds: true # prefer loading from DS files +``` + +Then you should prepare some DS files which are properly segmented. If you export DS files with OpenUTAU for DiffSinger, the DS files are already segmented according to the spaces between notes. You should put these DS files in a folder named `ds` in your raw dataset directory (besides the `wavs` folder). + +The DS files should also use the same dictionary as that of your target model. The attributes required vary from your target functionalities, as listed below: + +| attribute name | required by duration prediction | required by pitch prediction | required by variance parameters prediction | previous source | current source | +|:----------------------------:|:-------------------------------:|:----------------------------:|:------------------------------------------:|:---------------:|:--------------:| +| `name` | ✓ | ✓ | ✓ | CSV | CSV | +| `ph_seq` | ✓ | ✓ | ✓ | CSV | DS/CSV | +| `ph_dur` | ✓ | ✓ | ✓ | CSV | DS/CSV | +| `ph_num` | ✓ | | | CSV | DS/CSV | +| `note_seq` | | ✓ | | CSV | DS/CSV | +| `note_dur` | | ✓ | | CSV | DS/CSV | +| `f0_seq` | ✓ | ✓ | ✓ | WAV | DS/WAV | +| `energy`, `breathiness`, ... | | | ✓ | WAV | DS/WAV | + +This means you only need one column in transcriptions.csv, the `name` column, to declare all DS files included in the dataset. The name pattern can be: + +- Full name: `some-name` will firstly match the first segment in `some-name.ds`. +- Name with index: `some-name#0` and `some-name#1` will match segment 0 and segment 1 in `some-name.ds` if there are no match with full name. + +Though not recommended, the binarizer will still try to load attributes from transcriptions.csv or extract parameters from recordings if there are no matching DS files. In this case the full name matching logic is applied (the same as the normal binarization process). + +## Choosing variance parameters + +Variance parameters are a type of parameters that are significantly related to singing styles and emotions, have no default values and need to be predicted by the variance models. Choosing the proper variance parameters can obtain more controllability and expressiveness for your singing models. In this section, we are only talking about **narrowly defined variance parameters**, which are variance parameters except the pitch. + +### Supported variance parameters + +#### Energy + +> WARNING +> +> This parameter is no longer recommended in favor of the new voicing parameter. The latter are less coupled with breathiness than energy. + +Energy is defined as the RMS curve of the singing, in dB, which can control the strength of voice to a certain extent. + +#### Breathiness + +Breathiness is defined as the RMS curve of the aperiodic part of the singing, in dB, which can control the power of the air and unvoiced consonants in the voice. + +#### Voicing + +Voicing is defined as the RMS curve of the harmonic part of the singing, in dB, which can control the power of the harmonics in vowels and voiced consonants in the voice. + +#### Tension + +Tension is mostly related to the ratio of the base harmonic to the full harmonics, which can be used to control the strength and timbre of the voice. The ratio is calculated as +$$ +r = \frac{\text{RMS}(H_{full}-H_{base})}{\text{RMS}(H_{full})} +$$ +where $H_{full}$ is the full harmonics and $H_{base}$ is the base harmonic. The ratio is then mapped to the final domain via the inverse function of Sigmoid, that +$$ +T = \log{\frac{r}{1-r}} +$$ +where $T$ is the tension value. + +### Principles of choosing multiple parameters + +#### Energy, breathiness and voicing + +These three parameters should **NOT** be enabled together. Energy is the RMS of the full waveform, which is the composition of the harmonic part and the aperiodic part. Therefore, these three parameters are coupled with each other. + +#### Energy, voicing and tension + +When voicing (or energy) is enabled, it almost fixes the loudness. However, tension sometimes rely on the implicitly predicted loudness for more expressiveness, because when a person sings with higher tension, he/she always produces louder voice. For this reason, some people may find their models or datasets _less natural_ with tension control. To be specific, changing tension will change the timbre but keep the loudness, and changing voicing (or energy) will change the loudness but keep the timbre. This behavior can be suitable for some, but not all datasets and users. Therefore, it is highly recommended for everyone to conduct some experiments on the actual datasets used to train the model. + +## Mutual influence between variance modules + +In some recent experiments and researches, some mutual influence between the modules of variance models has been found. In practice, being aware of the influence and making use of it can improve accuracy and avoid instability of the model. + +### Influence on the duration predictor + +The duration predictor benefits from its downstream modules, like the pitch predictor and the variance predictor. + +The experiments were conducted on both manually refined datasets and automatically labeled datasets, and with pitch predictors driven by both base pitch and melody encoder. All the results have shown that when either of the pitch predictor and the variance predictor is enabled together with the duration predictor, its rhythm correctness and duration accuracy significantly outperforms those of a solely trained duration predictor. + +Possible reason for this difference can be the lack of information carried by pure phoneme duration sequences, which may not fully represent the phoneme features in the real world. With the help of frame-level feature predictors, the encoder learns more knowledge about the voice features related to the phoneme types and durations, thus making the duration predictor produce better results. + +### Influence on frame-level feature predictors + +Frame-level feature predictors, including the pitch predictor and the variance predictor, have better performance when trained without enabling the duration predictor. + +The experiments found that when the duration predictor is enabled, the pitch accuracy drops and the dynamics of variance parameters sometimes become unstable. And it has nothing to do with the gradients from the duration predictor, because applying a scale factor on the gradients does not make any difference even if the gradients are completely cut off. + +Possible reason for this phenomenon can be the lack of direct phoneme duration input. When the duration predictor is enabled, the model takes in word durations instead of phoneme durations; when there is no duration predictor together, the phoneme duration sequence is directly taken in and passed through the attention-based linguistic encoder. With direct modeling on the phoneme duration, the frame-level predictors can have a better understanding of the context, thus producing better results. + +Another set of experiments showed that there is no significant influence between the pitch predictor and the variance predictor. When they are enabled together without the duration predictor, both can converge well and produce satisfactory results. No conclusion can be drawn on this issue, and it can depend on the dataset. + +### Suggested procedures of training variance models + +According to the experiment results and the analysis above, the suggested procedures of training a set of variance models are listed below: + +1. Train the duration predictor together with the variance predictor, and discard the variance predictor part. +2. Train the pitch predictor and the variance predictor separately or together. +3. If interested, compare across different combinations in step 2 and choose the best. + +## Feature extraction + +Feature extraction is the process of extracting low-level features from the recordings, which are needed as inputs for the acoustic models, or as outputs for the variance models. + +### Pitch extraction + +A pitch extractor estimates pitch (F0 sequence) from given recordings. F0 (fundamental frequency) is one of the most important components of singing voice that is needed by both acoustic models and variance models. + +```yaml +pe: parselmouth # pitch extractor type +pe_ckpt: checkpoints/xxx/model.pt # pitch extractor model path (if it requires any) +``` + +#### Parselmouth + +[Parselmouth](https://github.com/YannickJadoul/Parselmouth) is the default pitch extractor in this repository. It is based on DSP algorithms, runs fast on CPU and can get accurate F0 on clean and normal recordings. + +To use parselmouth, simply include the following line in your configuration file: + +```yaml +pe: parselmouth +``` + +#### RMVPE (recommended) + +[RMVPE](https://github.com/Dream-High/RMVPE) (Robust Model for Vocal Pitch Estimation) is the state-of-the-art NN-based pitch estimation model for singing voice. It runs slower than parselmouth, consumes more memory, however uses CUDA to accelerate computation (if available) and produce better results on noisy recordings and edge cases. + +To enable RMVPE, download its pre-trained checkpoint from [here](https://github.com/yxlllc/RMVPE/releases), extract it into the `checkpoints/` folder and edit the configuration file: + +```yaml +pe: rmvpe +pe_ckpt: checkpoints/rmvpe/model.pt +``` + +#### Harvest + +Harvest (Harvest: A high-performance fundamental frequency estimator from speech signals) is the recommended pitch extractor from Masanori Morise's [WORLD](https://github.com/mmorise/World), a free software for high-quality speech analysis, manipulation and synthesis. It is a state-of-the-art algorithmic pitch estimator designed for speech, but has seen use in singing voice synthesis. It runs the slowest compared to the others, but provides very accurate F0 on clean and normal recordings compared to parselmouth. + +To use Harvest, simply include the following line in your configuration file: + +```yaml +pe: harvest +``` + +**Note:** It is also recommended to change the F0 detection range for Harvest with accordance to your dataset, as they are hard boundaries for this algorithm and the defaults might not suffice for most use cases. To change the F0 detection range, you may include or edit this part in the configuration file: + +```yaml +f0_min: 65 # Minimum F0 to detect +f0_max: 800 # Maximum F0 to detect +``` + +### Harmonic-noise separation + +Harmonic-noise separation is the process of separating the harmonic part and the aperiodic part of the singing voice. These parts are the fundamental components for variance parameters including breathiness, voicing and tension to be calculated from. + +#### WORLD + +This algorithm uses Masanori Morise's [WORLD](https://github.com/mmorise/World), a free software for high-quality speech analysis, manipulation and synthesis. It uses CPU (no CUDA required) but runs relatively slow. + +To use WORLD, simply include the following line in your configuration file: + +```yaml +hnsep: world +``` + +#### Vocal Remover (recommended) + +Vocal Remover (VR) is originally a popular NN-based algorithm for music source separation that removes the vocal part from the music. This repository uses a specially trained model for harmonic-noise separation. VR extracts much cleaner harmonic parts, utilizes CUDA to accelerate computation (if available) and runs much faster than WORLD. However, it consumes more memory and should not be used with too many parallel workers. + +To enable VR, download its pre-trained checkpoint from [here](https://github.com/yxlllc/vocal-remover/releases), extract it into the `checkpoints/` folder and edit the configuration file: + +```yaml +hnsep: vr +hnsep_ckpt: checkpoints/vr/model.pt +``` + +## Shallow diffusion + +Shallow diffusion is a mechanism that can improve quality and save inference time for diffusion models that was first introduced in the original DiffSinger [paper](https://arxiv.org/abs/2105.02446). Instead of starting the diffusion process from purely gaussian noise as classic diffusion does, shallow diffusion adds a shallow gaussian noise on a low-quality results generated by a simple network (which is called the auxiliary decoder) to skip many unnecessary steps from the beginning. With the combination of shallow diffusion and sampling acceleration algorithms, we can get better results under the same inference speed as before, or achieve higher inference speed without quality deterioration. + +Currently, acoustic models in this repository support shallow diffusion. The main switch of shallow diffusion is `use_shallow_diffusion` in the configuration file, and most arguments of shallow diffusion can be adjusted under `shallow_diffusion_args`. See [Configuration Schemas](ConfigurationSchemas.md) for more details. + +### Train full shallow diffusion models from scratch + +To train a full shallow diffusion model from scratch, simply introduce the following settings in your configuration file: + +```yaml +use_shallow_diffusion: true +K_step: 400 # adjust according to your needs +K_step_infer: 400 # should be <= K_step +``` + +Please note that when shallow diffusion is enabled, only the last $K$ diffusion steps will be trained. Unlike classic diffusion models which are trained on full steps, the limit of `K_step` can make the training more efficient. However, `K_step` should not be set too small because without enough diffusion depth (steps), the low-quality auxiliary decoder results cannot be well refined. 200 ~ 400 should be the proper range of `K_step`. + +The auxiliary decoder and the diffusion decoder shares the same linguistic encoder, which receives gradients from both the decoders. In some experiments, it was found that gradients from the auxiliary decoder will cause mismatching between the encoder and the diffusion decoder, resulting in the latter being unable to produce reasonable results. To prevent this case, a configuration item called `aux_decoder_grad` is introduced to apply a scale factor on the gradients from the auxiliary decoder during training. To adjust this factor, introduce the following in the configuration file: + +```yaml +shallow_diffusion_args: + aux_decoder_grad: 0.1 # should not be too high +``` + +### Train auxiliary decoder and diffusion decoder separately + +Training a full shallow diffusion model can consume more memory because the auxiliary decoder is also in the training graph. In limited situations, the two decoders can be trained separately, i.e. train one decoder after another. + +**STEP 1: train the diffusion decoder** + +In the first stage, the linguistic encoder and the diffusion decoder is trained together, while the auxiliary decoder is left unchanged. Edit your configuration file like this: + +```yaml +use_shallow_diffusion: true # make sure the main option is turned on +shallow_diffusion_args: + train_aux_decoder: false # exclude the auxiliary decoder from the training graph + train_diffusion: true # train diffusion decoder as normal + val_gt_start: true # should be true because the auxiliary decoder is not trained yet +``` + +Start training until `max_updates` is reached, or until you get satisfactory results on the TensorBoard. + +**STEP 2: train the auxiliary decoder** + +In the second stage, the auxiliary decoder is trained besides the linguistic encoder and the diffusion decoder. Edit your configuration file like this: + +```yaml +shallow_diffusion_args: + train_aux_decoder: true + train_diffusion: false # exclude the diffusion decoder from the training graph +lambda_aux_mel_loss: 1.0 # no more need to limit the auxiliary loss +``` + +Then you should freeze the encoder to prevent it from getting updates. This is because if the encoder changes, it no longer matches with the diffusion decoder, thus making the latter unable to produce correct results again. Edit your configuration file: + +```yaml +freezing_enabled: true +frozen_params: + - model.fs2 # the linguistic encoder +``` + +You should also manually reset your learning rate scheduler because this is a new training process for the auxiliary decoder. Possible ways are: + +1. Rename the latest checkpoint to `model_ckpt_steps_0.ckpt` and remove the other checkpoints from the directory. +2. Increase the initial learning rate (if you use a scheduler that decreases the LR over training steps) so that the auxiliary decoder gets proper learning rate. + +Additionally, `max_updates` should be adjusted to ensure enough training steps for the auxiliary decoder. + +Once you finished the configurations above, you can resume the training. The auxiliary decoder normally does not need many steps to train, and you can stop training when you get stable results on the TensorBoard. Because this step is much more complicated than the previous step, it is recommended to run some inference to verify if the model is trained properly after everything is finished. + +### Add shallow diffusion to classic diffusion models + +Actually, all classic DDPMs have the ability to be "shallow". If you want to add shallow diffusion functionality to a former classic diffusion model, the only thing you need to do is to train an auxiliary decoder for it. + +Before you start, you should edit the configuration file to ensure that you use the same datasets, and that you do not remove or add any of the functionalities of the old model. Then you can configure the old checkpoint in your configuration file: + +```yaml +finetune_enabled: true +finetune_ckpt_path: xxx.ckpt # path to your old checkpoint +finetune_ignored_params: [] # do not ignore any parameters +``` + +Then you can follow the instructions in STEP 2 of the [previous section](#add-shallow-diffusion-to-classic-diffusion-models) to finish your training. + +## Performance tuning + +This section is about accelerating training and utilizing hardware. + +### Data loader and batch sampler + +The data loader loads data pieces from the binary dataset, and the batch sampler forms batches according to data lengths. + +To configure the data loader, edit your configuration file: + +```yaml +ds_workers: 4 # number of DataLoader workers +dataloader_prefetch_factor: 2 # load data in advance +``` + +To configure the batch sampler, edit your configuration file: + +```yaml +sampler_frame_count_grid: 6 # lower value means higher speed but less randomness +``` + +For more details of the batch sampler algorithm and this configuration key, see [sampler_frame_count_grid](ConfigurationSchemas.md#sampler_frame_count_grid). + +### Automatic mixed precision + +Enabling automatic mixed precision (AMP) can accelerate training and save GPU memory. DiffSinger have adapted the latest version of PyTorch Lightning for AMP functionalities. + +By default, the training runs in FP32 precision. To enable AMP, edit your configuration file: + +```yaml +pl_trainer_precision: 16-mixed # FP16 precision +``` + +or + +```yaml +pl_trainer_precision: bf16-mixed # BF16 precision +``` + +For more precision options, please check out the [official documentation](https://lightning.ai/docs/pytorch/stable/common/trainer.html#precision). + +### Training on multiple GPUs + +Using distributed data parallel (DDP) can divide training tasks to multiple GPUs and synchronize gradients and weights between them. DiffSinger have adapted the latest version of PyTorch Lightning for DDP functionalities. + +By default, the trainer will utilize all CUDA devices defined in the `CUDA_VISIBLE_DEVICES` environment variable (empty means using all available devices). If you want to specify which GPUs to use, edit your configuration file: + +```yaml +pl_trainer_devices: [0, 1, 2, 3] # use the first 4 GPUs defined in CUDA_VISIBLE_DEVICES +``` + +Please note that `max_batch_size` and `max_batch_frames` are values for **each** GPU. + +By default, the trainer uses NCCL as the DDP backend. If this gets stuck on your machine, try disabling P2P first via + +```yaml +nccl_p2p: false # disable P2P in NCCL +``` + +Or if your machine does not support NCCL, you can switch to Gloo instead: + +```yaml +pl_trainer_strategy: + name: ddp # must manually choose a strategy instead of 'auto' + process_group_backend: gloo # however, it has a lower performance than NCCL +``` + +### Gradient accumulation + +Gradient accumulation means accumulating losses for several batches before each time the weights are updated. This can simulate a larger batch size with a lower GPU memory cost. + +By default, the trainer calls `backward()` each time the losses are calculated through one batch of data. To enable gradient accumulation, edit your configuration file: + +```yaml +accumulate_grad_batches: 4 # the actual batch size will be 4x. +``` + +Please note that enabling gradient accumulation will slow down training because the losses must be calculated for several times before the weights are updated (1 update to the weights = 1 actual training step). + +## Optimizers and learning rate schedulers + +The optimizer and the learning rate scheduler can take an important role in the training process. DiffSinger uses a flexible configuration logic for these two modules. + +### Basic configurations + +The optimizer and learning rate scheduler used during training can be configured by their full class name and keyword arguments in the configuration file. Take the following as an example for the optimizer: + +```yaml +optimizer_args: + optimizer_cls: torch.optim.AdamW # class name of optimizer + lr: 0.0004 + beta1: 0.9 + beta2: 0.98 + weight_decay: 0 +``` + +and for the learning rate scheduler: + +```yaml +lr_scheduler_args: + scheduler_cls: torch.optim.lr_scheduler.StepLR # class name of learning rate schedule + warmup_steps: 2000 + step_size: 50000 + gamma: 0.5 +``` + +Note that `optimizer_args` and `lr_scheduler_args` will be filtered by needed parameters and passed to `__init__` as keyword arguments (`kwargs`) when constructing the optimizer and scheduler. Therefore, you could specify all arguments according to your need in the configuration file to directly control the behavior of optimization and LR scheduling. It will also tolerate parameters existing in the configuration but not needed in `__init__`. + +Also, note that the LR scheduler performs scheduling on the granularity of steps, not epochs. + +The special case applies when a tuple is needed in `__init__`: `beta1` and `beta2` are treated separately and form a tuple in the code. You could try to pass in an array instead. (And as an experiment, AdamW does accept `[beta1, beta2]`). If there is another special treatment required, please submit an issue. + +For PyTorch built-in optimizers and LR schedulers, see official [documentation](https://pytorch.org/docs/stable/optim.html) of the `torch.optim` package. If you found other optimizer and learning rate scheduler useful, you can raise a topic in [Discussions](https://github.com/openvpi/DiffSinger/discussions), raise [Issues](https://github.com/openvpi/DiffSinger/issues) or submit [PRs](https://github.com/openvpi/DiffSinger/pulls) if it introduces new codes or dependencies. + +### Composite LR schedulers + +Some LR schedulers like `SequentialLR` and `ChainedScheduler` may use other schedulers as arguments. Besides built-in types, there is a special design to configure these scheduler objects. See the following example. + +```yaml +lr_scheduler_args: + scheduler_cls: torch.optim.lr_scheduler.SequentialLR + schedulers: + - cls: torch.optim.lr_scheduler.ExponentialLR + gamma: 0.5 + - cls: torch.optim.lr_scheduler.LinearLR + - cls: torch.optim.lr_scheduler.MultiStepLR + milestones: + - 10 + - 20 + milestones: + - 10 + - 20 +``` + +The LR scheduler objects will be recursively construct objects if `cls` is present in sub-arguments. Please note that `cls` must be a scheduler class because this is a special design. + +**WARNING:** Nested `SequentialLR` and `ChainedScheduler` have unexpected behavior. **DO NOT** nest them. Also, make sure the scheduler is _chainable_ before using it in `ChainedScheduler`. + +## Fine-tuning and parameter freezing + +### Fine-tuning from existing checkpoints + +By default, the training starts from a model from scratch with randomly initialized parameters. However, if you already have some pre-trained checkpoints, and you need to adapt them to other datasets with their functionalities unchanged, fine-tuning may save training steps and time. In general, you need to add the following structure into the configuration file: + +```yaml +# take acoustic models as an example +finetune_enabled: true # the main switch to enable fine-tuning +finetune_ckpt_path: checkpoints/pretrained/model_ckpt_steps_320000.ckpt # path to your pre-trained checkpoint +finetune_ignored_params: # prefix rules to exclude specific parameters when loading the checkpoints + - model.fs2.encoder.embed_tokens # in case when the phoneme set is changed + - model.fs2.txt_embed # same as above + - model.fs2.spk_embed # in case when the speaker set is changed +finetune_strict_shapes: true # whether to raise an error when parameter shapes mismatch +``` + +For the pre-trained checkpoint, it must be a file saved with `torch.save`, containing a `dict` object and a `state_dict` key, like the following example: + +```json5 +{ + "state_dict": { + "model.fs2.txt_embed": null, // torch.Tensor + "model.fs2.pitch_embed.weight": null, // torch.Tensor + "model.fs2.pitch_embed.bias": null, // torch.Tensor + // ... (other parameters) + } + // ... (other possible keys +} +``` + +**IMPORTANT NOTES**: + +- The pre-trained checkpoint is **loaded only once** at the beginning of the training experiment. You may interrupt the training at any time, but after this new experiment has saved its own checkpoint, the pre-trained checkpoint will not be loaded again when the training is resumed. +- Only the state dict of the checkpoint will be loaded. The optimizer state in the pre-trained checkpoint will be ignored. +- The parameter name matching is **not strict** when loading the pre-trained checkpoint. This means that missing parameters in the state dict will still be left as randomly initialized, and redundant parameters will be ignored without any warnings and errors. There are cases where the tensor shapes mismatch between the pre-trained state dict and the model - edit `finetune_strict_shapes` to change the behavior when dealing with this. +- Be careful if you want to change the functionalities when fine-tuning. Starting from a checkpoint trained under different functionalities may be even slower than training from scratch. + +### Freezing model parameters + +Sometimes you want to freeze part of the model during training or fine-tuning to save GPU memory, accelerate the training process or avoid catastrophic forgetting. Parameter freezing may also be useful if you want to add/remove functionalities from pre-trained checkpoints. In general, you need to add the following structure into the configuration file: + +```yaml +# take acoustic models as an example +freezing_enabled: true # main switch to enable parameter freezing +frozen_params: # prefix rules to freeze specific parameters during training + - model.fs2.encoder + - model.fs2.pitch_embed +``` + +You may interrupt the training and change the settings above at any time. Sometimes this will cause mismatching optimizer state - and it will be discarded silently. diff --git a/docs/ConfigurationSchemas.md b/docs/ConfigurationSchemas.md new file mode 100644 index 0000000..b7c8df6 --- /dev/null +++ b/docs/ConfigurationSchemas.md @@ -0,0 +1,2274 @@ +# Configuration Schemas + +## The configuration system + +DiffSinger uses a cascading configuration system based on YAML files. All configuration files originally inherit and override [configs/base.yaml](../configs/base.yaml), and each file directly override another file by setting the `base_config` attribute. The overriding rules are: + +- Configuration keys with the same path and the same name will be replaced. Other paths and names will be merged. +- All configurations in the inheritance chain will be squashed (via the rule above) as the final configuration. +- The trainer will save the final configuration in the experiment directory, which is detached from the chain and made independent from other configuration files. + +## Configurable parameters + +This following are the meaning and usages of all editable keys in a configuration file. + +Each configuration key (including nested keys) are described with a brief explanation and several attributes listed as follows: + +| Attribute | Explanation | +|:---------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| visibility | Represents what kind(s) of models and tasks this configuration belongs to. | +| scope | The scope of effects of the configuration, indicating what it can influence within the whole pipeline. Possible values are:
**nn** - This configuration is related to how the neural networks are formed and initialized. Modifying it will result in failure when loading or resuming from checkpoints.
**preprocessing** - This configuration controls how raw data pieces or inference inputs are converted to inputs of neural networks. Binarizers should be re-run if this configuration is modified.
**training** - This configuration describes the training procedures. Most training configurations can affect training performance, memory consumption, device utilization and loss calculation. Modifying training-only configurations will not cause severe inconsistency or errors in most situations.
**inference** - This configuration describes the calculation logic through the model graph. Changing it can lead to inconsistent or wrong outputs of inference or validation.
**others** - Other configurations not discussed above. Will have different effects according to the descriptions. | +| customizability | The level of customizability of the configuration. Possible values are:
**required** - This configuration **must** be set or modified according to the actual situation or condition, otherwise errors can be raised.
**recommended** - It is recommended to adjust this configuration according to the dataset, requirements, environment and hardware. Most functionality-related and feature-related configurations are at this level, and all configurations in this level are widely tested with different values. However, leaving it unchanged will not cause problems.
**normal** - There is no need to modify it as the default value is carefully tuned and widely validated. However, one can still use another value if there are some special requirements or situations.
**not recommended** - No other values except the default one of this configuration are tested. Modifying it will not cause errors, but may cause unpredictable or significant impacts to the pipelines.
**reserved** - This configuration **must not** be modified. It appears in the configuration file only for future scalability, and currently changing it will result in errors. | +| type | Value type of the configuration. Follows the syntax of Python type hints. | +| constraints | Value constraints of the configuration. | +| default | Default value of the configuration. Uses YAML value syntax. | + +### accumulate_grad_batches + +Indicates that gradients of how many training steps are accumulated before each `optimizer.step()` call. 1 means no gradient accumulation. + + + + + + + +
visibilityall
scopetraining
customizabilityrecommended
typeint
default1
+ +### audio_num_mel_bins + +Number of mel channels for the mel-spectrogram. + + + + + + + +
visibilityacoustic
scopenn, preprocessing, inference
customizabilityreserved
typeint
default128
+ +### audio_sample_rate + +Sampling rate of waveforms. + + + + + + + +
visibilityacoustic, variance
scopepreprocessing
customizabilityreserved
typeint
default44100
+ +### augmentation_args + +Arguments for data augmentation. + + + +
typedict
+ +### augmentation_args.fixed_pitch_shifting + +Arguments for fixed pitch shifting augmentation. + + + +
typedict
+ +### augmentation_args.fixed_pitch_shifting.enabled + +Whether to apply fixed pitch shifting augmentation. + + + + + + + + +
visibilityacoustic
scopepreprocessing
customizabilityrecommended
typebool
defaultfalse
constraintsMust be false if augmentation_args.random_pitch_shifting.enabled is set to true.
+ +### augmentation_args.fixed_pitch_shifting.scale + +Scale ratio of each target in fixed pitch shifting augmentation. + + + + + + + +
visibilityacoustic
scopepreprocessing
customizabilityrecommended
typetuple
default0.5
+ +### augmentation_args.fixed_pitch_shifting.targets + +Targets (in semitones) of fixed pitch shifting augmentation. + + + + + + + +
visibilityacoustic
scopepreprocessing
customizabilitynot recommended
typetuple
default[-5.0, 5.0]
+ +### augmentation_args.random_pitch_shifting + +Arguments for random pitch shifting augmentation. + + + +
typedict
+ +### augmentation_args.random_pitch_shifting.enabled + +Whether to apply random pitch shifting augmentation. + + + + + + + + +
visibilityacoustic
scopepreprocessing
customizabilityrecommended
typebool
defaulttrue
constraintsMust be false if augmentation_args.fixed_pitch_shifting.enabled is set to true.
+ +### augmentation_args.random_pitch_shifting.range + +Range of the random pitch shifting ( in semitones). + + + + + + + +
visibilityacoustic
scopepreprocessing
customizabilitynot recommended
typetuple
default[-5.0, 5.0]
+ +### augmentation_args.random_pitch_shifting.scale + +Scale ratio of the random pitch shifting augmentation. + + + + + + + +
visibilityacoustic
scopepreprocessing
customizabilityrecommended
typefloat
default0.75
+ +### augmentation_args.random_time_stretching + +Arguments for random time stretching augmentation. + + + +
typedict
+ +### augmentation_args.random_time_stretching.enabled + +Whether to apply random time stretching augmentation. + + + + + + + +
visibilityacoustic
scopepreprocessing
customizabilityrecommended
typebool
defaulttrue
+ +### augmentation_args.random_time_stretching.range + +Range of random time stretching factors. + + + + + + + +
visibilityacoustic
scopepreprocessing
customizabilitynot recommended
typetuple
default[0.5, 2]
+ +### augmentation_args.random_time_stretching.scale + +Scale ratio of random time stretching augmentation. + + + + + + + +
visibilityacoustic
scopepreprocessing
customizabilityrecommended
typefloat
default0.75
+ +### backbone_args + +Keyword arguments for the backbone of main decoder module. + + + + + +
visibilityacoustic, variance
scopenn
typedict
+ +Available arguments for each backbone type are listed below. + +**WaveNet** (`backbone_type: wavenet`) + +| argument name | type | default | description | +|:----------------------|:----:|:-------:|:--------------------------------------------------------------------------------------------------------------| +| num_layers | int | 20 | Number of residual block layers, or depth of the network | +| num_channels | int | 512 | Number of channels, or width of the network | +| dilation_cycle_length | int | 4 | Length k of the cycle $2^0, 2^1, \ldots, 2^k$ of convolution dilation factors through WaveNet residual blocks | + +**LYNXNet** (`backbone_type: lynxnet`) + +| argument name | type | default | description | +|:--------------|:-----:|:-------:|:--------------------------------------------------------------------------------| +| num_layers | int | 6 | Number of LYNXNet blocks, or depth of the network | +| num_channels | int | 1024 | Number of channels, or width of the network | +| kernel_size | int | 31 | Kernel size of the depthwise convolution layers | +| dropout_rate | float | 0.0 | Dropout rate applied in each LYNXNet block | +| strong_cond | bool | false | Whether to use strong conditioning, which injects condition before the GLU gate | + +**LYNXNet2** (`backbone_type: lynxnet2`) + +| argument name | type | default | description | +|:----------------------|:-----:|:-------:|:-------------------------------------------------------------------------------------------------| +| num_layers | int | 6 | Number of LYNXNet2 blocks, or depth of the network | +| num_channels | int | 1024 | Number of channels, or width of the network | +| kernel_size | int | 31 | Kernel size of the depthwise convolution layers | +| dropout_rate | float | 0.0 | Dropout rate applied in each LYNXNet2 block | +| use_conditioner_cache | bool | true | Whether to use Conv1d-based conditioner projection (compatible with conditioner caching) | +| glu_type | str | atanglu | Type of gated linear unit activation. Choose from `'swiglu'` for SwiGLU, `'atanglu'` for ATanGLU | +| expansion_factor | int | 1 | Channel expansion factor within each gated block (not commonly overridden) | + +### backbone_type + +Backbone type of the main decoder/predictor module. + + + + + + + + +
visibilityacoustic, variance
scopenn
customizabilitynormal
typestr
defaultlynxnet2
constraintsChoose from 'wavenet', 'lynxnet', 'lynxnet2'.
+ +### base_config + +Path(s) of other config files that the current config is based on and will override. + + + + +
scopeothers
typeUnion[str, list]
+ +### binarization_args + +Arguments for binarizers. + + + +
typedict
+ +### binarization_args.num_workers + +Number of worker subprocesses when running binarizers. More workers can speed up the preprocessing but will consume more memory. 0 means the main processing doing everything. + + + + + + + +
visibilityall
scopepreprocessing
customizabilityrecommended
typeint
default1
+ +### binarization_args.prefer_ds + +Whether to prefer loading attributes and parameters from DS files. + + + + + + + +
visibilityvariance
scopepreprocessing
customizabilityrecommended
typebool
defaultFalse
+ +### binarization_args.shuffle + +Whether binarized dataset will be shuffled or not. + + + + + + + +
visibilityall
scopepreprocessing
customizabilitynormal
typebool
defaulttrue
+ +### binarizer_cls + +Binarizer class name. + + + + + + +
visibilityall
scopepreprocessing
customizabilityreserved
typestr
+ +### binary_data_dir + +Path to the binarized dataset. + + + + + + +
visibilityall
scopepreprocessing, training
customizabilityrequired
typestr
+ +### breathiness_db_max + +Maximum breathiness value in dB used for normalization to [-1, 1]. + + + + + + + +
visibilityvariance
scopeinference
customizabilityrecommended
typefloat
default-20.0
+ +### breathiness_db_min + +Minimum breathiness value in dB used for normalization to [-1, 1]. + + + + + + + +
visibilityacoustic, variance
scopeinference
customizabilityrecommended
typefloat
default-96.0
+ +### breathiness_smooth_width + +Length of sinusoidal smoothing convolution kernel (in seconds) on extracted breathiness curve. + + + + + + + +
visibilityacoustic, variance
scopepreprocessing
customizabilitynormal
typefloat
default0.06
+ +### clip_grad_norm + +The value at which to clip gradients. Equivalent to `gradient_clip_val` in `lightning.pytorch.Trainer`. + + + + + + + +
visibilityall
scopetraining
customizabilitynot recommended
typefloat
default1
+ +### dataloader_prefetch_factor + +Number of batches loaded in advance by each `torch.utils.data.DataLoader` worker. + + + + + + + +
visibilityall
scopetraining
customizabilitynormal
typeint
default2
+ +### dataset_size_key + +The key that indexes the binarized metadata to be used as the `sizes` when batching by size + + + + + + + +
visibilityall
scopetraining
customizabilitynot recommended
typestr
defaultlengths
+ +### datasets + +List of dataset configs for preprocessing. + + + + + +
visibilityacoustic, variance
scopepreprocessing
typeList[dict]
+ +### datasets[].language + +Language context of this dataset. Must be a key of [dictionaries](#dictionaries). + + + + + + +
visibilityacoustic, variance
scopepreprocessing
customizabilityrequired
typestr
+ +### datasets[].raw_data_dir + +Path to this dataset including wave files, transcriptions, etc. + + + + + + +
visibilityall
scopepreprocessing
customizabilityrequired
typestr
+ +### datasets[].speaker + +The name of speaker of this dataset. Speaker names are mapped to speaker indexes and stored into spk_map.json when preprocessing. + + + + + + +
visibilityacoustic, variance
scopepreprocessing
customizabilityrequired
typestr
+ +### datasets[].spk_id + +The speaker ID assigned to this dataset. Will be automatically assigned if not given. IDs can be duplicate or discontinuous to merge multiple datasets to one speaker. + + + + + + +
visibilityacoustic, variance
scopepreprocessing
customizabilitynormal
typeint
+ +### datasets[].test_prefixes + +List of data item names or name prefixes in this dataset for the validation set. For each string `s` in the list: + +- If `s` equals to an actual item name, add that item to validation set. +- If `s` does not equal to any item names, add all items whose names start with `s` to validation set. + + + + + + +
visibilityall
scopepreprocessing
customizabilityrequired
typelist
+ +### dictionaries + +Map of language names and their corresponding dictionary file paths. The phonemes in these dictionaries will be combined as the final phoneme set and have their phoneme IDs. Training data must fully cover all phoneme IDs. + + + + + + + +
visibilityacoustic, variance
scopepreprocessing
customizabilityrequired
typeDict[str, str]
default{}
+ +### diff_accelerator + +DDPM sampling acceleration method. The following methods are currently available: + +- DDIM: the DDIM method from [Denoising Diffusion Implicit Models](https://arxiv.org/abs/2010.02502) +- PNDM: the PLMS method from [Pseudo Numerical Methods for Diffusion Models on Manifolds](https://arxiv.org/abs/2202.09778) +- DPM-Solver++ adapted from [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://github.com/LuChengTHU/dpm-solver) +- UniPC adapted from [UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models](https://github.com/wl-zhao/UniPC) + + + + + + + + +
visibilityacoustic, variance
scopeinference
customizabilitynormal
typestr
defaultddim
constraintsChoose from 'ddim', 'pndm', 'dpm-solver', 'unipc'.
+ +### diff_speedup + +DDPM sampling speed-up ratio. 1 means no speeding up. + + + + + + + + +
visibilityacoustic, variance
scopeinference
customizabilitynormal
typeint
default10
constraintsMust be a factor of K_step.
+ +### diffusion_type + +The type of ODE-based generative model algorithm. The following models are currently available: + +- Denoising Diffusion Probabilistic Models (DDPM) from [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) +- Rectified Flow from [Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow](https://arxiv.org/abs/2209.03003) + + + + + + + + +
visibilityacoustic, variance
scopenn
customizabilitynormal
typestr
defaultreflow
constraintsChoose from 'ddpm', 'reflow'.
+ +### dropout + +Dropout rate in some FastSpeech2 modules. + + + + + + + +
visibilityacoustic, variance
scopenn
customizabilitynot recommended
typefloat
default0.1
+ +### ds_workers + +Number of workers of `torch.utils.data.DataLoader`. + + + + + + + +
visibilityall
scopetraining
customizabilitynormal
typeint
default4
+ +### dur_prediction_args + +Arguments for phoneme duration prediction. + + + +
typedict
+ +### dur_prediction_args.arch + +Architecture of duration predictor. `'fs2'` uses the original FastSpeech2 duration predictor with standard convolution layers. `'resnet'` uses a residual-style variant with additional layer normalization and residual connections, which may improve training stability. + + + + + + + + +
visibilityvariance
scopenn
customizabilityreserved
typestr
defaultresnet
constraintsChoose from 'fs2', 'resnet'.
+ +### dur_prediction_args.dropout + +Dropout rate in duration predictor. + + + + + + + +
visibilityvariance
scopenn
customizabilitynot recommended
typefloat
default0.1
+ +### dur_prediction_args.hidden_size + +Dimensions of hidden layers in duration predictor. + + + + + + + +
visibilityvariance
scopenn
customizabilitynormal
typeint
default256
+ +### dur_prediction_args.kernel_size + +Kernel size of convolution layers of duration predictor. + + + + + + + +
visibilityvariance
scopenn
customizabilitynormal
typeint
default3
+ +### dur_prediction_args.lambda_pdur_loss + +Coefficient of single phone duration loss when calculating joint duration loss. + + + + + + + +
visibilityvariance
scopetraining
customizabilitynormal
typefloat
default0.3
+ +### dur_prediction_args.lambda_sdur_loss + +Coefficient of sentence duration loss when calculating joint duration loss. + + + + + + + +
visibilityvariance
scopetraining
customizabilitynormal
typefloat
default3.0
+ +### dur_prediction_args.lambda_wdur_loss + +Coefficient of word duration loss when calculating joint duration loss. + + + + + + + +
visibilityvariance
scopetraining
customizabilitynormal
typefloat
default1.0
+ +### dur_prediction_args.log_offset + +Offset for log domain duration loss calculation, where the following transformation is applied: +$$ +D' = \ln{(D+d)} +$$ +with the offset value $d$. + + + + + + + +
visibilityvariance
scopetraining
customizabilitynot recommended
typefloat
default1.0
+ +### dur_prediction_args.loss_type + +Underlying loss type of duration loss. + + + + + + + + +
visibilityvariance
scopetraining
customizabilitynormal
typestr
defaultmse
constraintsChoose from 'mse', 'huber'.
+ +### dur_prediction_args.num_layers + +Number of duration predictor layers. + + + + + + + +
visibilityvariance
scopenn
customizabilitynormal
typeint
default5
+ +### enc_ffn_kernel_size + +Size of TransformerFFNLayer convolution kernel size in FastSpeech2 encoder. + + + + + + + +
visibilityacoustic, variance
scopenn
customizabilitynot recommended
typeint
default3
+ +### enc_layers + +Number of FastSpeech2 encoder layers. + + + + + + + +
visibilityacoustic, variance
scopenn
customizabilitynormal
typeint
default4
+ +### energy_db_max + +Maximum energy value in dB used for normalization to [-1, 1]. + + + + + + + +
visibilityvariance
scopeinference
customizabilityrecommended
typefloat
default-12.0
+ +### energy_db_min + +Minimum energy value in dB used for normalization to [-1, 1]. + + + + + + + +
visibilityvariance
scopeinference
customizabilityrecommended
typefloat
default-96.0
+ +### energy_smooth_width + +Length of sinusoidal smoothing convolution kernel (in seconds) on extracted energy curve. + + + + + + + +
visibilityacoustic, variance
scopepreprocessing
customizabilitynormal
typefloat
default0.06
+ +### extra_phonemes + +Extra phonemes to be added to the phoneme set. This list can be used to define custom global phoneme tags besides `AP` and `SP`, or to contain phonemes that are not present in any of the dictionaries. + + + + + + + +
visibilityacoustic, variance
scopepreprocessing
customizabilitynormal
typelist
default[]
+ +### f0_max + +Maximum base frequency (F0) in Hz for pitch extraction. + + + + + + + +
visibilityacoustic, variance
scopepreprocessing
customizabilitynormal
typeint
default1100
+ +### f0_min + +Minimum base frequency (F0) in Hz for pitch extraction. + + + + + + + +
visibilityacoustic, variance
scopepreprocessing
customizabilitynormal
typeint
default65
+ +### ffn_act + +Activation function of TransformerFFNLayer in FastSpeech2 encoder: + +- `torch.nn.ReLU` if 'relu' +- `torch.nn.GELU` if 'gelu' +- `torch.nn.SiLU` if 'swish' + + + + + + + + +
visibilityacoustic, variance
scopenn
customizabilitynot recommended
typestr
defaultgelu
constraintsChoose from 'relu', 'gelu', 'swish'.
+ +### fft_size + +Fast Fourier Transforms parameter for mel extraction. + + + + + + + +
visibilityacoustic, variance
scopepreprocessing
customizabilityreserved
typeint
default2048
+ +### finetune_enabled + +Whether to finetune from a pretrained model. + + + + + + + +
visibilityall
scopetraining
customizabilitynormal
typebool
defaultFalse
+ +### finetune_ckpt_path + +Path to the pretrained model for finetuning. + + + + + + + +
visibilityall
scopetraining
customizabilitynormal
typestr
defaultnull
+ +### finetune_ignored_params + +Prefixes of parameter key names in the state dict of the pretrained model that need to be dropped before finetuning. + + + + + + +
visibilityall
scopetraining
customizabilitynormal
typelist
+ +### finetune_strict_shapes + +Whether to raise error if the tensor shapes of any parameter of the pretrained model and the target model mismatch. If set to `False`, parameters with mismatching shapes will be skipped. + + + + + + + +
visibilityall
scopetraining
customizabilitynormal
typebool
defaultTrue
+ +### fmax + +Maximum frequency of mel extraction. + + + + + + + +
visibilityacoustic
scopepreprocessing
customizabilityreserved
typeint
default16000
+ +### fmin + +Minimum frequency of mel extraction. + + + + + + + +
visibilityacoustic
scopepreprocessing
customizabilityreserved
typeint
default40
+ +### freezing_enabled + +Whether enabling parameter freezing during training. + + + + + + + +
visibilityall
scopetraining
customizabilitynormal
typebool
defaultFalse
+ +### frozen_params + +Parameter name prefixes to freeze during training. + + + + + + + +
visibilityall
scopetraining
customizabilitynormal
typelist
default[]
+ +### glide_embed_scale + +The scale factor to be multiplied on the glide embedding values for melody encoder. + + + + + + + +
visibilityvariance
scopenn
customizabilitynot recommended
typefloat
default11.313708498984760
+ +### glide_types + +Type names of glide notes. + + + + + + + +
visibilityvariance
scopepreprocessing
customizabilitynormal
typelist
default[up, down]
+ +### hidden_size + +Dimension of hidden layers of FastSpeech2, token and parameter embeddings, and diffusion condition. + + + + + + + +
visibilityacoustic, variance
scopenn
customizabilitynormal
typeint
default384
+ +### hnsep + +Harmonic-noise separation algorithm type. + + + + + + + + +
visibilityall
scopepreprocessing
customizabilitynormal
typestr
defaultworld
constraintsChoose from 'world', 'vr'.
+ +### hnsep_ckpt + +Checkpoint or model path of NN-based harmonic-noise separator. + + + + + + +
visibilityall
scopepreprocessing
customizabilitynormal
typestr
+ +### hop_size + +Hop size or step length (in number of waveform samples) of mel and feature extraction. + + + + + + + +
visibilityacoustic, variance
scopepreprocessing
customizabilityreserved
typeint
default512
+ +### lambda_aux_mel_loss + +Coefficient of aux mel loss when calculating total loss of acoustic model with shallow diffusion. + + + + + + + +
visibilityacoustic
scopetraining
customizabilitynormal
typefloat
default0.2
+ +### lambda_dur_loss + +Coefficient of duration loss when calculating total loss of variance model. + + + + + + + +
visibilityvariance
scopetraining
customizabilitynormal
typefloat
default1.0
+ +### lambda_pitch_loss + +Coefficient of pitch loss when calculating total loss of variance model. + + + + + + + +
visibilityvariance
scopetraining
customizabilitynormal
typefloat
default1.0
+ +### lambda_var_loss + +Coefficient of variance loss (all variance parameters other than pitch, like energy, breathiness, etc.) when calculating total loss of variance model. + + + + + + + +
visibilityvariance
scopetraining
customizabilitynormal
typefloat
default1.0
+ +### K_step + +Maximum number of DDPM steps used by shallow diffusion. + + + + + + + +
visibilityacoustic
scopetraining
customizabilityrecommended
typeint
default400
+ +### K_step_infer + +Number of DDPM steps used during shallow diffusion inference. Normally set as same as [K_step](#K_step). + + + + + + + + +
visibilityacoustic
scopeinference
customizabilityrecommended
typeint
default400
constraintsShould be no larger than K_step.
+ +### log_interval + +Controls how often to log within training steps. Equivalent to `log_every_n_steps` in `lightning.pytorch.Trainer`. + + + + + + + +
visibilityall
scopetraining
customizabilitynormal
typeint
default100
+ +### lr_scheduler_args + +Arguments of learning rate scheduler. Keys will be used as keyword arguments of the `__init__()` method of [lr_scheduler_args.scheduler_cls](#lr_scheduler_argsscheduler_cls). + + + +
typedict
+ +### lr_scheduler_args.scheduler_cls + +Learning rate scheduler class name. + + + + + + + +
visibilityall
scopetraining
customizabilitynot recommended
typestr
defaulttorch.optim.lr_scheduler.StepLR
+ +### main_loss_log_norm + +Whether to use log-normalized weight for the main loss. This is similar to the method in the Stable Diffusion 3 paper [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://arxiv.org/abs/2403.03206). + + + + + + +
visibilityacoustic, variance
scopetraining
customizabilitynormal
typebool
+ +### main_loss_type + +Loss type of the main decoder/predictor. + + + + + + + + +
visibilityacoustic, variance
scopetraining
customizabilitynot recommended
typestr
defaultl2
constraintsChoose from 'l1', 'l2'.
+ +### max_batch_frames + +Maximum number of data frames in each training batch. Used to dynamically control the batch size. + + + + + + + +
visibilityacoustic, variance
scopetraining
customizabilityrecommended
typeint
default80000
+ +### max_batch_size + +The maximum training batch size. + + + + + + + +
visibilityall
scopetraining
customizabilityrecommended
typeint
default48
+ +### max_beta + +Max beta of the DDPM noise schedule. + + + + + + + +
visibilityacoustic, variance
scopenn, inference
customizabilitynormal
typefloat
default0.02
+ +### max_updates + +Stop training after this number of steps. Equivalent to `max_steps` in `lightning.pytorch.Trainer`. + + + + + + + +
visibilityall
scopetraining
customizabilityrecommended
typeint
default100000
+ +### max_val_batch_frames + +Maximum number of data frames in each validation batch. + + + + + + + +
visibilityacoustic, variance
scopetraining
customizabilitynormal
typeint
default60000
+ +### max_val_batch_size + +The maximum validation batch size. + + + + + + + +
visibilityall
scopetraining
customizabilitynormal
typeint
default1
+ +### mel_base + +The logarithmic base of mel spectrogram calculation. + +**WARNING: Since v2.4.0 release, this value is no longer configurable for preprocessing new datasets.** + + + + + + + +
visibilityacoustic
scopepreprocessing
customizabilityreserved
typestr
defaulte
+ +### mel_vmax + +Maximum mel spectrogram heatmap value for TensorBoard plotting. + + + + + + + +
visibilityacoustic
scopetraining
customizabilitynot recommended
typefloat
default4.
+ +### mel_vmin + +Minimum mel spectrogram heatmap value for TensorBoard plotting. + + + + + + + +
visibilityacoustic
scopetraining
customizabilitynot recommended
typefloat
default-14.
+ +### melody_encoder_args + +Arguments for melody encoder. Available sub-keys: `hidden_size`, `enc_layers`, `enc_ffn_kernel_size`, `ffn_act`, `dropout`, `num_heads`, `use_pos_embed`, `rel_pos`. If either of the parameter does not exist in this configuration key, it inherits from the linguistic encoder. + + + +
typedict
+ +### merged_phoneme_groups + +Phoneme groups to merge. Each group is a phoneme name list. The merged phonemes share the same ID and thus the same phoneme embedding. + + + + + + + +
visibilityacoustic, variance
scopepreprocessing
customizabilityrequired
typelist
default[]
+ +### midi_smooth_width + +Length of sinusoidal smoothing convolution kernel (in seconds) on the step function representing MIDI sequence for base pitch calculation. + + + + + + + +
visibilityvariance
scopepreprocessing
customizabilitynormal
typefloat
default0.06
+ +### mix_ln_layer + +List of 0-based encoder layer indices where Mixed LayerNorm is applied. Only takes effect when [use_mix_ln](#use_mix_ln) is enabled. For each selected layer, both self-attention layer norm and FFN layer norm are replaced with `Mixed_LayerNorm` which conditions the normalization on speaker embedding. + + + + + + + +
visibilityacoustic
scopenn
customizabilitynormal
typeList[int]
default[0, 2]
+ +### nccl_p2p + +Whether to enable P2P when using NCCL as the backend. Turn it to `false` if the training process is stuck upon beginning. + + + + + + + +
visibilityall
scopetraining
customizabilitynormal
typebool
defaulttrue
+ +### num_ckpt_keep + +Number of newest checkpoints kept during training. + + + + + + + +
visibilityall
scopetraining
customizabilitynormal
typeint
default5
+ +### num_heads + +The number of attention heads of `torch.nn.MultiheadAttention` in FastSpeech2 encoder. + + + + + + + +
visibilityacoustic, variance
scopenn
customizabilitynot recommended
typeint
default2
+ +### num_lang + +Number of languages. This value is used to allocate language embeddings in the linguistic encoder. + + + + + + +
visibilityacoustic, variance
scopenn
customizabilityrequired
typeint
+ +### num_sanity_val_steps + +Number of sanity validation steps at the beginning. + + + + + + + +
visibilityall
scopetraining
customizabilityreserved
typeint
default1
+ +### num_spk + +Maximum number of speakers in multi-speaker models. + + + + + + + +
visibilityacoustic, variance
scopenn
customizabilityrequired
typeint
default1
+ +### num_valid_plots + +Number of validation plots in each validation. Plots will be chosen from the start of the validation set. + + + + + + + +
visibilityacoustic, variance
scopetraining
customizabilityrecommended
typeint
default10
+ +### optimizer_args + +Arguments of optimizer. Keys will be used as keyword arguments of the `__init__()` method of [optimizer_args.optimizer_cls](#optimizer_argsoptimizer_cls). + + + +
typedict
+ +### optimizer_args.optimizer_cls + +Optimizer class name. The following optimizers are currently recommended: + +- `torch.optim.AdamW` — Standard AdamW optimizer. Use with `adamw_args` for the weight decay setting. +- `modules.optimizer.muon.Muon_AdamW` — Chained optimizer that applies Muon (MomentUm Orthogonalized by Newton-schulz) to internal weight matrices (e.g. linear layers) and AdamW to other parameters (e.g. biases, embeddings). Configure via `muon_args` and `adamw_args` sub-keys under [optimizer_args](#optimizer_args). + + + + + + + +
visibilityall
scopetraining
customizabilityreserved
typestr
defaultmodules.optimizer.muon.Muon_AdamW
+ +### pe + +Pitch extraction algorithm type. + + + + + + + + +
visibilityall
scopepreprocessing
customizabilitynormal
typestr
defaultparselmouth
constraintsChoose from 'parselmouth', 'rmvpe', 'harvest'.
+ +### pe_ckpt + +Checkpoint or model path of NN-based pitch extractor. + + + + + + +
visibilityall
scopepreprocessing
customizabilitynormal
typestr
+ +### permanent_ckpt_interval + +The interval (in number of training steps) of permanent checkpoints. Permanent checkpoints will not be removed even if they are not the newest ones. + + + + + + +
visibilityall
scopetraining
typeint
default10000
+ +### permanent_ckpt_start + +Checkpoints will be marked as permanent every [permanent_ckpt_interval](#permanent_ckpt_interval) training steps after this number of training steps. + + + + + + +
visibilityall
scopetraining
typeint
default60000
+ +### pitch_prediction_args + +Arguments for pitch prediction. + + + +
typedict
+ +### pitch_prediction_args.backbone_args + +Equivalent to [backbone_args](#backbone_args) but only for the pitch predictor model. If not set, use the root backbone type. + + + +
visibilityvariance
+ +### pitch_prediction_args.backbone_type + +Equivalent to [backbone_type](#backbone_type) but only for the pitch predictor model. + + + + +
visibilityvariance
defaultlynxnet2
+ +### pitch_prediction_args.pitd_clip_max + +Maximum clipping value (in semitones) of pitch delta between actual pitch and base pitch. + + + + + + +
visibilityvariance
scopeinference
typefloat
default12.0
+ +### pitch_prediction_args.pitd_clip_min + +Minimum clipping value (in semitones) of pitch delta between actual pitch and base pitch. + + + + + + +
visibilityvariance
scopeinference
typefloat
default-12.0
+ +### pitch_prediction_args.pitd_norm_max + +Maximum pitch delta value in semitones used for normalization to [-1, 1]. + + + + + + + +
visibilityvariance
scopeinference
customizabilityrecommended
typefloat
default8.0
+ +### pitch_prediction_args.pitd_norm_min + +Minimum pitch delta value in semitones used for normalization to [-1, 1]. + + + + + + + +
visibilityvariance
scopeinference
customizabilityrecommended
typefloat
default-8.0
+ +### pitch_prediction_args.repeat_bins + +Number of repeating bins in the pitch predictor. + + + + + + + +
visibilityvariance
scopenn, inference
customizabilityrecommended
typeint
default64
+ +### pl_trainer_accelerator + +Type of Lightning trainer hardware accelerator. + + + + + + + + +
visibilityall
scopetraining
customizabilitynot recommended
typestr
defaultauto
constraintsSee Accelerator — PyTorch Lightning 2.X.X documentation for available values.
+ +### pl_trainer_devices + +To determine on which device(s) model should be trained. + +'auto' will utilize all visible devices defined with the `CUDA_VISIBLE_DEVICES` environment variable, or utilize all available devices if that variable is not set. Otherwise, it behaves like `CUDA_VISIBLE_DEVICES` which can filter out visible devices. + + + + + + + +
visibilityall
scopetraining
customizabilitynot recommended
typestr
defaultauto
+ +### pl_trainer_precision + +The computation precision of training. + + + + + + + + +
visibilityall
scopetraining
customizabilitynormal
typestr
default16-mixed
constraintsChoose from '32-true', 'bf16-mixed', '16-mixed'. See more possible values at Trainer — PyTorch Lightning 2.X.X documentation.
+ +### pl_trainer_num_nodes + +Number of nodes in the training cluster of Lightning trainer. + + + + + + + +
visibilityall
scopetraining
customizabilityreserved
typeint
default1
+ +### pl_trainer_strategy + +Arguments of Lightning Strategy. Values will be used as keyword arguments when constructing the Strategy object. + + + +
typedict
+ +### pl_trainer_strategy.name + +Strategy name for the Lightning trainer. + + + + + + + +
visibilityall
scopetraining
customizabilityreserved
typestr
defaultauto
+ +### predict_breathiness + +Whether to enable breathiness prediction. + + + + + + + +
visibilityvariance
scopenn, preprocessing, training, inference
customizabilityrecommended
typebool
defaultfalse
+ +### predict_dur + +Whether to enable phoneme duration prediction. + + + + + + + +
visibilityvariance
scopenn, preprocessing, training, inference
customizabilityrecommended
typebool
defaulttrue
+ +### predict_energy + +Whether to enable energy prediction. + + + + + + + +
visibilityvariance
scopenn, preprocessing, training, inference
customizabilityrecommended
typebool
defaultfalse
+ +### predict_pitch + +Whether to enable pitch prediction. + + + + + + + +
visibilityvariance
scopenn, preprocessing, training, inference
customizabilityrecommended
typebool
defaulttrue
+ +### predict_tension + +Whether to enable tension prediction. + + + + + + + +
visibilityvariance
scopenn, preprocessing, training, inference
customizabilityrecommended
typebool
defaultfalse
+ +### predict_voicing + +Whether to enable voicing prediction. + + + + + + + +
visibilityvariance
scopenn, preprocessing, training, inference
customizabilityrecommended
typebool
defaultfalse
+ +### rel_pos + +Whether to use relative positional encoding in FastSpeech2 module. + + + + + + + +
visibilityacoustic, variance
scopenn
customizabilitynot recommended
typeboolean
defaulttrue
+ +### rope_interleaved + +Whether to use the interleaved (alternating) layout for RoPE (Rotary Positional Encoding) in the encoder self-attention. When set to `false`, the non-interleaved (contiguous half-real-half-imaginary) layout is used instead. + + + + + + + +
visibilityacoustic, variance
scopenn
customizabilitynot recommended
typebool
defaultfalse
+ +### sampler_frame_count_grid + +The batch sampler applies an algorithm called _sorting by similar length_ when collecting batches. Data samples are first grouped by their approximate lengths before they get shuffled within each group. Assume this value is set to $L_{grid}$, the approximate length of a data sample with length $L_{real}$ can be calculated through the following expression: + +$$ +L_{approx} = \lfloor\frac{L_{real}}{L_{grid}}\rfloor\cdot L_{grid} +$$ + +Training performance on some datasets may be very sensitive to this value. Change it to 1 (completely sorted by length without shuffling) to get the best performance in theory. + + + + + + + +
visibilityacoustic, variance
scopetraining
customizabilitynormal
typeint
default6
+ +### sampling_algorithm + +The algorithm to solve the ODE of Rectified Flow. The following methods are currently available: + +- Euler: The Euler method. +- Runge-Kutta (order 2): The 2nd-order Runge-Kutta method. +- Runge-Kutta (order 4): The 4th-order Runge-Kutta method. +- Runge-Kutta (order 5): The 5th-order Runge-Kutta method. + + + + + + + + +
visibilityacoustic, variance
scopeinference
customizabilitynormal
typestr
defaulteuler
constraintsChoose from 'euler', 'rk2', 'rk4', 'rk5'.
+ +### sampling_steps + +The total sampling steps to solve the ODE of Rectified Flow. Note that this value may not equal to NFE (Number of Function Evaluations) because some methods may require more than one function evaluation per step. + + + + + + + +
visibilityacoustic, variance
scopeinference
customizabilitynormal
typeint
default20
+ +### schedule_type + +The DDPM schedule type. + + + + + + + + +
visibilityacoustic, variance
scopenn
customizabilitynot recommended
typestr
defaultlinear
constraintsChoose from 'linear', 'cosine'.
+ +### shallow_diffusion_args + +Arguments for shallow diffusion. + + + +
typedict
+ +### shallow_diffusion_args.aux_decoder_arch + +Architecture type of the auxiliary decoder. + + + + + + + + +
visibilityacoustic
scopenn
customizabilityreserved
typestr
defaultconvnext
constraintsChoose from 'convnext'.
+ +### shallow_diffusion_args.aux_decoder_args + +Keyword arguments for dynamically constructing the auxiliary decoder. + + + + + +
visibilityacoustic
scopenn
typedict
+ +### shallow_diffusion_args.aux_decoder_grad + +Scale factor of the gradients from the auxiliary decoder to the encoder. + + + + + + + +
visibilityacoustic
scopetraining
customizabilitynormal
typefloat
default0.1
+ +### shallow_diffusion_args.train_aux_decoder + +Whether to forward and backward the auxiliary decoder during training. If set to `false`, the auxiliary decoder hangs in the memory and does not get any updates. + + + + + + + +
visibilityacoustic
scopetraining
customizabilitynormal
typebool
defaulttrue
+ +### shallow_diffusion_args.train_diffusion + +Whether to forward and backward the diffusion (main) decoder during training. If set to `false`, the diffusion decoder hangs in the memory and does not get any updates. + + + + + + + +
visibilityacoustic
scopetraining
customizabilitynormal
typebool
defaulttrue
+ +### shallow_diffusion_args.val_gt_start + +Whether to use the ground truth as `x_start` in the shallow diffusion validation process. If set to `true`, gaussian noise is added to the ground truth before shallow diffusion is performed; otherwise the noise is added to the output of the auxiliary decoder. This option is useful when the auxiliary decoder has not been trained yet. + + + + + + + +
visibilityacoustic
scopetraining
customizabilitynormal
typebool
defaultfalse
+ +### sort_by_len + +Whether to apply the _sorting by similar length_ algorithm described in [sampler_frame_count_grid](#sampler_frame_count_grid). Turning off this option may slow down training because sorting by length can better utilize the computing resources. + + + + + + + +
visibilityacoustic, variance
scopetraining
customizabilitynot recommended
typebool
defaulttrue
+ +### spec_min + +Minimum mel spectrogram value used for normalization to [-1, 1]. Different mel bins can have different minimum values. + + + + + + + +
visibilityacoustic
scopeinference
customizabilitynot recommended
typeList[float]
default[-12]
+ +### spec_max + +Maximum mel spectrogram value used for normalization to [-1, 1]. Different mel bins can have different maximum values. + + + + + + + +
visibilityacoustic
scopeinference
customizabilitynot recommended
typeList[float]
default[0.0]
+ +### T_start + +The starting value of time $t$ in the Rectified Flow ODE which applies on $t \in (T_{start}, 1)$. + + + + + + + +
visibilityacoustic
scopetraining
customizabilityrecommended
typefloat
default0.4
+ +### T_start_infer + +The starting value of time $t$ in the ODE during shallow Rectified Flow inference. Normally set as same as [T_start](#T_start). + + + + + + + + +
visibilityacoustic
scopeinference
customizabilityrecommended
typefloat
default0.4
constraintsShould be no less than T_start.
+ +### task_cls + +Task trainer class name. + + + + + + +
visibilityall
scopetraining
customizabilityreserved
typestr
+ +### tension_logit_max + +Maximum tension logit value used for normalization to [-1, 1]. Logit is the reverse function of Sigmoid: + +$$ +f(x) = \ln\frac{x}{1-x} +$$ + + + + + + + +
visibilityvariance
scopeinference
customizabilityrecommended
typefloat
default10.0
+ +### tension_logit_min + +Minimum tension logit value used for normalization to [-1, 1]. Logit is the reverse function of Sigmoid: + +$$ +f(x) = \ln\frac{x}{1-x} +$$ + + + + + + + +
visibilityvariance
scopeinference
customizabilityrecommended
typefloat
default-10.0
+ +### tension_smooth_width + +Length of sinusoidal smoothing convolution kernel (in seconds) on extracted tension curve. + + + + + + + +
visibilityacoustic, variance
scopepreprocessing
customizabilitynormal
typefloat
default0.06
+ +### time_scale_factor + +The scale factor that will be multiplied on the time $t$ of Rectified Flow before embedding into the model. + + + + + + + +
visibilityacoustic, variance
scopenn
customizabilitynot recommended
typefloat
default1000
+ +### timesteps + +Total number of DDPM steps. + + + + + + + +
visibilityacoustic, variance
scopenn
customizabilitynot recommended
typeint
default1000
+ +### use_breathiness_embed + +Whether to accept and embed breathiness values into the model. + + + + + + + +
visibilityacoustic
scopenn, preprocessing, inference
customizabilityrecommended
typeboolean
defaultfalse
+ +### use_energy_embed + +Whether to accept and embed energy values into the model. + + + + + + + +
visibilityacoustic
scopenn, preprocessing, inference
customizabilityrecommended
typeboolean
defaultfalse
+ +### use_glide_embed + +Whether to accept and embed glide types in melody encoder. + + + + + + + + +
visibilityvariance
scopenn, preprocessing, inference
customizabilityrecommended
typeboolean
defaultfalse
constraintsOnly take affects when melody encoder is enabled.
+ +### use_key_shift_embed + +Whether to embed key shifting values introduced by random pitch shifting augmentation. + + + + + + + + +
visibilityacoustic
scopenn, preprocessing, inference
customizabilityrecommended
typeboolean
defaultfalse
constraintsMust be true if random pitch shifting is enabled.
+ +### use_lang_id + +Whether to embed the language ID from a multilingual dataset. This option only takes effect for those cross-lingual phonemes in the merged groups. + + + + + + + +
visibilityacoustic, variance
scopenn, preprocessing, inference
customizabilityrecommended
typebool
defaultfalse
+ +### use_melody_encoder + +Whether to enable melody encoder for the pitch predictor. + + + + + + + +
visibilityvariance
scopenn
customizabilityrecommended
typeboolean
defaultfalse
+ +### use_mix_ln + +Whether to use Mixed LayerNorm with speaker-conditioned mixup in the acoustic encoder. When enabled, encoder layers specified in [mix_ln_layer](#mix_ln_layer) use `Mixed_LayerNorm`, which mixes the standard layer normalization with a speaker-conditioned scale factor, allowing speaker identity to influence the normalization behavior. + + + + + + + +
visibilityacoustic
scopenn
customizabilitynormal
typebool
defaultfalse
+ +### use_pos_embed + +Whether to use SinusoidalPositionalEmbedding in FastSpeech2 encoder. + + + + + + + +
visibilityacoustic, variance
scopenn
customizabilitynot recommended
typeboolean
defaulttrue
+ +### use_rope + +Whether to use RoPE (Rotary Positional Encoding) in FastSpeech2 encoder. + + + + + + + +
visibilityacoustic, variance
scopenn
customizabilitynot recommended
typeboolean
defaulttrue
+ +### use_shallow_diffusion + +Whether to use shallow diffusion. + + + + + + + +
visibilityacoustic
scopenn, inference
customizabilityrecommended
typeboolean
defaultfalse
+ +### use_speed_embed + +Whether to embed speed values introduced by random time stretching augmentation. + + + + + + + +
visibilityacoustic
scopenn, preprocessing, inference
typeboolean
defaultfalse
constraintsMust be true if random time stretching is enabled.
+ +### use_spk_id + +Whether to embed the speaker ID from a multi-speaker dataset. + + + + + + + +
visibilityacoustic, variance
scopenn, preprocessing, inference
customizabilityrecommended
typebool
defaultfalse
+ +### use_stretch_embed + +Whether to accept and embed phoneme-level time stretching ratios into the acoustic encoder. The stretch ratio is computed by the `StretchRegulator` module, which measures how much each mel frame is stretched or compressed relative to its corresponding phoneme's average duration. When random time stretching augmentation is enabled, this embedding helps the model condition on the actual stretch applied during data augmentation. + + + + + + + +
visibilityacoustic, variance
scopenn, preprocessing, inference
customizabilitynot recommended
typebool
defaulttrue for acoustic, false for variance
+ +### use_tension_embed + +Whether to accept and embed tension values into the model. + + + + + + + +
visibilityacoustic
scopenn, preprocessing, inference
customizabilityrecommended
typeboolean
defaultfalse
+ +### use_variance_scaling + +Whether to apply log-domain scaling to duration and MIDI embeddings to compress their dynamic range. When enabled, phoneme duration values are embedded in log space via `log(1 + dur)`, and MIDI note numbers are normalized by 1/128. This scaling helps the model handle the wide range of duration and MIDI values more stably during training and inference. + + + + + + + +
visibilityacoustic, variance
scopenn, inference
customizabilitynot recommended
typebool
defaulttrue
+ +### use_voicing_embed + +Whether to accept and embed voicing values into the model. + + + + + + + +
visibilityacoustic
scopenn, preprocessing, inference
customizabilityrecommended
typeboolean
defaultfalse
+ +### val_check_interval + +Interval (in number of training steps) between validation checks. + + + + + + + +
visibilityall
scopetraining
customizabilityrecommended
typeint
default4000
+ +### val_with_vocoder + +Whether to load and use the vocoder to generate audio during validation. Validation audio will not be available if this option is disabled. + + + + + + + +
visibilityacoustic
scopetraining
customizabilitynormal
typebool
defaulttrue
+ +### variances_prediction_args + +Arguments for prediction of variance parameters other than pitch, like energy, breathiness, etc. + + + +
typedict
+ +### variances_prediction_args.backbone_args + +Equivalent to [backbone_args](#backbone_args) but only for the multi-variance predictor. + + + +
visibilityvariance
+ +### variances_prediction_args.backbone_type + +Equivalent to [backbone_type](#backbone_type) but only for the multi-variance predictor model. If not set, use the root backbone type. + + + + +
visibilityvariance
defaultlynxnet2
+ +### variances_prediction_args.total_repeat_bins + +Total number of repeating bins in the multi-variance predictor. Repeating bins are distributed evenly to each variance parameter. + + + + + + + +
visibilityvariance
scopenn, inference
customizabilityrecommended
typeint
default72
+ +### vocoder + +The vocoder class name. + + + + + + + +
visibilityacoustic
scopepreprocessing, training, inference
customizabilitynormal
typestr
defaultNsfHifiGAN
+ +### vocoder_ckpt + +Path of the vocoder model. + + + + + + + +
visibilityacoustic
scopepreprocessing, training, inference
customizabilitynormal
typestr
defaultcheckpoints/nsf_hifigan/model
+ +### voicing_db_max + +Maximum voicing value in dB used for normalization to [-1, 1]. + + + + + + + +
visibilityvariance
scopeinference
customizabilityrecommended
typefloat
default-20.0
+ +### voicing_db_min + +Minimum voicing value in dB used for normalization to [-1, 1]. + + + + + + + +
visibilityacoustic, variance
scopeinference
customizabilityrecommended
typefloat
default-96.0
+ +### voicing_smooth_width + +Length of sinusoidal smoothing convolution kernel (in seconds) on extracted voicing curve. + + + + + + + +
visibilityacoustic, variance
scopepreprocessing
customizabilitynormal
typefloat
default0.06
+ +### win_size + +Window size for mel or feature extraction. + + + + + + + +
visibilityacoustic, variance
scopepreprocessing
customizabilityreserved
typeint
default2048
diff --git a/docs/GettingStarted.md b/docs/GettingStarted.md new file mode 100644 index 0000000..1f7ef8f --- /dev/null +++ b/docs/GettingStarted.md @@ -0,0 +1,152 @@ +# Getting Started + +## Installation + +### Environments and dependencies + +DiffSinger requires Python 3.10 or later. We strongly recommend you create a virtual environment via Conda, venv or uv before installing dependencies. + +1. Install The latest PyTorch following the [official instructions](https://pytorch.org/get-started/locally/) according to your OS and hardware. We recommend using the latest stable release that is >= 2.4.0. + +2. Install other dependencies via the following command: + + ```bash + pip install -r requirements.txt + ``` + +### Concepts and materials + +Before you proceed, it is necessary to understand some fundamental concepts in this repository and prepare some materials and assets. See [fundamental concepts and materials](BestPractices.md#fundamental-concepts-and-materials) for detailed information. + +## Configuration + +Every model needs a configuration file to run preprocessing, training, inference and deployment. Templates of configurations files are in [configs/templates](../configs/templates). Please **copy** the templates to your own data directory before you edit them. + +Before you continue, it is highly recommended to read through [Best Practices](BestPractices.md), which is a more detailed tutorial on how to configure your experiments. + +For more details about configurable parameters, see [Configuration Schemas](ConfigurationSchemas.md). + +> Tips: to see which parameters are required or recommended to be edited, you can search by _customizability_ in the configuration schemas. + +## Preprocessing + +Raw data pieces and transcriptions should be binarized into dataset files before training. Before doing this step, please ensure all required configurations like `raw_data_dir` and `binary_data_dir` are set properly, and all your desired functionalities and features are enabled and configured. + +Assume that you have a configuration file called `my_config.yaml`. Run: + +```bash +python scripts/binarize.py --config my_config.yaml +``` + +Preprocessing can be accelerated through multiprocessing. See [binarization_args.num_workers](ConfigurationSchemas.md#binarization_args.num_workers) for more explanations. + +## Training + +Assume that you have a configuration file called `my_config.yaml` and the name of your model is `my_experiment`. Run: + +```bash +python scripts/train.py --config my_config.yaml --exp_name my_experiment --reset +``` + +Checkpoints will be saved at the `checkpoints/my_experiment/` directory. When interrupting the program and running the above command again, the training resumes automatically from the latest checkpoint. + +For more suggestions related to training performance, see [performance tuning](BestPractices.md#performance-tuning). + +### TensorBoard + +Run the following command to start the TensorBoard: + +```bash +tensorboard --logdir checkpoints/ +``` + +> NOTICE +> +> If you are training a model with multiple GPUs (DDP), please add `--reload_multifile=true` option when launching TensorBoard, otherwise it may not update properly. + +## Inference + +Inference of DiffSinger is based on DS files. Assume that you have a DS file named `my_song.ds` and your model is named `my_experiment`. + +If your model is a variance model, run: + +```bash +python scripts/infer.py variance my_song.ds --exp my_experiment +``` + +or run + +```bash +python scripts/infer.py variance --help +``` + +for more configurable options. + +If your model is an acoustic model, run: + +```bash +python scripts/infer.py acoustic my_song.ds --exp my_experiment +``` + +or run + +```bash +python scripts/infer.py acoustic --help +``` + +for more configurable options. + +## Deployment + +DiffSinger uses [ONNX](https://onnx.ai/) as the deployment format. + +Assume that you have a model named `my_experiment`. + +If your model is a variance model, run: + +```bash +python scripts/export.py variance --exp my_experiment +``` + +or run + +```bash +python scripts/export.py variance --help +``` + +for more configurable options. + +If your model is an acoustic model, run: + +```bash +python scripts/export.py acoustic --exp my_experiment +``` + +or run + +```bash +python scripts/export.py acoustic --help +``` + +for more configurable options. + +To export an NSF-HiFiGAN vocoder checkpoint, run: + +```bash +python scripts/export.py nsf-hifigan --config CONFIG --ckpt CKPT +``` + +where `CONFIG` is a configuration file that has configured the same mel parameters as the vocoder (can be configs/acoustic.yaml for most cases) and `CKPT` is the path of the checkpoint to be exported. + +For more configurable options, run + +```bash +python scripts/export.py nsf-hifigan --help +``` + +## Other utilities + +There are other useful CLI tools in the [scripts/](../scripts) directory not mentioned above: + +- drop_spk.py - delete speaker embeddings from checkpoints (for data security reasons when distributing models) +- vocoder.py - bypass the acoustic model and only run the vocoder on given mel-spectrograms diff --git a/docs/resources/arch-acoustic.drawio b/docs/resources/arch-acoustic.drawio new file mode 100644 index 0000000..305d9e1 --- /dev/null +++ b/docs/resources/arch-acoustic.drawio @@ -0,0 +1,179 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/resources/arch-acoustic.jpg b/docs/resources/arch-acoustic.jpg new file mode 100644 index 0000000..f1ce4a1 Binary files /dev/null and b/docs/resources/arch-acoustic.jpg differ diff --git a/docs/resources/arch-overview.drawio b/docs/resources/arch-overview.drawio new file mode 100644 index 0000000..a7b9a9e --- /dev/null +++ b/docs/resources/arch-overview.drawio @@ -0,0 +1,123 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/resources/arch-overview.jpg b/docs/resources/arch-overview.jpg new file mode 100644 index 0000000..2acae69 Binary files /dev/null and b/docs/resources/arch-overview.jpg differ diff --git a/docs/resources/arch-variance.drawio b/docs/resources/arch-variance.drawio new file mode 100644 index 0000000..8761b79 --- /dev/null +++ b/docs/resources/arch-variance.drawio @@ -0,0 +1,399 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/resources/arch-variance.jpg b/docs/resources/arch-variance.jpg new file mode 100644 index 0000000..b58f898 Binary files /dev/null and b/docs/resources/arch-variance.jpg differ diff --git a/docs/resources/phoneme-distribution.jpg b/docs/resources/phoneme-distribution.jpg new file mode 100644 index 0000000..394de37 Binary files /dev/null and b/docs/resources/phoneme-distribution.jpg differ diff --git a/inference/dpm_solver_pytorch.py b/inference/dpm_solver_pytorch.py new file mode 100644 index 0000000..23e4d3c --- /dev/null +++ b/inference/dpm_solver_pytorch.py @@ -0,0 +1,1305 @@ +import torch +import torch.nn.functional as F +import math + + +class NoiseScheduleVP: + def __init__( + self, + schedule='discrete', + betas=None, + alphas_cumprod=None, + continuous_beta_0=0.1, + continuous_beta_1=20., + dtype=torch.float32, + ): + """Create a wrapper class for the forward SDE (VP type). + + *** + Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t. + We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images. + *** + + The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ). + We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper). + Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have: + + log_alpha_t = self.marginal_log_mean_coeff(t) + sigma_t = self.marginal_std(t) + lambda_t = self.marginal_lambda(t) + + Moreover, as lambda(t) is an invertible function, we also support its inverse function: + + t = self.inverse_lambda(lambda_t) + + =============================================================== + + We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]). + + 1. For discrete-time DPMs: + + For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by: + t_i = (i + 1) / N + e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1. + We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3. + + Args: + betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details) + alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details) + + Note that we always have alphas_cumprod = cumprod(1 - betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`. + + **Important**: Please pay special attention for the args for `alphas_cumprod`: + The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that + q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ). + Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have + alpha_{t_n} = \sqrt{\hat{alpha_n}}, + and + log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}). + + + 2. For continuous-time DPMs: + + We support the linear VPSDE for the continuous time setting. The hyperparameters for the noise + schedule are the default settings in Yang Song's ScoreSDE: + + Args: + beta_min: A `float` number. The smallest beta for the linear schedule. + beta_max: A `float` number. The largest beta for the linear schedule. + T: A `float` number. The ending time of the forward process. + + =============================================================== + + Args: + schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs, + 'linear' for continuous-time DPMs. + Returns: + A wrapper object of the forward SDE (VP type). + + =============================================================== + + Example: + + # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1): + >>> ns = NoiseScheduleVP('discrete', betas=betas) + + # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1): + >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod) + + # For continuous-time DPMs (VPSDE), linear schedule: + >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.) + + """ + + if schedule not in ['discrete', 'linear']: + raise ValueError("Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear'".format(schedule)) + + self.schedule = schedule + if schedule == 'discrete': + if betas is not None: + log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0) + else: + assert alphas_cumprod is not None + log_alphas = 0.5 * torch.log(alphas_cumprod) + self.T = 1. + self.log_alpha_array = self.numerical_clip_alpha(log_alphas).reshape((1, -1,)).to(dtype=dtype) + self.total_N = self.log_alpha_array.shape[1] + self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1)).to(dtype=dtype) + else: + self.T = 1. + self.total_N = 1000 + self.beta_0 = continuous_beta_0 + self.beta_1 = continuous_beta_1 + + def numerical_clip_alpha(self, log_alphas, clipped_lambda=-5.1): + """ + For some beta schedules such as cosine schedule, the log-SNR has numerical isssues. + We clip the log-SNR near t=T within -5.1 to ensure the stability. + Such a trick is very useful for diffusion models with the cosine schedule, such as i-DDPM, guided-diffusion and GLIDE. + """ + log_sigmas = 0.5 * torch.log(1. - torch.exp(2. * log_alphas)) + lambs = log_alphas - log_sigmas + idx = torch.searchsorted(torch.flip(lambs, [0]), clipped_lambda) + if idx > 0: + log_alphas = log_alphas[:-idx] + return log_alphas + + def marginal_log_mean_coeff(self, t): + """ + Compute log(alpha_t) of a given continuous-time label t in [0, T]. + """ + if self.schedule == 'discrete': + return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device), self.log_alpha_array.to(t.device)).reshape((-1)) + elif self.schedule == 'linear': + return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0 + + def marginal_alpha(self, t): + """ + Compute alpha_t of a given continuous-time label t in [0, T]. + """ + return torch.exp(self.marginal_log_mean_coeff(t)) + + def marginal_std(self, t): + """ + Compute sigma_t of a given continuous-time label t in [0, T]. + """ + return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t))) + + def marginal_lambda(self, t): + """ + Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T]. + """ + log_mean_coeff = self.marginal_log_mean_coeff(t) + log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff)) + return log_mean_coeff - log_std + + def inverse_lambda(self, lamb): + """ + Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t. + """ + if self.schedule == 'linear': + tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb)) + Delta = self.beta_0**2 + tmp + return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0) + elif self.schedule == 'discrete': + log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb) + t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]), torch.flip(self.t_array.to(lamb.device), [1])) + return t.reshape((-1,)) + + +def model_wrapper( + model, + noise_schedule, + model_type="noise", + model_kwargs={}, + guidance_type="uncond", + condition=None, + unconditional_condition=None, + guidance_scale=1., + classifier_fn=None, + classifier_kwargs={}, +): + """Create a wrapper function for the noise prediction model. + + DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to + firstly wrap the model function to a noise prediction model that accepts the continuous time as the input. + + We support four types of the diffusion model by setting `model_type`: + + 1. "noise": noise prediction model. (Trained by predicting noise). + + 2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0). + + 3. "v": velocity prediction model. (Trained by predicting the velocity). + The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2]. + + [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models." + arXiv preprint arXiv:2202.00512 (2022). + [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models." + arXiv preprint arXiv:2210.02303 (2022). + + 4. "score": marginal score function. (Trained by denoising score matching). + Note that the score function and the noise prediction model follows a simple relationship: + ``` + noise(x_t, t) = -sigma_t * score(x_t, t) + ``` + + We support three types of guided sampling by DPMs by setting `guidance_type`: + 1. "uncond": unconditional sampling by DPMs. + The input `model` has the following format: + `` + model(x, t_input, **model_kwargs) -> noise | x_start | v | score + `` + + 2. "classifier": classifier guidance sampling [3] by DPMs and another classifier. + The input `model` has the following format: + `` + model(x, t_input, **model_kwargs) -> noise | x_start | v | score + `` + + The input `classifier_fn` has the following format: + `` + classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond) + `` + + [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis," + in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794. + + 3. "classifier-free": classifier-free guidance sampling by conditional DPMs. + The input `model` has the following format: + `` + model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score + `` + And if cond == `unconditional_condition`, the model output is the unconditional DPM output. + + [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance." + arXiv preprint arXiv:2207.12598 (2022). + + + The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999) + or continuous-time labels (i.e. epsilon to T). + + We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise: + `` + def model_fn(x, t_continuous) -> noise: + t_input = get_model_input_time(t_continuous) + return noise_pred(model, x, t_input, **model_kwargs) + `` + where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver. + + =============================================================== + + Args: + model: A diffusion model with the corresponding format described above. + noise_schedule: A noise schedule object, such as NoiseScheduleVP. + model_type: A `str`. The parameterization type of the diffusion model. + "noise" or "x_start" or "v" or "score". + model_kwargs: A `dict`. A dict for the other inputs of the model function. + guidance_type: A `str`. The type of the guidance for sampling. + "uncond" or "classifier" or "classifier-free". + condition: A pytorch tensor. The condition for the guided sampling. + Only used for "classifier" or "classifier-free" guidance type. + unconditional_condition: A pytorch tensor. The condition for the unconditional sampling. + Only used for "classifier-free" guidance type. + guidance_scale: A `float`. The scale for the guided sampling. + classifier_fn: A classifier function. Only used for the classifier guidance. + classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function. + Returns: + A noise prediction model that accepts the noised data and the continuous time as the inputs. + """ + + def get_model_input_time(t_continuous): + """ + Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time. + For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N]. + For continuous-time DPMs, we just use `t_continuous`. + """ + if noise_schedule.schedule == 'discrete': + return (t_continuous - 1. / noise_schedule.total_N) * noise_schedule.total_N + else: + return t_continuous + + def noise_pred_fn(x, t_continuous, cond=None): + t_input = get_model_input_time(t_continuous) + if cond is None: + output = model(x, t_input, **model_kwargs) + else: + output = model(x, t_input, cond, **model_kwargs) + if model_type == "noise": + return output + elif model_type == "x_start": + alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous) + return (x - expand_dims(alpha_t, x.dim()) * output) / expand_dims(sigma_t, x.dim()) + elif model_type == "v": + alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous) + return expand_dims(alpha_t, x.dim()) * output + expand_dims(sigma_t, x.dim()) * x + elif model_type == "score": + sigma_t = noise_schedule.marginal_std(t_continuous) + return -expand_dims(sigma_t, x.dim()) * output + + def cond_grad_fn(x, t_input): + """ + Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t). + """ + with torch.enable_grad(): + x_in = x.detach().requires_grad_(True) + log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs) + return torch.autograd.grad(log_prob.sum(), x_in)[0] + + def model_fn(x, t_continuous): + """ + The noise predicition model function that is used for DPM-Solver. + """ + if guidance_type == "uncond": + return noise_pred_fn(x, t_continuous) + elif guidance_type == "classifier": + assert classifier_fn is not None + t_input = get_model_input_time(t_continuous) + cond_grad = cond_grad_fn(x, t_input) + sigma_t = noise_schedule.marginal_std(t_continuous) + noise = noise_pred_fn(x, t_continuous) + return noise - guidance_scale * expand_dims(sigma_t, x.dim()) * cond_grad + elif guidance_type == "classifier-free": + if guidance_scale == 1. or unconditional_condition is None: + return noise_pred_fn(x, t_continuous, cond=condition) + else: + x_in = torch.cat([x] * 2) + t_in = torch.cat([t_continuous] * 2) + c_in = torch.cat([unconditional_condition, condition]) + noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2) + return noise_uncond + guidance_scale * (noise - noise_uncond) + + assert model_type in ["noise", "x_start", "v", "score"] + assert guidance_type in ["uncond", "classifier", "classifier-free"] + return model_fn + + +class DPM_Solver: + def __init__( + self, + model_fn, + noise_schedule, + algorithm_type="dpmsolver++", + correcting_x0_fn=None, + correcting_xt_fn=None, + thresholding_max_val=1., + dynamic_thresholding_ratio=0.995, + ): + """Construct a DPM-Solver. + + We support both DPM-Solver (`algorithm_type="dpmsolver"`) and DPM-Solver++ (`algorithm_type="dpmsolver++"`). + + We also support the "dynamic thresholding" method in Imagen[1]. For pixel-space diffusion models, you + can set both `algorithm_type="dpmsolver++"` and `correcting_x0_fn="dynamic_thresholding"` to use the + dynamic thresholding. The "dynamic thresholding" can greatly improve the sample quality for pixel-space + DPMs with large guidance scales. Note that the thresholding method is **unsuitable** for latent-space + DPMs (such as stable-diffusion). + + To support advanced algorithms in image-to-image applications, we also support corrector functions for + both x0 and xt. + + Args: + model_fn: A noise prediction model function which accepts the continuous-time input (t in [epsilon, T]): + `` + def model_fn(x, t_continuous): + return noise + `` + The shape of `x` is `(batch_size, **shape)`, and the shape of `t_continuous` is `(batch_size,)`. + noise_schedule: A noise schedule object, such as NoiseScheduleVP. + algorithm_type: A `str`. Either "dpmsolver" or "dpmsolver++". + correcting_x0_fn: A `str` or a function with the following format: + ``` + def correcting_x0_fn(x0, t): + x0_new = ... + return x0_new + ``` + This function is to correct the outputs of the data prediction model at each sampling step. e.g., + ``` + x0_pred = data_pred_model(xt, t) + if correcting_x0_fn is not None: + x0_pred = correcting_x0_fn(x0_pred, t) + xt_1 = update(x0_pred, xt, t) + ``` + If `correcting_x0_fn="dynamic_thresholding"`, we use the dynamic thresholding proposed in Imagen[1]. + correcting_xt_fn: A function with the following format: + ``` + def correcting_xt_fn(xt, t, step): + x_new = ... + return x_new + ``` + This function is to correct the intermediate samples xt at each sampling step. e.g., + ``` + xt = ... + xt = correcting_xt_fn(xt, t, step) + ``` + thresholding_max_val: A `float`. The max value for thresholding. + Valid only when use `dpmsolver++` and `correcting_x0_fn="dynamic_thresholding"`. + dynamic_thresholding_ratio: A `float`. The ratio for dynamic thresholding (see Imagen[1] for details). + Valid only when use `dpmsolver++` and `correcting_x0_fn="dynamic_thresholding"`. + + [1] Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, + Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al. Photorealistic text-to-image diffusion models + with deep language understanding. arXiv preprint arXiv:2205.11487, 2022b. + """ + self.model = lambda x, t: model_fn(x, t.expand((x.shape[0]))) + self.noise_schedule = noise_schedule + assert algorithm_type in ["dpmsolver", "dpmsolver++"] + self.algorithm_type = algorithm_type + if correcting_x0_fn == "dynamic_thresholding": + self.correcting_x0_fn = self.dynamic_thresholding_fn + else: + self.correcting_x0_fn = correcting_x0_fn + self.correcting_xt_fn = correcting_xt_fn + self.dynamic_thresholding_ratio = dynamic_thresholding_ratio + self.thresholding_max_val = thresholding_max_val + + def dynamic_thresholding_fn(self, x0, t): + """ + The dynamic thresholding method. + """ + dims = x0.dim() + p = self.dynamic_thresholding_ratio + s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1) + s = expand_dims(torch.maximum(s, self.thresholding_max_val * torch.ones_like(s).to(s.device)), dims) + x0 = torch.clamp(x0, -s, s) / s + return x0 + + def noise_prediction_fn(self, x, t): + """ + Return the noise prediction model. + """ + return self.model(x, t) + + def data_prediction_fn(self, x, t): + """ + Return the data prediction model (with corrector). + """ + noise = self.noise_prediction_fn(x, t) + alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t) + x0 = (x - sigma_t * noise) / alpha_t + if self.correcting_x0_fn is not None: + x0 = self.correcting_x0_fn(x0, t) + return x0 + + def model_fn(self, x, t): + """ + Convert the model to the noise prediction model or the data prediction model. + """ + if self.algorithm_type == "dpmsolver++": + return self.data_prediction_fn(x, t) + else: + return self.noise_prediction_fn(x, t) + + def get_time_steps(self, skip_type, t_T, t_0, N, device): + """Compute the intermediate time steps for sampling. + + Args: + skip_type: A `str`. The type for the spacing of the time steps. We support three types: + - 'logSNR': uniform logSNR for the time steps. + - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.) + - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.) + t_T: A `float`. The starting time of the sampling (default is T). + t_0: A `float`. The ending time of the sampling (default is epsilon). + N: A `int`. The total number of the spacing of the time steps. + device: A torch device. + Returns: + A pytorch tensor of the time steps, with the shape (N + 1,). + """ + if skip_type == 'logSNR': + lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device)) + lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device)) + logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device) + return self.noise_schedule.inverse_lambda(logSNR_steps) + elif skip_type == 'time_uniform': + return torch.linspace(t_T, t_0, N + 1).to(device) + elif skip_type == 'time_quadratic': + t_order = 2 + t = torch.linspace(t_T**(1. / t_order), t_0**(1. / t_order), N + 1).pow(t_order).to(device) + return t + else: + raise ValueError("Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type)) + + def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device): + """ + Get the order of each step for sampling by the singlestep DPM-Solver. + + We combine both DPM-Solver-1,2,3 to use all the function evaluations, which is named as "DPM-Solver-fast". + Given a fixed number of function evaluations by `steps`, the sampling procedure by DPM-Solver-fast is: + - If order == 1: + We take `steps` of DPM-Solver-1 (i.e. DDIM). + - If order == 2: + - Denote K = (steps // 2). We take K or (K + 1) intermediate time steps for sampling. + - If steps % 2 == 0, we use K steps of DPM-Solver-2. + - If steps % 2 == 1, we use K steps of DPM-Solver-2 and 1 step of DPM-Solver-1. + - If order == 3: + - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling. + - If steps % 3 == 0, we use (K - 2) steps of DPM-Solver-3, and 1 step of DPM-Solver-2 and 1 step of DPM-Solver-1. + - If steps % 3 == 1, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-1. + - If steps % 3 == 2, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-2. + + ============================================ + Args: + order: A `int`. The max order for the solver (2 or 3). + steps: A `int`. The total number of function evaluations (NFE). + skip_type: A `str`. The type for the spacing of the time steps. We support three types: + - 'logSNR': uniform logSNR for the time steps. + - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.) + - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.) + t_T: A `float`. The starting time of the sampling (default is T). + t_0: A `float`. The ending time of the sampling (default is epsilon). + device: A torch device. + Returns: + orders: A list of the solver order of each step. + """ + if order == 3: + K = steps // 3 + 1 + if steps % 3 == 0: + orders = [3,] * (K - 2) + [2, 1] + elif steps % 3 == 1: + orders = [3,] * (K - 1) + [1] + else: + orders = [3,] * (K - 1) + [2] + elif order == 2: + if steps % 2 == 0: + K = steps // 2 + orders = [2,] * K + else: + K = steps // 2 + 1 + orders = [2,] * (K - 1) + [1] + elif order == 1: + K = 1 + orders = [1,] * steps + else: + raise ValueError("'order' must be '1' or '2' or '3'.") + if skip_type == 'logSNR': + # To reproduce the results in DPM-Solver paper + timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device) + else: + timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[torch.cumsum(torch.tensor([0,] + orders), 0).to(device)] + return timesteps_outer, orders + + def denoise_to_zero_fn(self, x, s): + """ + Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization. + """ + return self.data_prediction_fn(x, s) + + def dpm_solver_first_update(self, x, s, t, model_s=None, return_intermediate=False): + """ + DPM-Solver-1 (equivalent to DDIM) from time `s` to time `t`. + + Args: + x: A pytorch tensor. The initial value at time `s`. + s: A pytorch tensor. The starting time, with the shape (1,). + t: A pytorch tensor. The ending time, with the shape (1,). + model_s: A pytorch tensor. The model function evaluated at time `s`. + If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it. + return_intermediate: A `bool`. If true, also return the model value at time `s`. + Returns: + x_t: A pytorch tensor. The approximated solution at time `t`. + """ + ns = self.noise_schedule + dims = x.dim() + lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t) + h = lambda_t - lambda_s + log_alpha_s, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(t) + sigma_s, sigma_t = ns.marginal_std(s), ns.marginal_std(t) + alpha_t = torch.exp(log_alpha_t) + + if self.algorithm_type == "dpmsolver++": + phi_1 = torch.expm1(-h) + if model_s is None: + model_s = self.model_fn(x, s) + x_t = ( + sigma_t / sigma_s * x + - alpha_t * phi_1 * model_s + ) + if return_intermediate: + return x_t, {'model_s': model_s} + else: + return x_t + else: + phi_1 = torch.expm1(h) + if model_s is None: + model_s = self.model_fn(x, s) + x_t = ( + torch.exp(log_alpha_t - log_alpha_s) * x + - (sigma_t * phi_1) * model_s + ) + if return_intermediate: + return x_t, {'model_s': model_s} + else: + return x_t + + def singlestep_dpm_solver_second_update(self, x, s, t, r1=0.5, model_s=None, return_intermediate=False, solver_type='dpmsolver'): + """ + Singlestep solver DPM-Solver-2 from time `s` to time `t`. + + Args: + x: A pytorch tensor. The initial value at time `s`. + s: A pytorch tensor. The starting time, with the shape (1,). + t: A pytorch tensor. The ending time, with the shape (1,). + r1: A `float`. The hyperparameter of the second-order solver. + model_s: A pytorch tensor. The model function evaluated at time `s`. + If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it. + return_intermediate: A `bool`. If true, also return the model value at time `s` and `s1` (the intermediate time). + solver_type: either 'dpmsolver' or 'taylor'. The type for the high-order solvers. + The type slightly impacts the performance. We recommend to use 'dpmsolver' type. + Returns: + x_t: A pytorch tensor. The approximated solution at time `t`. + """ + if solver_type not in ['dpmsolver', 'taylor']: + raise ValueError("'solver_type' must be either 'dpmsolver' or 'taylor', got {}".format(solver_type)) + if r1 is None: + r1 = 0.5 + ns = self.noise_schedule + lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t) + h = lambda_t - lambda_s + lambda_s1 = lambda_s + r1 * h + s1 = ns.inverse_lambda(lambda_s1) + log_alpha_s, log_alpha_s1, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(s1), ns.marginal_log_mean_coeff(t) + sigma_s, sigma_s1, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(t) + alpha_s1, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_t) + + if self.algorithm_type == "dpmsolver++": + phi_11 = torch.expm1(-r1 * h) + phi_1 = torch.expm1(-h) + + if model_s is None: + model_s = self.model_fn(x, s) + x_s1 = ( + (sigma_s1 / sigma_s) * x + - (alpha_s1 * phi_11) * model_s + ) + model_s1 = self.model_fn(x_s1, s1) + if solver_type == 'dpmsolver': + x_t = ( + (sigma_t / sigma_s) * x + - (alpha_t * phi_1) * model_s + - (0.5 / r1) * (alpha_t * phi_1) * (model_s1 - model_s) + ) + elif solver_type == 'taylor': + x_t = ( + (sigma_t / sigma_s) * x + - (alpha_t * phi_1) * model_s + + (1. / r1) * (alpha_t * (phi_1 / h + 1.)) * (model_s1 - model_s) + ) + else: + phi_11 = torch.expm1(r1 * h) + phi_1 = torch.expm1(h) + + if model_s is None: + model_s = self.model_fn(x, s) + x_s1 = ( + torch.exp(log_alpha_s1 - log_alpha_s) * x + - (sigma_s1 * phi_11) * model_s + ) + model_s1 = self.model_fn(x_s1, s1) + if solver_type == 'dpmsolver': + x_t = ( + torch.exp(log_alpha_t - log_alpha_s) * x + - (sigma_t * phi_1) * model_s + - (0.5 / r1) * (sigma_t * phi_1) * (model_s1 - model_s) + ) + elif solver_type == 'taylor': + x_t = ( + torch.exp(log_alpha_t - log_alpha_s) * x + - (sigma_t * phi_1) * model_s + - (1. / r1) * (sigma_t * (phi_1 / h - 1.)) * (model_s1 - model_s) + ) + if return_intermediate: + return x_t, {'model_s': model_s, 'model_s1': model_s1} + else: + return x_t + + def singlestep_dpm_solver_third_update(self, x, s, t, r1=1./3., r2=2./3., model_s=None, model_s1=None, return_intermediate=False, solver_type='dpmsolver'): + """ + Singlestep solver DPM-Solver-3 from time `s` to time `t`. + + Args: + x: A pytorch tensor. The initial value at time `s`. + s: A pytorch tensor. The starting time, with the shape (1,). + t: A pytorch tensor. The ending time, with the shape (1,). + r1: A `float`. The hyperparameter of the third-order solver. + r2: A `float`. The hyperparameter of the third-order solver. + model_s: A pytorch tensor. The model function evaluated at time `s`. + If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it. + model_s1: A pytorch tensor. The model function evaluated at time `s1` (the intermediate time given by `r1`). + If `model_s1` is None, we evaluate the model at `s1`; otherwise we directly use it. + return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times). + solver_type: either 'dpmsolver' or 'taylor'. The type for the high-order solvers. + The type slightly impacts the performance. We recommend to use 'dpmsolver' type. + Returns: + x_t: A pytorch tensor. The approximated solution at time `t`. + """ + if solver_type not in ['dpmsolver', 'taylor']: + raise ValueError("'solver_type' must be either 'dpmsolver' or 'taylor', got {}".format(solver_type)) + if r1 is None: + r1 = 1. / 3. + if r2 is None: + r2 = 2. / 3. + ns = self.noise_schedule + lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t) + h = lambda_t - lambda_s + lambda_s1 = lambda_s + r1 * h + lambda_s2 = lambda_s + r2 * h + s1 = ns.inverse_lambda(lambda_s1) + s2 = ns.inverse_lambda(lambda_s2) + log_alpha_s, log_alpha_s1, log_alpha_s2, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(s1), ns.marginal_log_mean_coeff(s2), ns.marginal_log_mean_coeff(t) + sigma_s, sigma_s1, sigma_s2, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(s2), ns.marginal_std(t) + alpha_s1, alpha_s2, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_s2), torch.exp(log_alpha_t) + + if self.algorithm_type == "dpmsolver++": + phi_11 = torch.expm1(-r1 * h) + phi_12 = torch.expm1(-r2 * h) + phi_1 = torch.expm1(-h) + phi_22 = torch.expm1(-r2 * h) / (r2 * h) + 1. + phi_2 = phi_1 / h + 1. + phi_3 = phi_2 / h - 0.5 + + if model_s is None: + model_s = self.model_fn(x, s) + if model_s1 is None: + x_s1 = ( + (sigma_s1 / sigma_s) * x + - (alpha_s1 * phi_11) * model_s + ) + model_s1 = self.model_fn(x_s1, s1) + x_s2 = ( + (sigma_s2 / sigma_s) * x + - (alpha_s2 * phi_12) * model_s + + r2 / r1 * (alpha_s2 * phi_22) * (model_s1 - model_s) + ) + model_s2 = self.model_fn(x_s2, s2) + if solver_type == 'dpmsolver': + x_t = ( + (sigma_t / sigma_s) * x + - (alpha_t * phi_1) * model_s + + (1. / r2) * (alpha_t * phi_2) * (model_s2 - model_s) + ) + elif solver_type == 'taylor': + D1_0 = (1. / r1) * (model_s1 - model_s) + D1_1 = (1. / r2) * (model_s2 - model_s) + D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1) + D2 = 2. * (D1_1 - D1_0) / (r2 - r1) + x_t = ( + (sigma_t / sigma_s) * x + - (alpha_t * phi_1) * model_s + + (alpha_t * phi_2) * D1 + - (alpha_t * phi_3) * D2 + ) + else: + phi_11 = torch.expm1(r1 * h) + phi_12 = torch.expm1(r2 * h) + phi_1 = torch.expm1(h) + phi_22 = torch.expm1(r2 * h) / (r2 * h) - 1. + phi_2 = phi_1 / h - 1. + phi_3 = phi_2 / h - 0.5 + + if model_s is None: + model_s = self.model_fn(x, s) + if model_s1 is None: + x_s1 = ( + (torch.exp(log_alpha_s1 - log_alpha_s)) * x + - (sigma_s1 * phi_11) * model_s + ) + model_s1 = self.model_fn(x_s1, s1) + x_s2 = ( + (torch.exp(log_alpha_s2 - log_alpha_s)) * x + - (sigma_s2 * phi_12) * model_s + - r2 / r1 * (sigma_s2 * phi_22) * (model_s1 - model_s) + ) + model_s2 = self.model_fn(x_s2, s2) + if solver_type == 'dpmsolver': + x_t = ( + (torch.exp(log_alpha_t - log_alpha_s)) * x + - (sigma_t * phi_1) * model_s + - (1. / r2) * (sigma_t * phi_2) * (model_s2 - model_s) + ) + elif solver_type == 'taylor': + D1_0 = (1. / r1) * (model_s1 - model_s) + D1_1 = (1. / r2) * (model_s2 - model_s) + D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1) + D2 = 2. * (D1_1 - D1_0) / (r2 - r1) + x_t = ( + (torch.exp(log_alpha_t - log_alpha_s)) * x + - (sigma_t * phi_1) * model_s + - (sigma_t * phi_2) * D1 + - (sigma_t * phi_3) * D2 + ) + + if return_intermediate: + return x_t, {'model_s': model_s, 'model_s1': model_s1, 'model_s2': model_s2} + else: + return x_t + + def multistep_dpm_solver_second_update(self, x, model_prev_list, t_prev_list, t, solver_type="dpmsolver"): + """ + Multistep solver DPM-Solver-2 from time `t_prev_list[-1]` to time `t`. + + Args: + x: A pytorch tensor. The initial value at time `s`. + model_prev_list: A list of pytorch tensor. The previous computed model values. + t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (1,) + t: A pytorch tensor. The ending time, with the shape (1,). + solver_type: either 'dpmsolver' or 'taylor'. The type for the high-order solvers. + The type slightly impacts the performance. We recommend to use 'dpmsolver' type. + Returns: + x_t: A pytorch tensor. The approximated solution at time `t`. + """ + if solver_type not in ['dpmsolver', 'taylor']: + raise ValueError("'solver_type' must be either 'dpmsolver' or 'taylor', got {}".format(solver_type)) + ns = self.noise_schedule + model_prev_1, model_prev_0 = model_prev_list[-2], model_prev_list[-1] + t_prev_1, t_prev_0 = t_prev_list[-2], t_prev_list[-1] + lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_1), ns.marginal_lambda(t_prev_0), ns.marginal_lambda(t) + log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t) + sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) + alpha_t = torch.exp(log_alpha_t) + + h_0 = lambda_prev_0 - lambda_prev_1 + h = lambda_t - lambda_prev_0 + r0 = h_0 / h + D1_0 = (1. / r0) * (model_prev_0 - model_prev_1) + if self.algorithm_type == "dpmsolver++": + phi_1 = torch.expm1(-h) + if solver_type == 'dpmsolver': + x_t = ( + (sigma_t / sigma_prev_0) * x + - (alpha_t * phi_1) * model_prev_0 + - 0.5 * (alpha_t * phi_1) * D1_0 + ) + elif solver_type == 'taylor': + x_t = ( + (sigma_t / sigma_prev_0) * x + - (alpha_t * phi_1) * model_prev_0 + + (alpha_t * (phi_1 / h + 1.)) * D1_0 + ) + else: + phi_1 = torch.expm1(h) + if solver_type == 'dpmsolver': + x_t = ( + (torch.exp(log_alpha_t - log_alpha_prev_0)) * x + - (sigma_t * phi_1) * model_prev_0 + - 0.5 * (sigma_t * phi_1) * D1_0 + ) + elif solver_type == 'taylor': + x_t = ( + (torch.exp(log_alpha_t - log_alpha_prev_0)) * x + - (sigma_t * phi_1) * model_prev_0 + - (sigma_t * (phi_1 / h - 1.)) * D1_0 + ) + return x_t + + def multistep_dpm_solver_third_update(self, x, model_prev_list, t_prev_list, t, solver_type='dpmsolver'): + """ + Multistep solver DPM-Solver-3 from time `t_prev_list[-1]` to time `t`. + + Args: + x: A pytorch tensor. The initial value at time `s`. + model_prev_list: A list of pytorch tensor. The previous computed model values. + t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (1,) + t: A pytorch tensor. The ending time, with the shape (1,). + solver_type: either 'dpmsolver' or 'taylor'. The type for the high-order solvers. + The type slightly impacts the performance. We recommend to use 'dpmsolver' type. + Returns: + x_t: A pytorch tensor. The approximated solution at time `t`. + """ + ns = self.noise_schedule + model_prev_2, model_prev_1, model_prev_0 = model_prev_list + t_prev_2, t_prev_1, t_prev_0 = t_prev_list + lambda_prev_2, lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_2), ns.marginal_lambda(t_prev_1), ns.marginal_lambda(t_prev_0), ns.marginal_lambda(t) + log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t) + sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) + alpha_t = torch.exp(log_alpha_t) + + h_1 = lambda_prev_1 - lambda_prev_2 + h_0 = lambda_prev_0 - lambda_prev_1 + h = lambda_t - lambda_prev_0 + r0, r1 = h_0 / h, h_1 / h + D1_0 = (1. / r0) * (model_prev_0 - model_prev_1) + D1_1 = (1. / r1) * (model_prev_1 - model_prev_2) + D1 = D1_0 + (r0 / (r0 + r1)) * (D1_0 - D1_1) + D2 = (1. / (r0 + r1)) * (D1_0 - D1_1) + if self.algorithm_type == "dpmsolver++": + phi_1 = torch.expm1(-h) + phi_2 = phi_1 / h + 1. + phi_3 = phi_2 / h - 0.5 + x_t = ( + (sigma_t / sigma_prev_0) * x + - (alpha_t * phi_1) * model_prev_0 + + (alpha_t * phi_2) * D1 + - (alpha_t * phi_3) * D2 + ) + else: + phi_1 = torch.expm1(h) + phi_2 = phi_1 / h - 1. + phi_3 = phi_2 / h - 0.5 + x_t = ( + (torch.exp(log_alpha_t - log_alpha_prev_0)) * x + - (sigma_t * phi_1) * model_prev_0 + - (sigma_t * phi_2) * D1 + - (sigma_t * phi_3) * D2 + ) + return x_t + + def singlestep_dpm_solver_update(self, x, s, t, order, return_intermediate=False, solver_type='dpmsolver', r1=None, r2=None): + """ + Singlestep DPM-Solver with the order `order` from time `s` to time `t`. + + Args: + x: A pytorch tensor. The initial value at time `s`. + s: A pytorch tensor. The starting time, with the shape (1,). + t: A pytorch tensor. The ending time, with the shape (1,). + order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3. + return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times). + solver_type: either 'dpmsolver' or 'taylor'. The type for the high-order solvers. + The type slightly impacts the performance. We recommend to use 'dpmsolver' type. + r1: A `float`. The hyperparameter of the second-order or third-order solver. + r2: A `float`. The hyperparameter of the third-order solver. + Returns: + x_t: A pytorch tensor. The approximated solution at time `t`. + """ + if order == 1: + return self.dpm_solver_first_update(x, s, t, return_intermediate=return_intermediate) + elif order == 2: + return self.singlestep_dpm_solver_second_update(x, s, t, return_intermediate=return_intermediate, solver_type=solver_type, r1=r1) + elif order == 3: + return self.singlestep_dpm_solver_third_update(x, s, t, return_intermediate=return_intermediate, solver_type=solver_type, r1=r1, r2=r2) + else: + raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order)) + + def multistep_dpm_solver_update(self, x, model_prev_list, t_prev_list, t, order, solver_type='dpmsolver'): + """ + Multistep DPM-Solver with the order `order` from time `t_prev_list[-1]` to time `t`. + + Args: + x: A pytorch tensor. The initial value at time `s`. + model_prev_list: A list of pytorch tensor. The previous computed model values. + t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (1,) + t: A pytorch tensor. The ending time, with the shape (1,). + order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3. + solver_type: either 'dpmsolver' or 'taylor'. The type for the high-order solvers. + The type slightly impacts the performance. We recommend to use 'dpmsolver' type. + Returns: + x_t: A pytorch tensor. The approximated solution at time `t`. + """ + if order == 1: + return self.dpm_solver_first_update(x, t_prev_list[-1], t, model_s=model_prev_list[-1]) + elif order == 2: + return self.multistep_dpm_solver_second_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type) + elif order == 3: + return self.multistep_dpm_solver_third_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type) + else: + raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order)) + + def dpm_solver_adaptive(self, x, order, t_T, t_0, h_init=0.05, atol=0.0078, rtol=0.05, theta=0.9, t_err=1e-5, solver_type='dpmsolver'): + """ + The adaptive step size solver based on singlestep DPM-Solver. + + Args: + x: A pytorch tensor. The initial value at time `t_T`. + order: A `int`. The (higher) order of the solver. We only support order == 2 or 3. + t_T: A `float`. The starting time of the sampling (default is T). + t_0: A `float`. The ending time of the sampling (default is epsilon). + h_init: A `float`. The initial step size (for logSNR). + atol: A `float`. The absolute tolerance of the solver. For image data, the default setting is 0.0078, followed [1]. + rtol: A `float`. The relative tolerance of the solver. The default setting is 0.05. + theta: A `float`. The safety hyperparameter for adapting the step size. The default setting is 0.9, followed [1]. + t_err: A `float`. The tolerance for the time. We solve the diffusion ODE until the absolute error between the + current time and `t_0` is less than `t_err`. The default setting is 1e-5. + solver_type: either 'dpmsolver' or 'taylor'. The type for the high-order solvers. + The type slightly impacts the performance. We recommend to use 'dpmsolver' type. + Returns: + x_0: A pytorch tensor. The approximated solution at time `t_0`. + + [1] A. Jolicoeur-Martineau, K. Li, R. Piché-Taillefer, T. Kachman, and I. Mitliagkas, "Gotta go fast when generating data with score-based models," arXiv preprint arXiv:2105.14080, 2021. + """ + ns = self.noise_schedule + s = t_T * torch.ones((1,)).to(x) + lambda_s = ns.marginal_lambda(s) + lambda_0 = ns.marginal_lambda(t_0 * torch.ones_like(s).to(x)) + h = h_init * torch.ones_like(s).to(x) + x_prev = x + nfe = 0 + if order == 2: + r1 = 0.5 + lower_update = lambda x, s, t: self.dpm_solver_first_update(x, s, t, return_intermediate=True) + higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1, solver_type=solver_type, **kwargs) + elif order == 3: + r1, r2 = 1. / 3., 2. / 3. + lower_update = lambda x, s, t: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1, return_intermediate=True, solver_type=solver_type) + higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_third_update(x, s, t, r1=r1, r2=r2, solver_type=solver_type, **kwargs) + else: + raise ValueError("For adaptive step size solver, order must be 2 or 3, got {}".format(order)) + while torch.abs((s - t_0)).mean() > t_err: + t = ns.inverse_lambda(lambda_s + h) + x_lower, lower_noise_kwargs = lower_update(x, s, t) + x_higher = higher_update(x, s, t, **lower_noise_kwargs) + delta = torch.max(torch.ones_like(x).to(x) * atol, rtol * torch.max(torch.abs(x_lower), torch.abs(x_prev))) + norm_fn = lambda v: torch.sqrt(torch.square(v.reshape((v.shape[0], -1))).mean(dim=-1, keepdim=True)) + E = norm_fn((x_higher - x_lower) / delta).max() + if torch.all(E <= 1.): + x = x_higher + s = t + x_prev = x_lower + lambda_s = ns.marginal_lambda(s) + h = torch.min(theta * h * torch.float_power(E, -1. / order).float(), lambda_0 - lambda_s) + nfe += order + print('adaptive solver nfe', nfe) + return x + + def add_noise(self, x, t, noise=None): + """ + Compute the noised input xt = alpha_t * x + sigma_t * noise. + + Args: + x: A `torch.Tensor` with shape `(batch_size, *shape)`. + t: A `torch.Tensor` with shape `(t_size,)`. + Returns: + xt with shape `(t_size, batch_size, *shape)`. + """ + alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t) + if noise is None: + noise = torch.randn((t.shape[0], *x.shape), device=x.device) + x = x.reshape((-1, *x.shape)) + xt = expand_dims(alpha_t, x.dim()) * x + expand_dims(sigma_t, x.dim()) * noise + if t.shape[0] == 1: + return xt.squeeze(0) + else: + return xt + + def inverse(self, x, steps=20, t_start=None, t_end=None, order=2, skip_type='time_uniform', + method='multistep', lower_order_final=True, denoise_to_zero=False, solver_type='dpmsolver', + atol=0.0078, rtol=0.05, return_intermediate=False, + ): + """ + Inverse the sample `x` from time `t_start` to `t_end` by DPM-Solver. + For discrete-time DPMs, we use `t_start=1/N`, where `N` is the total time steps during training. + """ + t_0 = 1. / self.noise_schedule.total_N if t_start is None else t_start + t_T = self.noise_schedule.T if t_end is None else t_end + assert t_0 > 0 and t_T > 0, "Time range needs to be greater than 0. For discrete-time DPMs, it needs to be in [1 / N, 1], where N is the length of betas array" + return self.sample(x, steps=steps, t_start=t_0, t_end=t_T, order=order, skip_type=skip_type, + method=method, lower_order_final=lower_order_final, denoise_to_zero=denoise_to_zero, solver_type=solver_type, + atol=atol, rtol=rtol, return_intermediate=return_intermediate) + + def sample(self, x, steps=20, t_start=None, t_end=None, order=2, skip_type='time_uniform', + method='multistep', lower_order_final=True, denoise_to_zero=False, solver_type='dpmsolver', + atol=0.0078, rtol=0.05, return_intermediate=False, + ): + """ + Compute the sample at time `t_end` by DPM-Solver, given the initial `x` at time `t_start`. + + ===================================================== + + We support the following algorithms for both noise prediction model and data prediction model: + - 'singlestep': + Singlestep DPM-Solver (i.e. "DPM-Solver-fast" in the paper), which combines different orders of singlestep DPM-Solver. + We combine all the singlestep solvers with order <= `order` to use up all the function evaluations (steps). + The total number of function evaluations (NFE) == `steps`. + Given a fixed NFE == `steps`, the sampling procedure is: + - If `order` == 1: + - Denote K = steps. We use K steps of DPM-Solver-1 (i.e. DDIM). + - If `order` == 2: + - Denote K = (steps // 2) + (steps % 2). We take K intermediate time steps for sampling. + - If steps % 2 == 0, we use K steps of singlestep DPM-Solver-2. + - If steps % 2 == 1, we use (K - 1) steps of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1. + - If `order` == 3: + - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling. + - If steps % 3 == 0, we use (K - 2) steps of singlestep DPM-Solver-3, and 1 step of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1. + - If steps % 3 == 1, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of DPM-Solver-1. + - If steps % 3 == 2, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of singlestep DPM-Solver-2. + - 'multistep': + Multistep DPM-Solver with the order of `order`. The total number of function evaluations (NFE) == `steps`. + We initialize the first `order` values by lower order multistep solvers. + Given a fixed NFE == `steps`, the sampling procedure is: + Denote K = steps. + - If `order` == 1: + - We use K steps of DPM-Solver-1 (i.e. DDIM). + - If `order` == 2: + - We firstly use 1 step of DPM-Solver-1, then use (K - 1) step of multistep DPM-Solver-2. + - If `order` == 3: + - We firstly use 1 step of DPM-Solver-1, then 1 step of multistep DPM-Solver-2, then (K - 2) step of multistep DPM-Solver-3. + - 'singlestep_fixed': + Fixed order singlestep DPM-Solver (i.e. DPM-Solver-1 or singlestep DPM-Solver-2 or singlestep DPM-Solver-3). + We use singlestep DPM-Solver-`order` for `order`=1 or 2 or 3, with total [`steps` // `order`] * `order` NFE. + - 'adaptive': + Adaptive step size DPM-Solver (i.e. "DPM-Solver-12" and "DPM-Solver-23" in the paper). + We ignore `steps` and use adaptive step size DPM-Solver with a higher order of `order`. + You can adjust the absolute tolerance `atol` and the relative tolerance `rtol` to balance the computatation costs + (NFE) and the sample quality. + - If `order` == 2, we use DPM-Solver-12 which combines DPM-Solver-1 and singlestep DPM-Solver-2. + - If `order` == 3, we use DPM-Solver-23 which combines singlestep DPM-Solver-2 and singlestep DPM-Solver-3. + + ===================================================== + + Some advices for choosing the algorithm: + - For **unconditional sampling** or **guided sampling with small guidance scale** by DPMs: + Use singlestep DPM-Solver or DPM-Solver++ ("DPM-Solver-fast" in the paper) with `order = 3`. + e.g., DPM-Solver: + >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, algorithm_type="dpmsolver") + >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=3, + skip_type='time_uniform', method='singlestep') + e.g., DPM-Solver++: + >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, algorithm_type="dpmsolver++") + >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=3, + skip_type='time_uniform', method='singlestep') + - For **guided sampling with large guidance scale** by DPMs: + Use multistep DPM-Solver with `algorithm_type="dpmsolver++"` and `order = 2`. + e.g. + >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, algorithm_type="dpmsolver++") + >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=2, + skip_type='time_uniform', method='multistep') + + We support three types of `skip_type`: + - 'logSNR': uniform logSNR for the time steps. **Recommended for low-resolutional images** + - 'time_uniform': uniform time for the time steps. **Recommended for high-resolutional images**. + - 'time_quadratic': quadratic time for the time steps. + + ===================================================== + Args: + x: A pytorch tensor. The initial value at time `t_start` + e.g. if `t_start` == T, then `x` is a sample from the standard normal distribution. + steps: A `int`. The total number of function evaluations (NFE). + t_start: A `float`. The starting time of the sampling. + If `T` is None, we use self.noise_schedule.T (default is 1.0). + t_end: A `float`. The ending time of the sampling. + If `t_end` is None, we use 1. / self.noise_schedule.total_N. + e.g. if total_N == 1000, we have `t_end` == 1e-3. + For discrete-time DPMs: + - We recommend `t_end` == 1. / self.noise_schedule.total_N. + For continuous-time DPMs: + - We recommend `t_end` == 1e-3 when `steps` <= 15; and `t_end` == 1e-4 when `steps` > 15. + order: A `int`. The order of DPM-Solver. + skip_type: A `str`. The type for the spacing of the time steps. 'time_uniform' or 'logSNR' or 'time_quadratic'. + method: A `str`. The method for sampling. 'singlestep' or 'multistep' or 'singlestep_fixed' or 'adaptive'. + denoise_to_zero: A `bool`. Whether to denoise to time 0 at the final step. + Default is `False`. If `denoise_to_zero` is `True`, the total NFE is (`steps` + 1). + + This trick is firstly proposed by DDPM (https://arxiv.org/abs/2006.11239) and + score_sde (https://arxiv.org/abs/2011.13456). Such trick can improve the FID + for diffusion models sampling by diffusion SDEs for low-resolutional images + (such as CIFAR-10). However, we observed that such trick does not matter for + high-resolutional images. As it needs an additional NFE, we do not recommend + it for high-resolutional images. + lower_order_final: A `bool`. Whether to use lower order solvers at the final steps. + Only valid for `method=multistep` and `steps < 15`. We empirically find that + this trick is a key to stabilizing the sampling by DPM-Solver with very few steps + (especially for steps <= 10). So we recommend to set it to be `True`. + solver_type: A `str`. The taylor expansion type for the solver. `dpmsolver` or `taylor`. We recommend `dpmsolver`. + atol: A `float`. The absolute tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'. + rtol: A `float`. The relative tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'. + return_intermediate: A `bool`. Whether to save the xt at each step. + When set to `True`, method returns a tuple (x0, intermediates); when set to False, method returns only x0. + Returns: + x_end: A pytorch tensor. The approximated solution at time `t_end`. + + """ + t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end + t_T = self.noise_schedule.T if t_start is None else t_start + assert t_0 > 0 and t_T > 0, "Time range needs to be greater than 0. For discrete-time DPMs, it needs to be in [1 / N, 1], where N is the length of betas array" + if return_intermediate: + assert method in ['multistep', 'singlestep', 'singlestep_fixed'], "Cannot use adaptive solver when saving intermediate values" + if self.correcting_xt_fn is not None: + assert method in ['multistep', 'singlestep', 'singlestep_fixed'], "Cannot use adaptive solver when correcting_xt_fn is not None" + device = x.device + intermediates = [] + with torch.no_grad(): + if method == 'adaptive': + x = self.dpm_solver_adaptive(x, order=order, t_T=t_T, t_0=t_0, atol=atol, rtol=rtol, solver_type=solver_type) + elif method == 'multistep': + assert steps >= order + timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device) + assert timesteps.shape[0] - 1 == steps + # Init the initial values. + step = 0 + t = timesteps[step] + t_prev_list = [t] + model_prev_list = [self.model_fn(x, t)] + if self.correcting_xt_fn is not None: + x = self.correcting_xt_fn(x, t, step) + if return_intermediate: + intermediates.append(x) + # Init the first `order` values by lower order multistep DPM-Solver. + for step in range(1, order): + t = timesteps[step] + x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, t, step, solver_type=solver_type) + if self.correcting_xt_fn is not None: + x = self.correcting_xt_fn(x, t, step) + if return_intermediate: + intermediates.append(x) + t_prev_list.append(t) + model_prev_list.append(self.model_fn(x, t)) + # Compute the remaining values by `order`-th order multistep DPM-Solver. + for step in range(order, steps + 1): + t = timesteps[step] + # We only use lower order for steps < 10 + if lower_order_final and steps < 10: + step_order = min(order, steps + 1 - step) + else: + step_order = order + x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, t, step_order, solver_type=solver_type) + if self.correcting_xt_fn is not None: + x = self.correcting_xt_fn(x, t, step) + if return_intermediate: + intermediates.append(x) + for i in range(order - 1): + t_prev_list[i] = t_prev_list[i + 1] + model_prev_list[i] = model_prev_list[i + 1] + t_prev_list[-1] = t + # We do not need to evaluate the final model value. + if step < steps: + model_prev_list[-1] = self.model_fn(x, t) + elif method in ['singlestep', 'singlestep_fixed']: + if method == 'singlestep': + timesteps_outer, orders = self.get_orders_and_timesteps_for_singlestep_solver(steps=steps, order=order, skip_type=skip_type, t_T=t_T, t_0=t_0, device=device) + elif method == 'singlestep_fixed': + K = steps // order + orders = [order,] * K + timesteps_outer = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=K, device=device) + for step, order in enumerate(orders): + s, t = timesteps_outer[step], timesteps_outer[step + 1] + timesteps_inner = self.get_time_steps(skip_type=skip_type, t_T=s.item(), t_0=t.item(), N=order, device=device) + lambda_inner = self.noise_schedule.marginal_lambda(timesteps_inner) + h = lambda_inner[-1] - lambda_inner[0] + r1 = None if order <= 1 else (lambda_inner[1] - lambda_inner[0]) / h + r2 = None if order <= 2 else (lambda_inner[2] - lambda_inner[0]) / h + x = self.singlestep_dpm_solver_update(x, s, t, order, solver_type=solver_type, r1=r1, r2=r2) + if self.correcting_xt_fn is not None: + x = self.correcting_xt_fn(x, t, step) + if return_intermediate: + intermediates.append(x) + else: + raise ValueError("Got wrong method {}".format(method)) + if denoise_to_zero: + t = torch.ones((1,)).to(device) * t_0 + x = self.denoise_to_zero_fn(x, t) + if self.correcting_xt_fn is not None: + x = self.correcting_xt_fn(x, t, step + 1) + if return_intermediate: + intermediates.append(x) + if return_intermediate: + return x, intermediates + else: + return x + + + +############################################################# +# other utility functions +############################################################# + +def interpolate_fn(x, xp, yp): + """ + A piecewise linear function y = f(x), using xp and yp as keypoints. + We implement f(x) in a differentiable way (i.e. applicable for autograd). + The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.) + + Args: + x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver). + xp: PyTorch tensor with shape [C, K], where K is the number of keypoints. + yp: PyTorch tensor with shape [C, K]. + Returns: + The function values f(x), with shape [N, C]. + """ + N, K = x.shape[0], xp.shape[1] + all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2) + sorted_all_x, x_indices = torch.sort(all_x, dim=2) + x_idx = torch.argmin(x_indices, dim=2) + cand_start_idx = x_idx - 1 + start_idx = torch.where( + torch.eq(x_idx, 0), + torch.tensor(1, device=x.device), + torch.where( + torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx, + ), + ) + end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1) + start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2) + end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2) + start_idx2 = torch.where( + torch.eq(x_idx, 0), + torch.tensor(0, device=x.device), + torch.where( + torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx, + ), + ) + y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1) + start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2) + end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2) + cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x) + return cand + + +def expand_dims(v, dims): + """ + Expand the tensor `v` to the dim `dims`. + + Args: + `v`: a PyTorch tensor with shape [N]. + `dim`: a `int`. + Returns: + a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`. + """ + return v[(...,) + (None,)*(dims - 1)] \ No newline at end of file diff --git a/inference/ds_acoustic.py b/inference/ds_acoustic.py new file mode 100644 index 0000000..bbb3418 --- /dev/null +++ b/inference/ds_acoustic.py @@ -0,0 +1,271 @@ +import json +import pathlib +from collections import OrderedDict +from typing import Dict + +import numpy as np +import torch +import tqdm + +from basics.base_svs_infer import BaseSVSInfer +from modules.fastspeech.param_adaptor import VARIANCE_CHECKLIST +from modules.fastspeech.tts_modules import LengthRegulator +from modules.toplevel import DiffSingerAcoustic, ShallowDiffusionOutput +from modules.vocoders.registry import VOCODERS +from utils import load_ckpt +from utils.hparams import hparams +from utils.infer_utils import cross_fade, resample_align_curve, save_wav +from utils.phoneme_utils import load_phoneme_dictionary + + +class DiffSingerAcousticInfer(BaseSVSInfer): + def __init__(self, device=None, load_model=True, load_vocoder=True, ckpt_steps=None): + super().__init__(device=device) + if load_model: + self.variance_checklist = [] + + self.variances_to_embed = set() + + if hparams.get('use_energy_embed', False): + self.variances_to_embed.add('energy') + if hparams.get('use_breathiness_embed', False): + self.variances_to_embed.add('breathiness') + if hparams.get('use_voicing_embed', False): + self.variances_to_embed.add('voicing') + if hparams.get('use_tension_embed', False): + self.variances_to_embed.add('tension') + + self.phoneme_dictionary = load_phoneme_dictionary() + if hparams['use_spk_id']: + with open(pathlib.Path(hparams['work_dir']) / 'spk_map.json', 'r', encoding='utf8') as f: + self.spk_map = json.load(f) + assert isinstance(self.spk_map, dict) and len(self.spk_map) > 0, 'Invalid or empty speaker map!' + assert len(self.spk_map) == len(set(self.spk_map.values())), 'Duplicate speaker id in speaker map!' + lang_map_fn = pathlib.Path(hparams['work_dir']) / 'lang_map.json' + if lang_map_fn.exists(): + with open(lang_map_fn, 'r', encoding='utf8') as f: + self.lang_map = json.load(f) + self.model = self.build_model(ckpt_steps=ckpt_steps) + self.lr = LengthRegulator().to(self.device) + if load_vocoder: + self.vocoder = self.build_vocoder() + + def build_model(self, ckpt_steps=None): + model = DiffSingerAcoustic( + vocab_size=len(self.phoneme_dictionary), + out_dims=hparams['audio_num_mel_bins'] + ).eval().to(self.device) + load_ckpt(model, hparams['work_dir'], ckpt_steps=ckpt_steps, + prefix_in_ckpt='model', strict=True, device=self.device) + return model + + def build_vocoder(self): + if hparams['vocoder'] in VOCODERS: + vocoder = VOCODERS[hparams['vocoder']]() + else: + vocoder = VOCODERS[hparams['vocoder'].split('.')[-1]]() + vocoder.to_device(self.device) + return vocoder + + def preprocess_input(self, param, idx=0): + """ + :param param: one segment in the .ds file + :param idx: index of the segment + :return: batch of the model inputs + """ + batch = {} + summary = OrderedDict() + + lang = param.get('lang') + if lang is None: + assert len(self.lang_map) <= 1, ( + "This is a multilingual model. " + "Please specify a language by --lang option." + ) + else: + assert lang in self.lang_map, f'Unrecognized language name: \'{lang}\'.' + if hparams.get('use_lang_id', False): + languages = torch.LongTensor([ + ( + self.lang_map[lang if '/' not in p else p.split('/', maxsplit=1)[0]] + if self.phoneme_dictionary.is_cross_lingual(p if '/' in p else f'{lang}/{p}') + else 0 + ) + for p in param['ph_seq'].split() + ]).to(self.device) # => [B, T_txt] + batch['languages'] = languages + txt_tokens = torch.LongTensor([ + self.phoneme_dictionary.encode(param['ph_seq'], lang=lang) + ]).to(self.device) # => [B, T_txt] + batch['tokens'] = txt_tokens + + ph_dur = torch.from_numpy(np.array(param['ph_dur'].split(), np.float32)).to(self.device) + ph_acc = torch.round(torch.cumsum(ph_dur, dim=0) / self.timestep + 0.5).long() + durations = torch.diff(ph_acc, dim=0, prepend=torch.LongTensor([0]).to(self.device))[None] # => [B=1, T_txt] + mel2ph = self.lr(durations, txt_tokens == 0) # => [B=1, T] + batch['mel2ph'] = mel2ph + length = mel2ph.size(1) # => T + + summary['tokens'] = txt_tokens.size(1) + summary['frames'] = length + summary['seconds'] = '%.2f' % (length * self.timestep) + + if hparams['use_spk_id']: + spk_mix_id, spk_mix_value = self.load_speaker_mix( + param_src=param, summary_dst=summary, mix_mode='frame', mix_length=length + ) + batch['spk_mix_id'] = spk_mix_id + batch['spk_mix_value'] = spk_mix_value + + batch['f0'] = torch.from_numpy(resample_align_curve( + np.array(param['f0_seq'].split(), np.float32), + original_timestep=float(param['f0_timestep']), + target_timestep=self.timestep, + align_length=length + )).to(self.device)[None] + + for v_name in VARIANCE_CHECKLIST: + if v_name in self.variances_to_embed: + batch[v_name] = torch.from_numpy(resample_align_curve( + np.array(param[v_name].split(), np.float32), + original_timestep=float(param[f'{v_name}_timestep']), + target_timestep=self.timestep, + align_length=length + )).to(self.device)[None] + summary[v_name] = 'manual' + + if hparams['use_key_shift_embed']: + shift_min, shift_max = hparams['augmentation_args']['random_pitch_shifting']['range'] + gender = param.get('gender') + if gender is None: + gender = 0. + if isinstance(gender, (int, float, bool)): # static gender value + summary['gender'] = f'static({gender:.3f})' + key_shift_value = gender * shift_max if gender >= 0 else gender * abs(shift_min) + batch['key_shift'] = torch.FloatTensor([key_shift_value]).to(self.device)[:, None] # => [B=1, T=1] + else: + summary['gender'] = 'dynamic' + gender_seq = resample_align_curve( + np.array(gender.split(), np.float32), + original_timestep=float(param['gender_timestep']), + target_timestep=self.timestep, + align_length=length + ) + gender_mask = gender_seq >= 0 + key_shift_seq = gender_seq * (gender_mask * shift_max + (1 - gender_mask) * abs(shift_min)) + batch['key_shift'] = torch.clip( + torch.from_numpy(key_shift_seq.astype(np.float32)).to(self.device)[None], # => [B=1, T] + min=shift_min, max=shift_max + ) + + if hparams['use_speed_embed']: + if param.get('velocity') is None: + summary['velocity'] = 'default' + batch['speed'] = torch.FloatTensor([1.]).to(self.device)[:, None] # => [B=1, T=1] + else: + summary['velocity'] = 'manual' + speed_min, speed_max = hparams['augmentation_args']['random_time_stretching']['range'] + speed_seq = resample_align_curve( + np.array(param['velocity'].split(), np.float32), + original_timestep=float(param['velocity_timestep']), + target_timestep=self.timestep, + align_length=length + ) + batch['speed'] = torch.clip( + torch.from_numpy(speed_seq.astype(np.float32)).to(self.device)[None], # => [B=1, T] + min=speed_min, max=speed_max + ) + + print(f'[{idx}]\t' + ', '.join(f'{k}: {v}' for k, v in summary.items())) + + return batch + + @torch.no_grad() + def forward_model(self, sample): + txt_tokens = sample['tokens'] + variances = { + v_name: sample.get(v_name) + for v_name in self.variances_to_embed + } + if hparams['use_spk_id']: + spk_mix_id = sample['spk_mix_id'] + spk_mix_value = sample['spk_mix_value'] + # perform mixing on spk embed + spk_mix_embed = torch.sum( + self.model.fs2.spk_embed(spk_mix_id) * spk_mix_value.unsqueeze(3), # => [B, T, N, H] + dim=2, keepdim=False + ) # => [B, T, H] + else: + spk_mix_embed = None + mel_pred: ShallowDiffusionOutput = self.model( + txt_tokens, languages=sample.get('languages'), + mel2ph=sample['mel2ph'], f0=sample['f0'], **variances, + key_shift=sample.get('key_shift'), speed=sample.get('speed'), + spk_mix_embed=spk_mix_embed, + infer=True + ) + return mel_pred.diff_out + + @torch.no_grad() + def run_vocoder(self, spec, **kwargs): + y = self.vocoder.spec2wav_torch(spec, **kwargs) + return y[None] + + def run_inference( + self, params, + out_dir: pathlib.Path = None, + title: str = None, + num_runs: int = 1, + spk_mix: Dict[str, float] = None, + seed: int = -1, + save_mel: bool = False + ): + batches = [self.preprocess_input(param, idx=i) for i, param in enumerate(params)] + + out_dir.mkdir(parents=True, exist_ok=True) + suffix = '.wav' if not save_mel else '.mel.pt' + for i in range(num_runs): + if save_mel: + result = [] + else: + result = np.zeros(0) + current_length = 0 + + for param, batch in tqdm.tqdm( + zip(params, batches), desc='infer segments', total=len(params) + ): + if 'seed' in param: + torch.manual_seed(param["seed"] & 0xffff_ffff) + torch.cuda.manual_seed_all(param["seed"] & 0xffff_ffff) + elif seed >= 0: + torch.manual_seed(seed & 0xffff_ffff) + torch.cuda.manual_seed_all(seed & 0xffff_ffff) + + mel_pred = self.forward_model(batch) + if save_mel: + result.append({ + 'offset': param.get('offset', 0.), + 'mel': mel_pred.cpu(), + 'f0': batch['f0'].cpu() + }) + else: + waveform_pred = self.run_vocoder(mel_pred, f0=batch['f0'])[0].cpu().numpy() + silent_length = round(param.get('offset', 0) * hparams['audio_sample_rate']) - current_length + if silent_length >= 0: + result = np.append(result, np.zeros(silent_length)) + result = np.append(result, waveform_pred) + else: + result = cross_fade(result, waveform_pred, current_length + silent_length) + current_length = current_length + silent_length + waveform_pred.shape[0] + + if num_runs > 1: + filename = f'{title}-{str(i).zfill(3)}{suffix}' + else: + filename = title + suffix + save_path = out_dir / filename + if save_mel: + print(f'| save mel: {save_path}') + torch.save(result, save_path) + else: + print(f'| save audio: {save_path}') + save_wav(result, save_path, hparams['audio_sample_rate']) diff --git a/inference/ds_variance.py b/inference/ds_variance.py new file mode 100644 index 0000000..da3d6e9 --- /dev/null +++ b/inference/ds_variance.py @@ -0,0 +1,468 @@ +import copy +import json +import pathlib +from collections import OrderedDict +from typing import List, Tuple + +import librosa +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import tqdm +from scipy import interpolate + +from basics.base_svs_infer import BaseSVSInfer +from modules.fastspeech.param_adaptor import VARIANCE_CHECKLIST +from modules.fastspeech.tts_modules import ( + LengthRegulator, RhythmRegulator, + mel2ph_to_dur +) +from modules.toplevel import DiffSingerVariance +from utils import load_ckpt +from utils.hparams import hparams +from utils.infer_utils import resample_align_curve +from utils.phoneme_utils import load_phoneme_dictionary +from utils.pitch_utils import interp_f0 + + +class DiffSingerVarianceInfer(BaseSVSInfer): + def __init__( + self, device=None, ckpt_steps=None, + predictions: set = None + ): + super().__init__(device=device) + self.phoneme_dictionary = load_phoneme_dictionary() + if hparams['use_spk_id']: + with open(pathlib.Path(hparams['work_dir']) / 'spk_map.json', 'r', encoding='utf8') as f: + self.spk_map = json.load(f) + assert isinstance(self.spk_map, dict) and len(self.spk_map) > 0, 'Invalid or empty speaker map!' + assert len(self.spk_map) == len(set(self.spk_map.values())), 'Duplicate speaker id in speaker map!' + lang_map_fn = pathlib.Path(hparams['work_dir']) / 'lang_map.json' + if lang_map_fn.exists(): + with open(lang_map_fn, 'r', encoding='utf8') as f: + self.lang_map = json.load(f) + self.model: DiffSingerVariance = self.build_model(ckpt_steps=ckpt_steps) + self.lr = LengthRegulator() + self.rr = RhythmRegulator() + smooth_kernel_size = round(hparams['midi_smooth_width'] / self.timestep) + self.smooth = nn.Conv1d( + in_channels=1, + out_channels=1, + kernel_size=smooth_kernel_size, + bias=False, + padding='same', + padding_mode='replicate' + ).eval().to(self.device) + smooth_kernel = torch.sin(torch.from_numpy( + np.linspace(0, 1, smooth_kernel_size).astype(np.float32) * np.pi + ).to(self.device)) + smooth_kernel /= smooth_kernel.sum() + self.smooth.weight.data = smooth_kernel[None, None] + + glide_types = hparams.get('glide_types', []) + assert 'none' not in glide_types, 'Type name \'none\' is reserved and should not appear in glide_types.' + self.glide_map = { + 'none': 0, + **{ + typename: idx + 1 + for idx, typename in enumerate(glide_types) + } + } + + self.auto_completion_mode = len(predictions) == 0 + self.global_predict_dur = 'dur' in predictions and hparams['predict_dur'] + self.global_predict_pitch = 'pitch' in predictions and hparams['predict_pitch'] + self.variance_prediction_set = predictions.intersection(VARIANCE_CHECKLIST) + self.global_predict_variances = len(self.variance_prediction_set) > 0 + + def build_model(self, ckpt_steps=None): + model = DiffSingerVariance( + vocab_size=len(self.phoneme_dictionary) + ).eval().to(self.device) + load_ckpt(model, hparams['work_dir'], ckpt_steps=ckpt_steps, + prefix_in_ckpt='model', strict=True, device=self.device) + return model + + @torch.no_grad() + def preprocess_input( + self, param, idx=0, + load_dur: bool = False, + load_pitch: bool = False + ): + """ + :param param: one segment in the .ds file + :param idx: index of the segment + :param load_dur: whether ph_dur is loaded + :param load_pitch: whether pitch is loaded + :return: batch of the model inputs + """ + batch = {} + summary = OrderedDict() + + lang = param.get('lang') + if lang is None: + assert len(self.lang_map) <= 1, ( + "This is a multilingual model. " + "Please specify a language by --lang option." + ) + else: + assert lang in self.lang_map, f'Unrecognized language name: \'{lang}\'.' + if hparams.get('use_lang_id', False): + languages = torch.LongTensor([ + ( + self.lang_map[lang if '/' not in p else p.split('/', maxsplit=1)[0]] + if self.phoneme_dictionary.is_cross_lingual(p if '/' in p else f'{lang}/{p}') + else 0 + ) + for p in param['ph_seq'].split() + ]).to(self.device) # [B=1, T_ph] + batch['languages'] = languages + txt_tokens = torch.LongTensor([ + self.phoneme_dictionary.encode(param['ph_seq'], lang=lang) + ]).to(self.device) # [B=1, T_ph] + T_ph = txt_tokens.shape[1] + batch['tokens'] = txt_tokens + ph_num = torch.from_numpy(np.array([param['ph_num'].split()], np.int64)).to(self.device) # [B=1, T_w] + ph2word = self.lr(ph_num) # => [B=1, T_ph] + T_w = int(ph2word.max()) + batch['ph2word'] = ph2word + + note_midi = np.array( + [(librosa.note_to_midi(n, round_midi=False) if n != 'rest' else -1) for n in param['note_seq'].split()], + dtype=np.float32 + ) + note_rest = note_midi < 0 + if np.all(note_rest): + # All rests, fill with constants + note_midi = np.full_like(note_midi, fill_value=60.) + else: + # Interpolate rest values + interp_func = interpolate.interp1d( + np.where(~note_rest)[0], note_midi[~note_rest], + kind='nearest', fill_value='extrapolate' + ) + note_midi[note_rest] = interp_func(np.where(note_rest)[0]) + note_midi = torch.from_numpy(note_midi).to(self.device)[None] # [B=1, T_n] + note_rest = torch.from_numpy(note_rest).to(self.device)[None] # [B=1, T_n] + + T_n = note_midi.shape[1] + note_dur_sec = torch.from_numpy(np.array([param['note_dur'].split()], np.float32)).to(self.device) # [B=1, T_n] + note_acc = torch.round(torch.cumsum(note_dur_sec, dim=1) / self.timestep + 0.5).long() + note_dur = torch.diff(note_acc, dim=1, prepend=note_acc.new_zeros(1, 1)) + mel2note = self.lr(note_dur) # [B=1, T_s] + T_s = mel2note.shape[1] + + summary['words'] = T_w + summary['notes'] = T_n + summary['tokens'] = T_ph + summary['frames'] = T_s + summary['seconds'] = '%.2f' % (T_s * self.timestep) + + if hparams['use_spk_id']: + ph_spk_mix_id, ph_spk_mix_value = self.load_speaker_mix( + param_src=param, summary_dst=summary, mix_mode='token', mix_length=T_ph + ) + spk_mix_id, spk_mix_value = self.load_speaker_mix( + param_src=param, summary_dst=summary, mix_mode='frame', mix_length=T_s + ) + batch['ph_spk_mix_id'] = ph_spk_mix_id + batch['ph_spk_mix_value'] = ph_spk_mix_value + batch['spk_mix_id'] = spk_mix_id + batch['spk_mix_value'] = spk_mix_value + + if load_dur: + # Get mel2ph if ph_dur is needed + ph_dur_sec = torch.from_numpy( + np.array([param['ph_dur'].split()], np.float32) + ).to(self.device) # [B=1, T_ph] + ph_acc = torch.round(torch.cumsum(ph_dur_sec, dim=1) / self.timestep + 0.5).long() + ph_dur = torch.diff(ph_acc, dim=1, prepend=ph_acc.new_zeros(1, 1)) + mel2ph = self.lr(ph_dur, txt_tokens == 0) + if mel2ph.shape[1] != T_s: # Align phones with notes + mel2ph = F.pad(mel2ph, [0, T_s - mel2ph.shape[1]], value=mel2ph[0, -1]) + ph_dur = mel2ph_to_dur(mel2ph, T_ph) + # Get word_dur from ph_dur and ph_num + word_dur = note_dur.new_zeros(1, T_w + 1).scatter_add( + 1, ph2word, ph_dur + )[:, 1:] # => [B=1, T_w] + else: + ph_dur = None + mel2ph = None + # Get word_dur from note_dur and note_slur + is_slur = torch.BoolTensor([[int(s) for s in param['note_slur'].split()]]).to(self.device) # [B=1, T_n] + note2word = torch.cumsum(~is_slur, dim=1) # [B=1, T_n] + word_dur = note_dur.new_zeros(1, T_w + 1).scatter_add( + 1, note2word, note_dur + )[:, 1:] # => [B=1, T_w] + + batch['ph_dur'] = ph_dur + batch['mel2ph'] = mel2ph + + mel2word = self.lr(word_dur) # [B=1, T_s] + if mel2word.shape[1] != T_s: # Align words with notes + mel2word = F.pad(mel2word, [0, T_s - mel2word.shape[1]], value=mel2word[0, -1]) + word_dur = mel2ph_to_dur(mel2word, T_w) + batch['word_dur'] = word_dur + + batch['note_midi'] = note_midi + batch['note_dur'] = note_dur + batch['note_rest'] = note_rest + if hparams.get('use_glide_embed', False) and param.get('note_glide') is not None: + batch['note_glide'] = torch.LongTensor( + [[self.glide_map.get(x, 0) for x in param['note_glide'].split()]] + ).to(self.device) + else: + batch['note_glide'] = torch.zeros(1, T_n, dtype=torch.long, device=self.device) + batch['mel2note'] = mel2note + + # Calculate and smoothen the frame-level MIDI pitch, which is a step function curve + frame_midi_pitch = torch.gather( + F.pad(note_midi, [1, 0]), 1, mel2note + ) # => frame-level MIDI pitch, [B=1, T_s] + base_pitch = self.smooth(frame_midi_pitch) + batch['base_pitch'] = base_pitch + + if ph_dur is not None: + # Phone durations are available, calculate phoneme-level MIDI. + mel2pdur = torch.gather(F.pad(ph_dur, [1, 0], value=1), 1, mel2ph) # frame-level phone duration + ph_midi = frame_midi_pitch.new_zeros(1, T_ph + 1).scatter_add( + 1, mel2ph, frame_midi_pitch / mel2pdur + )[:, 1:] + else: + # Phone durations are not available, calculate word-level MIDI instead. + mel2wdur = torch.gather(F.pad(word_dur, [1, 0], value=1), 1, mel2word) + w_midi = frame_midi_pitch.new_zeros(1, T_w + 1).scatter_add( + 1, mel2word, frame_midi_pitch / mel2wdur + )[:, 1:] + # Convert word-level MIDI to phoneme-level MIDI + ph_midi = torch.gather(F.pad(w_midi, [1, 0]), 1, ph2word) + ph_midi = ph_midi.round().long() + batch['midi'] = ph_midi + + if load_pitch: + f0 = resample_align_curve( + np.array(param['f0_seq'].split(), np.float32), + original_timestep=float(param['f0_timestep']), + target_timestep=self.timestep, + align_length=T_s + ) + batch['pitch'] = torch.from_numpy( + librosa.hz_to_midi(interp_f0(f0)[0]).astype(np.float32) + ).to(self.device)[None] + + if self.model.predict_dur: + if load_dur: + summary['ph_dur'] = 'manual' + elif self.auto_completion_mode or self.global_predict_dur: + summary['ph_dur'] = 'auto' + else: + summary['ph_dur'] = 'ignored' + + if self.model.predict_pitch: + if load_pitch: + summary['pitch'] = 'manual' + elif self.auto_completion_mode or self.global_predict_pitch: + summary['pitch'] = 'auto' + + # Load expressiveness + expr = param.get('expr', 1.) + if isinstance(expr, (int, float, bool)): + summary['expr'] = f'static({expr:.3f})' + batch['expr'] = torch.FloatTensor([expr]).to(self.device)[:, None] # [B=1, T=1] + else: + summary['expr'] = 'dynamic' + expr = resample_align_curve( + np.array(expr.split(), np.float32), + original_timestep=float(param['expr_timestep']), + target_timestep=self.timestep, + align_length=T_s + ) + batch['expr'] = torch.from_numpy(expr.astype(np.float32)).to(self.device)[None] + + else: + summary['pitch'] = 'ignored' + + if self.model.predict_variances: + for v_name in self.model.variance_prediction_list: + if self.auto_completion_mode and param.get(v_name) is None or v_name in self.variance_prediction_set: + summary[v_name] = 'auto' + else: + summary[v_name] = 'ignored' + + print(f'[{idx}]\t' + ', '.join(f'{k}: {v}' for k, v in summary.items())) + + return batch + + @torch.no_grad() + def forward_model(self, sample): + txt_tokens = sample['tokens'] + midi = sample['midi'] + ph2word = sample['ph2word'] + word_dur = sample['word_dur'] + ph_dur = sample['ph_dur'] + mel2ph = sample['mel2ph'] + note_midi = sample['note_midi'] + note_rest = sample['note_rest'] + note_dur = sample['note_dur'] + note_glide = sample['note_glide'] + mel2note = sample['mel2note'] + base_pitch = sample['base_pitch'] + expr = sample.get('expr') + pitch = sample.get('pitch') + + if hparams['use_spk_id']: + ph_spk_mix_id = sample['ph_spk_mix_id'] + ph_spk_mix_value = sample['ph_spk_mix_value'] + spk_mix_id = sample['spk_mix_id'] + spk_mix_value = sample['spk_mix_value'] + ph_spk_mix_embed = torch.sum( + self.model.spk_embed(ph_spk_mix_id) * ph_spk_mix_value.unsqueeze(3), # => [B, T_ph, N, H] + dim=2, keepdim=False + ) # => [B, T_ph, H] + spk_mix_embed = torch.sum( + self.model.spk_embed(spk_mix_id) * spk_mix_value.unsqueeze(3), # => [B, T_s, N, H] + dim=2, keepdim=False + ) # [B, T_s, H] + else: + ph_spk_mix_embed = spk_mix_embed = None + + dur_pred, pitch_pred, variance_pred = self.model( + txt_tokens, languages=sample.get('languages'), + midi=midi, ph2word=ph2word, word_dur=word_dur, ph_dur=ph_dur, mel2ph=mel2ph, + note_midi=note_midi, note_rest=note_rest, note_dur=note_dur, note_glide=note_glide, mel2note=mel2note, + base_pitch=base_pitch, pitch=pitch, pitch_expr=expr, + ph_spk_mix_embed=ph_spk_mix_embed, spk_mix_embed=spk_mix_embed, + infer=True + ) + if dur_pred is not None: + dur_pred = self.rr(dur_pred, ph2word, word_dur) + if pitch_pred is not None: + pitch_pred = base_pitch + pitch_pred + return dur_pred, pitch_pred, variance_pred + + def infer_once(self, param): + batch = self.preprocess_input(param) + dur_pred, pitch_pred, variance_pred = self.forward_model(batch) + if dur_pred is not None: + dur_pred = dur_pred[0].cpu().numpy() + if pitch_pred is not None: + pitch_pred = pitch_pred[0].cpu().numpy() + f0_pred = librosa.midi_to_hz(pitch_pred) + else: + f0_pred = None + variance_pred = { + k: v[0].cpu().numpy() + for k, v in variance_pred.items() + } + return dur_pred, f0_pred, variance_pred + + def run_inference( + self, params, + out_dir: pathlib.Path = None, + title: str = None, + num_runs: int = 1, + seed: int = -1 + ): + batches = [] + predictor_flags: List[Tuple[bool, bool, bool]] = [] + + for i, param in enumerate(params): + param: dict + if self.auto_completion_mode: + flag = ( + self.model.fs2.predict_dur and param.get('ph_dur') is None, + self.model.predict_pitch and param.get('f0_seq') is None, + self.model.predict_variances and any( + param.get(v_name) is None for v_name in self.model.variance_prediction_list + ) + ) + else: + predict_variances = self.model.predict_variances and self.global_predict_variances + predict_pitch = self.model.predict_pitch and ( + self.global_predict_pitch or (param.get('f0_seq') is None and predict_variances) + ) + predict_dur = self.model.predict_dur and ( + self.global_predict_dur or (param.get('ph_dur') is None and (predict_pitch or predict_variances)) + ) + flag = (predict_dur, predict_pitch, predict_variances) + predictor_flags.append(flag) + batches.append(self.preprocess_input( + param, idx=i, + load_dur=not flag[0] and (flag[1] or flag[2]), + load_pitch=not flag[1] and flag[2] + )) + + out_dir.mkdir(parents=True, exist_ok=True) + for i in range(num_runs): + results = [] + for param, flag, batch in tqdm.tqdm( + zip(params, predictor_flags, batches), desc='infer segments', total=len(params) + ): + if 'seed' in param: + torch.manual_seed(param["seed"] & 0xffff_ffff) + torch.cuda.manual_seed_all(param["seed"] & 0xffff_ffff) + elif seed >= 0: + torch.manual_seed(seed & 0xffff_ffff) + torch.cuda.manual_seed_all(seed & 0xffff_ffff) + param_copy = copy.deepcopy(param) + + flag_saved = ( + self.model.fs2.predict_dur, + self.model.predict_pitch, + self.model.predict_variances + ) + ( + self.model.fs2.predict_dur, + self.model.predict_pitch, + self.model.predict_variances + ) = flag + dur_pred, pitch_pred, variance_pred = self.forward_model(batch) + ( + self.model.fs2.predict_dur, + self.model.predict_pitch, + self.model.predict_variances + ) = flag_saved + + if dur_pred is not None and (self.auto_completion_mode or self.global_predict_dur): + dur_pred = dur_pred[0].cpu().numpy() + param_copy['ph_dur'] = ' '.join(str(round(dur, 6)) for dur in (dur_pred * self.timestep).tolist()) + if pitch_pred is not None and (self.auto_completion_mode or self.global_predict_pitch): + pitch_pred = pitch_pred[0].cpu().numpy() + f0_pred = librosa.midi_to_hz(pitch_pred) + param_copy['f0_seq'] = ' '.join([str(round(freq, 1)) for freq in f0_pred.tolist()]) + param_copy['f0_timestep'] = str(self.timestep) + variance_pred = { + k: v[0].cpu().numpy() + for k, v in variance_pred.items() + if (self.auto_completion_mode and param.get(k) is None) or k in self.variance_prediction_set + } + for v_name, v_pred in variance_pred.items(): + param_copy[v_name] = ' '.join([str(round(v, 4)) for v in v_pred.tolist()]) + param_copy[f'{v_name}_timestep'] = str(self.timestep) + + # Restore ph_spk_mix and spk_mix + if 'ph_spk_mix' in param_copy and 'spk_mix' in param_copy: + if 'ph_spk_mix_backup' in param_copy: + if param_copy['ph_spk_mix_backup'] is None: + del param_copy['ph_spk_mix'] + else: + param_copy['ph_spk_mix'] = param_copy['ph_spk_mix_backup'] + del param['ph_spk_mix_backup'] + if 'spk_mix_backup' in param_copy: + if param_copy['ph_spk_mix_backup'] is None: + del param_copy['spk_mix'] + else: + param_copy['spk_mix'] = param_copy['spk_mix_backup'] + del param['spk_mix_backup'] + + results.append(param_copy) + + if num_runs > 1: + filename = f'{title}-{str(i).zfill(3)}.ds' + else: + filename = f'{title}.ds' + save_path = out_dir / filename + with open(save_path, 'w', encoding='utf8') as f: + print(f'| save params: {save_path}') + json.dump(results, f, ensure_ascii=False, indent=2) diff --git a/inference/uni_pc.py b/inference/uni_pc.py new file mode 100644 index 0000000..4226570 --- /dev/null +++ b/inference/uni_pc.py @@ -0,0 +1,731 @@ +import torch +import torch.nn.functional as F +import math + + +class NoiseScheduleVP: + def __init__( + self, + schedule='discrete', + betas=None, + alphas_cumprod=None, + continuous_beta_0=0.1, + continuous_beta_1=20., + dtype=torch.float32, + ): + """Create a wrapper class for the forward SDE (VP type). + *** + Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t. + We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images. + *** + The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ). + We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper). + Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have: + log_alpha_t = self.marginal_log_mean_coeff(t) + sigma_t = self.marginal_std(t) + lambda_t = self.marginal_lambda(t) + Moreover, as lambda(t) is an invertible function, we also support its inverse function: + t = self.inverse_lambda(lambda_t) + =============================================================== + We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]). + 1. For discrete-time DPMs: + For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by: + t_i = (i + 1) / N + e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1. + We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3. + Args: + betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details) + alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details) + Note that we always have alphas_cumprod = cumprod(1 - betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`. + **Important**: Please pay special attention for the args for `alphas_cumprod`: + The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that + q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ). + Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have + alpha_{t_n} = \sqrt{\hat{alpha_n}}, + and + log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}). + 2. For continuous-time DPMs: + We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise + schedule are the default settings in DDPM and improved-DDPM: + Args: + beta_min: A `float` number. The smallest beta for the linear schedule. + beta_max: A `float` number. The largest beta for the linear schedule. + cosine_s: A `float` number. The hyperparameter in the cosine schedule. + cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule. + T: A `float` number. The ending time of the forward process. + =============================================================== + Args: + schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs, + 'linear' or 'cosine' for continuous-time DPMs. + Returns: + A wrapper object of the forward SDE (VP type). + + =============================================================== + Example: + # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1): + >>> ns = NoiseScheduleVP('discrete', betas=betas) + # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1): + >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod) + # For continuous-time DPMs (VPSDE), linear schedule: + >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.) + """ + + if schedule not in ['discrete', 'linear', 'cosine']: + raise ValueError("Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(schedule)) + + self.schedule = schedule + if schedule == 'discrete': + if betas is not None: + log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0) + else: + assert alphas_cumprod is not None + log_alphas = 0.5 * torch.log(alphas_cumprod) + self.total_N = len(log_alphas) + self.T = 1. + self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1)).to(dtype=dtype) + self.log_alpha_array = log_alphas.reshape((1, -1,)).to(dtype=dtype) + else: + self.total_N = 1000 + self.beta_0 = continuous_beta_0 + self.beta_1 = continuous_beta_1 + self.cosine_s = 0.008 + self.cosine_beta_max = 999. + self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s + self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.)) + self.schedule = schedule + if schedule == 'cosine': + # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T. + # Note that T = 0.9946 may be not the optimal setting. However, we find it works well. + self.T = 0.9946 + else: + self.T = 1. + + def marginal_log_mean_coeff(self, t): + """ + Compute log(alpha_t) of a given continuous-time label t in [0, T]. + """ + if self.schedule == 'discrete': + return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device), self.log_alpha_array.to(t.device)).reshape((-1)) + elif self.schedule == 'linear': + return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0 + elif self.schedule == 'cosine': + log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.)) + log_alpha_t = log_alpha_fn(t) - self.cosine_log_alpha_0 + return log_alpha_t + + def marginal_alpha(self, t): + """ + Compute alpha_t of a given continuous-time label t in [0, T]. + """ + return torch.exp(self.marginal_log_mean_coeff(t)) + + def marginal_std(self, t): + """ + Compute sigma_t of a given continuous-time label t in [0, T]. + """ + return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t))) + + def marginal_lambda(self, t): + """ + Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T]. + """ + log_mean_coeff = self.marginal_log_mean_coeff(t) + log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff)) + return log_mean_coeff - log_std + + def inverse_lambda(self, lamb): + """ + Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t. + """ + if self.schedule == 'linear': + tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb)) + Delta = self.beta_0**2 + tmp + return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0) + elif self.schedule == 'discrete': + log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb) + t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]), torch.flip(self.t_array.to(lamb.device), [1])) + return t.reshape((-1,)) + else: + log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb)) + t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s + t = t_fn(log_alpha) + return t + + +def model_wrapper( + model, + noise_schedule, + model_type="noise", + model_kwargs={}, + guidance_type="uncond", + condition=None, + unconditional_condition=None, + guidance_scale=1., + classifier_fn=None, + classifier_kwargs={}, +): + """Create a wrapper function for the noise prediction model. + """ + + def get_model_input_time(t_continuous): + """ + Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time. + For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N]. + For continuous-time DPMs, we just use `t_continuous`. + """ + if noise_schedule.schedule == 'discrete': + return (t_continuous - 1. / noise_schedule.total_N) * noise_schedule.total_N + else: + return t_continuous + + def noise_pred_fn(x, t_continuous, cond=None): + t_input = get_model_input_time(t_continuous) + if cond is None: + output = model(x, t_input, **model_kwargs) + else: + output = model(x, t_input, cond, **model_kwargs) + if model_type == "noise": + return output + elif model_type == "x_start": + alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous) + return (x - alpha_t * output) / sigma_t + elif model_type == "v": + alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous) + return alpha_t * output + sigma_t * x + elif model_type == "score": + sigma_t = noise_schedule.marginal_std(t_continuous) + return -sigma_t * output + + def cond_grad_fn(x, t_input): + """ + Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t). + """ + with torch.enable_grad(): + x_in = x.detach().requires_grad_(True) + log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs) + return torch.autograd.grad(log_prob.sum(), x_in)[0] + + def model_fn(x, t_continuous): + """ + The noise predicition model function that is used for DPM-Solver. + """ + if guidance_type == "uncond": + return noise_pred_fn(x, t_continuous) + elif guidance_type == "classifier": + assert classifier_fn is not None + t_input = get_model_input_time(t_continuous) + cond_grad = cond_grad_fn(x, t_input) + sigma_t = noise_schedule.marginal_std(t_continuous) + noise = noise_pred_fn(x, t_continuous) + return noise - guidance_scale * sigma_t * cond_grad + elif guidance_type == "classifier-free": + if guidance_scale == 1. or unconditional_condition is None: + return noise_pred_fn(x, t_continuous, cond=condition) + else: + x_in = torch.cat([x] * 2) + t_in = torch.cat([t_continuous] * 2) + c_in = torch.cat([unconditional_condition, condition]) + noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2) + return noise_uncond + guidance_scale * (noise - noise_uncond) + + assert model_type in ["noise", "x_start", "v"] + assert guidance_type in ["uncond", "classifier", "classifier-free"] + return model_fn + + +class UniPC: + def __init__( + self, + model_fn, + noise_schedule, + algorithm_type="data_prediction", + correcting_x0_fn=None, + correcting_xt_fn=None, + thresholding_max_val=1., + dynamic_thresholding_ratio=0.995, + variant='bh1' + ): + """Construct a UniPC. + + We support both data_prediction and noise_prediction. + """ + self.model = lambda x, t: model_fn(x, t.expand((x.shape[0]))) + self.noise_schedule = noise_schedule + assert algorithm_type in ["data_prediction", "noise_prediction"] + + if correcting_x0_fn == "dynamic_thresholding": + self.correcting_x0_fn = self.dynamic_thresholding_fn + else: + self.correcting_x0_fn = correcting_x0_fn + + self.correcting_xt_fn = correcting_xt_fn + self.dynamic_thresholding_ratio = dynamic_thresholding_ratio + self.thresholding_max_val = thresholding_max_val + + self.variant = variant + self.predict_x0 = algorithm_type == "data_prediction" + + def dynamic_thresholding_fn(self, x0, t=None): + """ + The dynamic thresholding method. + """ + dims = x0.dim() + p = self.dynamic_thresholding_ratio + s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1) + s = expand_dims(torch.maximum(s, self.thresholding_max_val * torch.ones_like(s).to(s.device)), dims) + x0 = torch.clamp(x0, -s, s) / s + return x0 + + def noise_prediction_fn(self, x, t): + """ + Return the noise prediction model. + """ + return self.model(x, t) + + def data_prediction_fn(self, x, t): + """ + Return the data prediction model (with corrector). + """ + noise = self.noise_prediction_fn(x, t) + alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t) + x0 = (x - sigma_t * noise) / alpha_t + if self.correcting_x0_fn is not None: + x0 = self.correcting_x0_fn(x0) + return x0 + + def model_fn(self, x, t): + """ + Convert the model to the noise prediction model or the data prediction model. + """ + if self.predict_x0: + return self.data_prediction_fn(x, t) + else: + return self.noise_prediction_fn(x, t) + + def get_time_steps(self, skip_type, t_T, t_0, N, device): + """Compute the intermediate time steps for sampling. + """ + if skip_type == 'logSNR': + lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device)) + lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device)) + logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device) + return self.noise_schedule.inverse_lambda(logSNR_steps) + elif skip_type == 'time_uniform': + return torch.linspace(t_T, t_0, N + 1).to(device) + elif skip_type == 'time_quadratic': + t_order = 2 + t = torch.linspace(t_T**(1. / t_order), t_0**(1. / t_order), N + 1).pow(t_order).to(device) + return t + else: + raise ValueError("Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type)) + + def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device): + """ + Get the order of each step for sampling by the singlestep DPM-Solver. + """ + if order == 3: + K = steps // 3 + 1 + if steps % 3 == 0: + orders = [3,] * (K - 2) + [2, 1] + elif steps % 3 == 1: + orders = [3,] * (K - 1) + [1] + else: + orders = [3,] * (K - 1) + [2] + elif order == 2: + if steps % 2 == 0: + K = steps // 2 + orders = [2,] * K + else: + K = steps // 2 + 1 + orders = [2,] * (K - 1) + [1] + elif order == 1: + K = steps + orders = [1,] * steps + else: + raise ValueError("'order' must be '1' or '2' or '3'.") + if skip_type == 'logSNR': + # To reproduce the results in DPM-Solver paper + timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device) + else: + timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[torch.cumsum(torch.tensor([0,] + orders), 0).to(device)] + return timesteps_outer, orders + + def denoise_to_zero_fn(self, x, s): + """ + Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization. + """ + return self.data_prediction_fn(x, s) + + def multistep_uni_pc_update(self, x, model_prev_list, t_prev_list, t, order, **kwargs): + if len(t.shape) == 0: + t = t.view(-1) + if 'bh' in self.variant: + return self.multistep_uni_pc_bh_update(x, model_prev_list, t_prev_list, t, order, **kwargs) + else: + assert self.variant == 'vary_coeff' + return self.multistep_uni_pc_vary_update(x, model_prev_list, t_prev_list, t, order, **kwargs) + + def multistep_uni_pc_vary_update(self, x, model_prev_list, t_prev_list, t, order, use_corrector=True): + #print(f'using unified predictor-corrector with order {order} (solver type: vary coeff)') + ns = self.noise_schedule + assert order <= len(model_prev_list) + + # first compute rks + t_prev_0 = t_prev_list[-1] + lambda_prev_0 = ns.marginal_lambda(t_prev_0) + lambda_t = ns.marginal_lambda(t) + model_prev_0 = model_prev_list[-1] + sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) + log_alpha_t = ns.marginal_log_mean_coeff(t) + alpha_t = torch.exp(log_alpha_t) + + h = lambda_t - lambda_prev_0 + + rks = [] + D1s = [] + for i in range(1, order): + t_prev_i = t_prev_list[-(i + 1)] + model_prev_i = model_prev_list[-(i + 1)] + lambda_prev_i = ns.marginal_lambda(t_prev_i) + rk = (lambda_prev_i - lambda_prev_0) / h + rks.append(rk) + D1s.append((model_prev_i - model_prev_0) / rk) + + rks.append(1.) + rks = torch.tensor(rks, device=x.device) + + K = len(rks) + # build C matrix + C = [] + + col = torch.ones_like(rks) + for k in range(1, K + 1): + C.append(col) + col = col * rks / (k + 1) + C = torch.stack(C, dim=1) + + if len(D1s) > 0: + D1s = torch.stack(D1s, dim=1) # (B, K) + C_inv_p = torch.linalg.inv(C[:-1, :-1]) + A_p = C_inv_p + + if use_corrector: + #print('using corrector') + C_inv = torch.linalg.inv(C) + A_c = C_inv + + hh = -h if self.predict_x0 else h + h_phi_1 = torch.expm1(hh) + h_phi_ks = [] + factorial_k = 1 + h_phi_k = h_phi_1 + for k in range(1, K + 2): + h_phi_ks.append(h_phi_k) + h_phi_k = h_phi_k / hh - 1 / factorial_k + factorial_k *= (k + 1) + + model_t = None + if self.predict_x0: + x_t_ = ( + sigma_t / sigma_prev_0 * x + - alpha_t * h_phi_1 * model_prev_0 + ) + # now predictor + x_t = x_t_ + if len(D1s) > 0: + # compute the residuals for predictor + for k in range(K - 1): + x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k]) + # now corrector + if use_corrector: + model_t = self.model_fn(x_t, t) + D1_t = (model_t - model_prev_0) + x_t = x_t_ + k = 0 + for k in range(K - 1): + x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1]) + x_t = x_t - alpha_t * h_phi_ks[K] * (D1_t * A_c[k][-1]) + else: + log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t) + x_t_ = ( + (torch.exp(log_alpha_t - log_alpha_prev_0)) * x + - (sigma_t * h_phi_1) * model_prev_0 + ) + # now predictor + x_t = x_t_ + if len(D1s) > 0: + # compute the residuals for predictor + for k in range(K - 1): + x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k]) + # now corrector + if use_corrector: + model_t = self.model_fn(x_t, t) + D1_t = (model_t - model_prev_0) + x_t = x_t_ + k = 0 + for k in range(K - 1): + x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1]) + x_t = x_t - sigma_t * h_phi_ks[K] * (D1_t * A_c[k][-1]) + return x_t, model_t + + def multistep_uni_pc_bh_update(self, x, model_prev_list, t_prev_list, t, order, x_t=None, use_corrector=True): + #print(f'using unified predictor-corrector with order {order} (solver type: B(h))') + ns = self.noise_schedule + assert order <= len(model_prev_list) + + # first compute rks + t_prev_0 = t_prev_list[-1] + lambda_prev_0 = ns.marginal_lambda(t_prev_0) + lambda_t = ns.marginal_lambda(t) + model_prev_0 = model_prev_list[-1] + sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) + log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t) + alpha_t = torch.exp(log_alpha_t) + + h = lambda_t - lambda_prev_0 + + rks = [] + D1s = [] + for i in range(1, order): + t_prev_i = t_prev_list[-(i + 1)] + model_prev_i = model_prev_list[-(i + 1)] + lambda_prev_i = ns.marginal_lambda(t_prev_i) + rk = (lambda_prev_i - lambda_prev_0) / h + rks.append(rk) + D1s.append((model_prev_i - model_prev_0) / rk) + + rks.append(1.) + rks = torch.tensor(rks, device=x.device) + + R = [] + b = [] + + hh = -h if self.predict_x0 else h + h_phi_1 = torch.expm1(hh) # h\phi_1(h) = e^h - 1 + h_phi_k = h_phi_1 / hh - 1 + + factorial_i = 1 + + if self.variant == 'bh1': + B_h = hh + elif self.variant == 'bh2': + B_h = torch.expm1(hh) + else: + raise NotImplementedError() + + for i in range(1, order + 1): + R.append(torch.pow(rks, i - 1)) + b.append(h_phi_k * factorial_i / B_h) + factorial_i *= (i + 1) + h_phi_k = h_phi_k / hh - 1 / factorial_i + + R = torch.stack(R) + b = torch.cat(b) + + # now predictor + use_predictor = len(D1s) > 0 and x_t is None + if len(D1s) > 0: + D1s = torch.stack(D1s, dim=1) # (B, K) + if x_t is None: + # for order 2, we use a simplified version + if order == 2: + rhos_p = torch.tensor([0.5], device=b.device) + else: + rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1]) + else: + D1s = None + + if use_corrector: + #print('using corrector') + # for order 1, we use a simplified version + if order == 1: + rhos_c = torch.tensor([0.5], device=b.device) + else: + rhos_c = torch.linalg.solve(R, b) + + model_t = None + if self.predict_x0: + x_t_ = ( + sigma_t / sigma_prev_0 * x + - alpha_t * h_phi_1 * model_prev_0 + ) + + if x_t is None: + if use_predictor: + pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s) + else: + pred_res = 0 + x_t = x_t_ - alpha_t * B_h * pred_res + + if use_corrector: + model_t = self.model_fn(x_t, t) + if D1s is not None: + corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s) + else: + corr_res = 0 + D1_t = (model_t - model_prev_0) + x_t = x_t_ - alpha_t * B_h * (corr_res + rhos_c[-1] * D1_t) + else: + x_t_ = ( + torch.exp(log_alpha_t - log_alpha_prev_0) * x + - sigma_t * h_phi_1 * model_prev_0 + ) + if x_t is None: + if use_predictor: + pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s) + else: + pred_res = 0 + x_t = x_t_ - sigma_t * B_h * pred_res + + if use_corrector: + model_t = self.model_fn(x_t, t) + if D1s is not None: + corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s) + else: + corr_res = 0 + D1_t = (model_t - model_prev_0) + x_t = x_t_ - sigma_t * B_h * (corr_res + rhos_c[-1] * D1_t) + return x_t, model_t + + def sample(self, x, steps=20, t_start=None, t_end=None, order=2, skip_type='time_uniform', + method='multistep', lower_order_final=True, denoise_to_zero=False, atol=0.0078, rtol=0.05, return_intermediate=False, + ): + """ + Compute the sample at time `t_end` by UniPC, given the initial `x` at time `t_start`. + """ + t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end + t_T = self.noise_schedule.T if t_start is None else t_start + assert t_0 > 0 and t_T > 0, "Time range needs to be greater than 0. For discrete-time DPMs, it needs to be in [1 / N, 1], where N is the length of betas array" + if return_intermediate: + assert method in ['multistep', 'singlestep', 'singlestep_fixed'], "Cannot use adaptive solver when saving intermediate values" + if self.correcting_xt_fn is not None: + assert method in ['multistep', 'singlestep', 'singlestep_fixed'], "Cannot use adaptive solver when correcting_xt_fn is not None" + device = x.device + intermediates = [] + with torch.no_grad(): + if method == 'multistep': + assert steps >= order + timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device) + assert timesteps.shape[0] - 1 == steps + # Init the initial values. + step = 0 + t = timesteps[step] + t_prev_list = [t] + model_prev_list = [self.model_fn(x, t)] + if self.correcting_xt_fn is not None: + x = self.correcting_xt_fn(x, t, step) + if return_intermediate: + intermediates.append(x) + + # Init the first `order` values by lower order multistep UniPC. + for step in range(1, order): + t = timesteps[step] + x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, t, step, use_corrector=True) + if model_x is None: + model_x = self.model_fn(x, t) + if self.correcting_xt_fn is not None: + x = self.correcting_xt_fn(x, t, step) + if return_intermediate: + intermediates.append(x) + t_prev_list.append(t) + model_prev_list.append(model_x) + + # Compute the remaining values by `order`-th order multistep DPM-Solver. + for step in range(order, steps + 1): + t = timesteps[step] + if lower_order_final: + step_order = min(order, steps + 1 - step) + else: + step_order = order + if step == steps: + #print('do not run corrector at the last step') + use_corrector = False + else: + use_corrector = True + x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, t, step_order, use_corrector=use_corrector) + if self.correcting_xt_fn is not None: + x = self.correcting_xt_fn(x, t, step) + if return_intermediate: + intermediates.append(x) + for i in range(order - 1): + t_prev_list[i] = t_prev_list[i + 1] + model_prev_list[i] = model_prev_list[i + 1] + t_prev_list[-1] = t + # We do not need to evaluate the final model value. + if step < steps: + if model_x is None: + model_x = self.model_fn(x, t) + model_prev_list[-1] = model_x + else: + raise ValueError("Got wrong method {}".format(method)) + + if denoise_to_zero: + t = torch.ones((1,)).to(device) * t_0 + x = self.denoise_to_zero_fn(x, t) + if self.correcting_xt_fn is not None: + x = self.correcting_xt_fn(x, t, step + 1) + if return_intermediate: + intermediates.append(x) + if return_intermediate: + return x, intermediates + else: + return x + + +############################################################# +# other utility functions +############################################################# + +def interpolate_fn(x, xp, yp): + """ + A piecewise linear function y = f(x), using xp and yp as keypoints. + We implement f(x) in a differentiable way (i.e. applicable for autograd). + The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.) + + Args: + x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver). + xp: PyTorch tensor with shape [C, K], where K is the number of keypoints. + yp: PyTorch tensor with shape [C, K]. + Returns: + The function values f(x), with shape [N, C]. + """ + N, K = x.shape[0], xp.shape[1] + all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2) + sorted_all_x, x_indices = torch.sort(all_x, dim=2) + x_idx = torch.argmin(x_indices, dim=2) + cand_start_idx = x_idx - 1 + start_idx = torch.where( + torch.eq(x_idx, 0), + torch.tensor(1, device=x.device), + torch.where( + torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx, + ), + ) + end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1) + start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2) + end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2) + start_idx2 = torch.where( + torch.eq(x_idx, 0), + torch.tensor(0, device=x.device), + torch.where( + torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx, + ), + ) + y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1) + start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2) + end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2) + cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x) + return cand + + +def expand_dims(v, dims): + """ + Expand the tensor `v` to the dim `dims`. + + Args: + `v`: a PyTorch tensor with shape [N]. + `dim`: a `int`. + Returns: + a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`. + """ + return v[(...,) + (None,)*(dims - 1)] \ No newline at end of file diff --git a/inference/val_nsf_hifigan.py b/inference/val_nsf_hifigan.py new file mode 100644 index 0000000..c0ed107 --- /dev/null +++ b/inference/val_nsf_hifigan.py @@ -0,0 +1,78 @@ +import os +import sys + +import librosa +import numpy as np +import resampy +import torch +import torchcrepe +import tqdm + +from utils.binarizer_utils import get_pitch_parselmouth, get_mel_torch +from modules.vocoders.nsf_hifigan import NsfHifiGAN +from utils.infer_utils import save_wav +from utils.hparams import set_hparams, hparams + +sys.argv = [ + 'inference/svs/ds_acoustic.py', + '--config', + 'configs/acoustic.yaml', +] + + +def get_pitch(wav_data, mel, hparams, threshold=0.3): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + # crepe只支持16khz采样率，需要重采样 + wav16k = resampy.resample(wav_data, hparams['audio_sample_rate'], 16000) + wav16k_torch = torch.FloatTensor(wav16k).unsqueeze(0).to(device) + + # 频率范围 + f0_min = 40 + f0_max = 800 + + # 重采样后按照hopsize=80,也就是5ms一帧分析f0 + f0, pd = torchcrepe.predict(wav16k_torch, 16000, 80, f0_min, f0_max, pad=True, model='full', batch_size=1024, + device=device, return_periodicity=True) + + # 滤波，去掉静音，设置uv阈值，参考原仓库readme + pd = torchcrepe.filter.median(pd, 3) + pd = torchcrepe.threshold.Silence(-60.)(pd, wav16k_torch, 16000, 80) + f0 = torchcrepe.threshold.At(threshold)(f0, pd) + f0 = torchcrepe.filter.mean(f0, 3) + + # 将nan频率（uv部分）转换为0频率 + f0 = torch.where(torch.isnan(f0), torch.full_like(f0, 0), f0) + + # 去掉0频率，并线性插值 + nzindex = torch.nonzero(f0[0]).squeeze() + f0 = torch.index_select(f0[0], dim=0, index=nzindex).cpu().numpy() + time_org = 0.005 * nzindex.cpu().numpy() + time_frame = np.arange(len(mel)) * hparams['hop_size'] / hparams['audio_sample_rate'] + f0 = np.interp(time_frame, time_org, f0, left=f0[0], right=f0[-1]) + return f0 + + +set_hparams() +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +vocoder = NsfHifiGAN() +in_path = 'path/to/input/wavs' +out_path = 'path/to/output/wavs' +os.makedirs(out_path, exist_ok=True) +for filename in tqdm.tqdm(os.listdir(in_path)): + if not filename.endswith('.wav'): + continue + wav, _ = librosa.load(os.path.join(in_path, filename), sr=hparams['audio_sample_rate'], mono=True) + mel = get_mel_torch( + wav, hparams['audio_sample_rate'], num_mel_bins=hparams['audio_num_mel_bins'], + hop_size=hparams['hop_size'], win_size=hparams['win_size'], fft_size=hparams['fft_size'], + fmin=hparams['fmin'], fmax=hparams['fmax'], + device=device + ) + + f0, _ = get_pitch_parselmouth( + wav, samplerate=hparams['audio_sample_rate'], length=len(mel), + hop_size=hparams['hop_size'] + ) + + wav_out = vocoder.spec2wav(mel, f0=f0) + save_wav(wav_out, os.path.join(out_path, filename), hparams['audio_sample_rate']) diff --git a/modules/__init__.py b/modules/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/modules/aux_decoder/__init__.py b/modules/aux_decoder/__init__.py new file mode 100644 index 0000000..54ceb21 --- /dev/null +++ b/modules/aux_decoder/__init__.py @@ -0,0 +1,70 @@ +import torch.nn +from torch import nn + +from .convnext import ConvNeXtDecoder +from utils import filter_kwargs + +AUX_DECODERS = { + 'convnext': ConvNeXtDecoder +} +AUX_LOSSES = { + 'convnext': nn.L1Loss +} + + +def build_aux_decoder( + in_dims: int, out_dims: int, + aux_decoder_arch: str, aux_decoder_args: dict +) -> torch.nn.Module: + decoder_cls = AUX_DECODERS[aux_decoder_arch] + kwargs = filter_kwargs(aux_decoder_args, decoder_cls) + return AUX_DECODERS[aux_decoder_arch](in_dims, out_dims, **kwargs) + + +def build_aux_loss(aux_decoder_arch): + return AUX_LOSSES[aux_decoder_arch]() + + +class AuxDecoderAdaptor(nn.Module): + def __init__(self, in_dims: int, out_dims: int, num_feats: int, + spec_min: list, spec_max: list, + aux_decoder_arch: str, aux_decoder_args: dict): + super().__init__() + self.decoder = build_aux_decoder( + in_dims=in_dims, out_dims=out_dims * num_feats, + aux_decoder_arch=aux_decoder_arch, + aux_decoder_args=aux_decoder_args + ) + self.out_dims = out_dims + self.n_feats = num_feats + if spec_min is not None and spec_max is not None: + # spec: [B, T, M] or [B, F, T, M] + # spec_min and spec_max: [1, 1, M] or [1, 1, F, M] => transpose(-3, -2) => [1, 1, M] or [1, F, 1, M] + spec_min = torch.FloatTensor(spec_min)[None, None, :].transpose(-3, -2) + spec_max = torch.FloatTensor(spec_max)[None, None, :].transpose(-3, -2) + self.register_buffer('spec_min', spec_min, persistent=False) + self.register_buffer('spec_max', spec_max, persistent=False) + + def norm_spec(self, x): + k = (self.spec_max - self.spec_min) / 2. + b = (self.spec_max + self.spec_min) / 2. + return (x - b) / k + + def denorm_spec(self, x): + k = (self.spec_max - self.spec_min) / 2. + b = (self.spec_max + self.spec_min) / 2. + return x * k + b + + def forward(self, condition, infer=False): + x = self.decoder(condition, infer=infer) # [B, T, F x C] + + if self.n_feats > 1: + # This is the temporary solution since PyTorch 1.13 + # does not support exporting aten::unflatten to ONNX + # x = x.unflatten(dim=2, sizes=(self.n_feats, self.in_dims)) + x = x.reshape(-1, x.shape[1], self.n_feats, self.out_dims) # [B, T, F, C] + x = x.transpose(1, 2) # [B, F, T, C] + if infer: + x = self.denorm_spec(x) + + return x # [B, T, C] or [B, F, T, C] diff --git a/modules/aux_decoder/convnext.py b/modules/aux_decoder/convnext.py new file mode 100644 index 0000000..ad3fa1e --- /dev/null +++ b/modules/aux_decoder/convnext.py @@ -0,0 +1,89 @@ +from typing import Optional + +import torch +import torch.nn as nn + +from modules.commons.common_layers import AdamWConv1d + + +class ConvNeXtBlock(nn.Module): + """ConvNeXt Block adapted from https://github.com/facebookresearch/ConvNeXt to 1D audio signal. + + Args: + dim (int): Number of input channels. + intermediate_dim (int): Dimensionality of the intermediate layer. + layer_scale_init_value (float, optional): Initial value for the layer scale. None means no scaling. + Defaults to None. + """ + + def __init__( + self, + dim: int, + intermediate_dim: int, + layer_scale_init_value: Optional[float] = None, drop_out: float = 0.0 + + ): + super().__init__() + self.dwconv = nn.Conv1d(dim, dim, kernel_size=7, padding=3, groups=dim) # depthwise conv + + self.norm = nn.LayerNorm(dim, eps=1e-6) + self.pwconv1 = nn.Linear(dim, intermediate_dim) # pointwise/1x1 convs, implemented with linear layers + self.act = nn.GELU() + self.pwconv2 = nn.Linear(intermediate_dim, dim) + self.gamma = ( + nn.Parameter(layer_scale_init_value * torch.ones(dim), requires_grad=True) + if layer_scale_init_value > 0 + else None + ) + # self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.drop_path = nn.Identity() + self.dropout = nn.Dropout(drop_out) if drop_out > 0. else nn.Identity() + + def forward(self, x: torch.Tensor, ) -> torch.Tensor: + residual = x + x = self.dwconv(x) + x = x.transpose(1, 2) # (B, C, T) -> (B, T, C) + + x = self.norm(x) + x = self.pwconv1(x) + x = self.act(x) + x = self.pwconv2(x) + if self.gamma is not None: + x = self.gamma * x + x = x.transpose(1, 2) # (B, T, C) -> (B, C, T) + x = self.dropout(x) + + x = residual + self.drop_path(x) + return x + + +class ConvNeXtDecoder(nn.Module): + def __init__( + self, in_dims, out_dims, /, *, + num_channels=512, num_layers=6, kernel_size=7, dropout_rate=0.1 + ): + super().__init__() + self.inconv = nn.Conv1d( + in_dims, num_channels, kernel_size, + stride=1, padding=(kernel_size - 1) // 2 + ) + self.conv = nn.ModuleList( + ConvNeXtBlock( + dim=num_channels, intermediate_dim=num_channels * 4, + layer_scale_init_value=1e-6, drop_out=dropout_rate + ) for _ in range(num_layers) + ) + self.outconv = AdamWConv1d( + num_channels, out_dims, kernel_size, + stride=1, padding=(kernel_size - 1) // 2 + ) + + # noinspection PyUnusedLocal + def forward(self, x, infer=False): + x = x.transpose(1, 2) + x = self.inconv(x) + for conv in self.conv: + x = conv(x) + x = self.outconv(x) + x = x.transpose(1, 2) + return x diff --git a/modules/backbones/__init__.py b/modules/backbones/__init__.py new file mode 100644 index 0000000..ebd9034 --- /dev/null +++ b/modules/backbones/__init__.py @@ -0,0 +1,20 @@ +import torch.nn +from modules.backbones.wavenet import WaveNet +from modules.backbones.lynxnet import LYNXNet +from modules.backbones.lynxnet2 import LYNXNet2 +from utils import filter_kwargs + +BACKBONES = { + 'wavenet': WaveNet, + 'lynxnet': LYNXNet, + 'lynxnet2': LYNXNet2, +} + + +def build_backbone( + out_dims: int, num_feats: int, + backbone_type: str, backbone_args: dict +) -> torch.nn.Module: + backbone = BACKBONES[backbone_type] + kwargs = filter_kwargs(backbone_args, backbone) + return BACKBONES[backbone_type](out_dims, num_feats, **kwargs) diff --git a/modules/backbones/lynxnet.py b/modules/backbones/lynxnet.py new file mode 100644 index 0000000..9529d1e --- /dev/null +++ b/modules/backbones/lynxnet.py @@ -0,0 +1,147 @@ +# refer to： +# https://github.com/CNChTu/Diffusion-SVC/blob/v2.0_dev/diffusion/naive_v2/model_conformer_naive.py +# https://github.com/CNChTu/Diffusion-SVC/blob/v2.0_dev/diffusion/naive_v2/naive_v2_diff.py + +import torch.nn as nn +import torch.nn.functional as F + +from modules.commons.common_layers import SinusoidalPosEmb, SwiGLU, Transpose, AdamWConv1d +from modules.commons.common_layers import KaimingNormalConv1d as Conv1d +from utils.hparams import hparams + + +class LYNXConvModule(nn.Module): + @staticmethod + def calc_same_padding(kernel_size): + pad = kernel_size // 2 + return pad, pad - (kernel_size + 1) % 2 + + def __init__(self, dim, expansion_factor, kernel_size=31, activation='PReLU', dropout=0.0): + super().__init__() + inner_dim = dim * expansion_factor + activation_classes = { + 'SiLU': nn.SiLU, + 'ReLU': nn.ReLU, + 'PReLU': lambda: nn.PReLU(inner_dim) + } + activation = activation if activation is not None else 'PReLU' + if activation not in activation_classes: + raise ValueError(f'{activation} is not a valid activation') + _activation = activation_classes[activation]() + padding = self.calc_same_padding(kernel_size) + if float(dropout) > 0.: + _dropout = nn.Dropout(dropout) + else: + _dropout = nn.Identity() + self.net = nn.Sequential( + nn.LayerNorm(dim), + Transpose((1, 2)), + nn.Conv1d(dim, inner_dim * 2, 1), + SwiGLU(dim=1), + nn.Conv1d(inner_dim, inner_dim, kernel_size=kernel_size, padding=padding[0], groups=inner_dim), + _activation, + nn.Conv1d(inner_dim, dim, 1), + Transpose((1, 2)), + _dropout + ) + + def forward(self, x): + return self.net(x) + + +class LYNXNetResidualLayer(nn.Module): + def __init__(self, dim_cond, dim, expansion_factor, kernel_size=31, activation='PReLU', dropout=0.0): + super().__init__() + self.diffusion_projection = nn.Conv1d(dim, dim, 1) + self.conditioner_projection = nn.Conv1d(dim_cond, dim, 1) + self.convmodule = LYNXConvModule(dim=dim, expansion_factor=expansion_factor, kernel_size=kernel_size, + activation=activation, dropout=dropout) + + def forward(self, x, conditioner, diffusion_step, front_cond_inject=False): + if front_cond_inject: + x = x + self.conditioner_projection(conditioner) + res_x = x + else: + res_x = x + x = x + self.conditioner_projection(conditioner) + x = x + self.diffusion_projection(diffusion_step) + x = x.transpose(1, 2) + x = self.convmodule(x) # (#batch, dim, length) + x = x.transpose(1, 2) + res_x + return x # (#batch, length, dim) + + +class LYNXNet(nn.Module): + def __init__(self, in_dims, n_feats, *, num_layers=6, num_channels=512, expansion_factor=2, kernel_size=31, + activation='PReLU', dropout_rate=0.0, strong_cond=False): + """ + LYNXNet(Linear Gated Depthwise Separable Convolution Network) + TIPS:You can control the style of the generated results by modifying the 'activation', + - 'PReLU'(default) : Similar to WaveNet + - 'SiLU' : Voice will be more pronounced, not recommended for use under DDPM + - 'ReLU' : Contrary to 'SiLU', Voice will be weakened + """ + super().__init__() + self.in_dims = in_dims + self.n_feats = n_feats + self.input_projection = Conv1d(in_dims * n_feats, num_channels, 1) + self.diffusion_embedding = nn.Sequential( + SinusoidalPosEmb(num_channels), + nn.Linear(num_channels, num_channels * 4), + nn.GELU(), + nn.Linear(num_channels * 4, num_channels), + ) + self.residual_layers = nn.ModuleList( + [ + LYNXNetResidualLayer( + dim_cond=hparams['hidden_size'], + dim=num_channels, + expansion_factor=expansion_factor, + kernel_size=kernel_size, + activation=activation, + dropout=dropout_rate + ) + for _ in range(num_layers) + ] + ) + self.norm = nn.LayerNorm(num_channels) + self.output_projection = AdamWConv1d(num_channels, in_dims * n_feats, kernel_size=1) + self.strong_cond = strong_cond + nn.init.zeros_(self.output_projection.weight) + + def forward(self, spec, diffusion_step, cond): + """ + :param spec: [B, F, M, T] + :param diffusion_step: [B, 1] + :param cond: [B, H, T] + :return: + """ + + if self.n_feats == 1: + x = spec[:, 0] # [B, M, T] + else: + x = spec.flatten(start_dim=1, end_dim=2) # [B, F x M, T] + + x = self.input_projection(x) # x [B, residual_channel, T] + if not self.strong_cond: + x = F.gelu(x) + + diffusion_step = self.diffusion_embedding(diffusion_step).unsqueeze(-1) + + for layer in self.residual_layers: + x = layer(x, cond, diffusion_step, front_cond_inject=self.strong_cond) + + # post-norm + x = self.norm(x.transpose(1, 2)).transpose(1, 2) + + # output_projection + x = self.output_projection(x) # [B, 128, T] + + if self.n_feats == 1: + x = x[:, None, :, :] + else: + # This is the temporary solution since PyTorch 1.13 + # does not support exporting aten::unflatten to ONNX + # x = x.unflatten(dim=1, sizes=(self.n_feats, self.in_dims)) + x = x.reshape(-1, self.n_feats, self.in_dims, x.shape[2]) + return x diff --git a/modules/backbones/lynxnet2.py b/modules/backbones/lynxnet2.py new file mode 100644 index 0000000..6e55d5f --- /dev/null +++ b/modules/backbones/lynxnet2.py @@ -0,0 +1,115 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from modules.commons.common_layers import SinusoidalPosEmb, SwiGLU, ATanGLU, Transpose, AdamWLinear +from utils.hparams import hparams + + +class LYNXNet2Block(nn.Module): + def __init__(self, dim, expansion_factor, kernel_size=31, dropout=0., glu_type='swiglu'): + super().__init__() + inner_dim = int(dim * expansion_factor) + if glu_type == 'swiglu': + _glu = SwiGLU() + elif glu_type == 'atanglu': + _glu = ATanGLU() + else: + raise ValueError(f'{glu_type} is not a valid activation') + if float(dropout) > 0.: + _dropout = nn.Dropout(dropout) + else: + _dropout = nn.Identity() + self.net = nn.Sequential( + nn.LayerNorm(dim), + Transpose((1, 2)), + nn.Conv1d(dim, dim, kernel_size=kernel_size, padding=kernel_size // 2, groups=dim), + Transpose((1, 2)), + nn.Linear(dim, inner_dim * 2), + _glu, + nn.Linear(inner_dim, inner_dim * 2), + _glu, + nn.Linear(inner_dim, dim), + _dropout + ) + + def forward(self, x): + return x + self.net(x) + + +class LYNXNet2(nn.Module): + def __init__(self, in_dims, n_feats, *, num_layers=6, num_channels=512, expansion_factor=1, kernel_size=31, + dropout_rate=0.0, use_conditioner_cache=False, glu_type='swiglu'): + """ + LYNXNet2(Linear Gated Depthwise Separable Convolution Network Version 2) + """ + super().__init__() + self.in_dims = in_dims + self.n_feats = n_feats + self.input_projection = nn.Linear(in_dims * n_feats, num_channels) + self.use_conditioner_cache = use_conditioner_cache + if self.use_conditioner_cache: + # Conv1d is used for condition cache compatibility + self.conditioner_projection = nn.Conv1d(hparams['hidden_size'], num_channels, 1) + else: + self.conditioner_projection = nn.Linear(hparams['hidden_size'], num_channels) + self.diffusion_embedding = nn.Sequential( + SinusoidalPosEmb(num_channels), + nn.Linear(num_channels, num_channels * 4), + nn.GELU(), + nn.Linear(num_channels * 4, num_channels), + ) + self.residual_layers = nn.ModuleList( + [ + LYNXNet2Block( + dim=num_channels, + expansion_factor=expansion_factor, + kernel_size=kernel_size, + dropout=dropout_rate, + glu_type=glu_type + ) + for _ in range(num_layers) + ] + ) + self.norm = nn.LayerNorm(num_channels) + self.output_projection = AdamWLinear(num_channels, in_dims * n_feats) + nn.init.kaiming_normal_(self.input_projection.weight) + nn.init.kaiming_normal_(self.conditioner_projection.weight) + nn.init.zeros_(self.output_projection.weight) + + def forward(self, spec, diffusion_step, cond): + """ + :param spec: [B, F, M, T] + :param diffusion_step: [B, 1] + :param cond: [B, H, T] + :return: + """ + + if self.n_feats == 1: + x = spec[:, 0] # [B, M, T] + else: + x = spec.flatten(start_dim=1, end_dim=2) # [B, F x M, T] + + x = self.input_projection(x.transpose(1, 2)) # [B, T, F x M] + if self.use_conditioner_cache: + x = x + self.conditioner_projection(cond).transpose(1, 2) + else: + x = x + self.conditioner_projection(cond.transpose(1, 2)) + x = x + self.diffusion_embedding(diffusion_step).unsqueeze(1) + + for layer in self.residual_layers: + x = layer(x) + + # post-norm + x = self.norm(x) + + # output projection + x = self.output_projection(x).transpose(1, 2) # [B, 128, T] + + if self.n_feats == 1: + x = x[:, None, :, :] + else: + # Using reshape instead of unflatten for ONNX export compatibility + # x = x.unflatten(dim=1, sizes=(self.n_feats, self.in_dims)) + x = x.reshape(-1, self.n_feats, self.in_dims, x.shape[2]) + return x diff --git a/modules/backbones/wavenet.py b/modules/backbones/wavenet.py new file mode 100644 index 0000000..77ccc64 --- /dev/null +++ b/modules/backbones/wavenet.py @@ -0,0 +1,104 @@ +import math +from math import sqrt + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from modules.commons.common_layers import SinusoidalPosEmb, AdamWConv1d +from modules.commons.common_layers import KaimingNormalConv1d as Conv1d +from utils.hparams import hparams + + +class ResidualBlock(nn.Module): + def __init__(self, encoder_hidden, residual_channels, dilation): + super().__init__() + self.residual_channels = residual_channels + self.dilated_conv = nn.Conv1d( + residual_channels, + 2 * residual_channels, + kernel_size=3, + padding=dilation, + dilation=dilation + ) + self.diffusion_projection = nn.Linear(residual_channels, residual_channels) + self.conditioner_projection = nn.Conv1d(encoder_hidden, 2 * residual_channels, 1) + self.output_projection = nn.Conv1d(residual_channels, 2 * residual_channels, 1) + + def forward(self, x, conditioner, diffusion_step): + diffusion_step = self.diffusion_projection(diffusion_step).unsqueeze(-1) + conditioner = self.conditioner_projection(conditioner) + y = x + diffusion_step + + y = self.dilated_conv(y) + conditioner + + # Using torch.split instead of torch.chunk to avoid using onnx::Slice + gate, filter = torch.split(y, [self.residual_channels, self.residual_channels], dim=1) + y = torch.sigmoid(gate) * torch.tanh(filter) + + y = self.output_projection(y) + + # Using torch.split instead of torch.chunk to avoid using onnx::Slice + residual, skip = torch.split(y, [self.residual_channels, self.residual_channels], dim=1) + return (x + residual) / math.sqrt(2.0), skip + + +class WaveNet(nn.Module): + def __init__(self, in_dims, n_feats, *, num_layers=20, num_channels=256, dilation_cycle_length=4): + super().__init__() + self.in_dims = in_dims + self.n_feats = n_feats + self.input_projection = Conv1d(in_dims * n_feats, num_channels, 1) + self.diffusion_embedding = SinusoidalPosEmb(num_channels) + self.mlp = nn.Sequential( + nn.Linear(num_channels, num_channels * 4), + nn.Mish(), + nn.Linear(num_channels * 4, num_channels) + ) + self.residual_layers = nn.ModuleList([ + ResidualBlock( + encoder_hidden=hparams['hidden_size'], + residual_channels=num_channels, + dilation=2 ** (i % dilation_cycle_length) + ) + for i in range(num_layers) + ]) + self.skip_projection = Conv1d(num_channels, num_channels, 1) + self.output_projection = AdamWConv1d(num_channels, in_dims * n_feats, 1) + nn.init.zeros_(self.output_projection.weight) + + def forward(self, spec, diffusion_step, cond): + """ + :param spec: [B, F, M, T] + :param diffusion_step: [B, 1] + :param cond: [B, H, T] + :return: + """ + if self.n_feats == 1: + # Use indexing instead of squeeze to avoid emitting an onnx::If + # whose branches have different rank, which breaks shape inference + # for the downstream Conv on PyTorch >= 2.0. + x = spec[:, 0] # [B, M, T] + else: + x = spec.flatten(start_dim=1, end_dim=2) # [B, F x M, T] + x = self.input_projection(x) # [B, C, T] + + x = F.relu(x) + diffusion_step = self.diffusion_embedding(diffusion_step) + diffusion_step = self.mlp(diffusion_step) + skip = [] + for layer in self.residual_layers: + x, skip_connection = layer(x, cond, diffusion_step) + skip.append(skip_connection) + + x = torch.sum(torch.stack(skip), dim=0) / sqrt(len(self.residual_layers)) + x = self.skip_projection(x) + x = F.relu(x) + x = self.output_projection(x) # [B, M, T] + if self.n_feats == 1: + x = x[:, None, :, :] + else: + # Using reshape instead of unflatten for ONNX export compatibility + # x = x.unflatten(dim=1, sizes=(self.n_feats, self.in_dims)) + x = x.reshape(-1, self.n_feats, self.in_dims, x.shape[2]) + return x diff --git a/modules/commons/common_layers.py b/modules/commons/common_layers.py new file mode 100644 index 0000000..4da6569 --- /dev/null +++ b/modules/commons/common_layers.py @@ -0,0 +1,427 @@ +from __future__ import annotations + +import math +import numpy as np +import torch +import torch.nn.functional as F +import torch.onnx.operators +from torch import nn +from torch.nn import LayerNorm, ReLU, GELU, SiLU + +import utils + + +class NormalInitEmbedding(torch.nn.Embedding): + def __init__( + self, + num_embeddings: int, + embedding_dim: int, + padding_idx: int | None = None, + *args, + **kwargs + ): + super().__init__(num_embeddings, embedding_dim, *args, padding_idx=padding_idx, **kwargs) + nn.init.normal_(self.weight, mean=0, std=self.embedding_dim ** -0.5) + if padding_idx is not None: + nn.init.constant_(self.weight[padding_idx], 0) + + +class AdamWLinear(torch.nn.Linear): + def __init__( + self, + in_features: int, + out_features: int, + *args, + bias: bool = True, + **kwargs + ): + super().__init__(in_features, out_features, *args, bias=bias, **kwargs) + nn.init.xavier_uniform_(self.weight) + if bias: + nn.init.constant_(self.bias, 0.) + + +class XavierUniformInitLinear(torch.nn.Linear): + def __init__( + self, + in_features: int, + out_features: int, + *args, + bias: bool = True, + **kwargs + ): + super().__init__(in_features, out_features, *args, bias=bias, **kwargs) + nn.init.xavier_uniform_(self.weight) + if bias: + nn.init.constant_(self.bias, 0.) + + +class SinusoidalPositionalEmbedding(nn.Module): + """This module produces sinusoidal positional embeddings of any length. + + Padding symbols are ignored. + """ + + def __init__(self, embedding_dim, padding_idx, init_size=1024): + super().__init__() + self.embedding_dim = embedding_dim + self.padding_idx = padding_idx + self.weights = SinusoidalPositionalEmbedding.get_embedding( + init_size, + embedding_dim, + padding_idx, + ) + self.register_buffer('_float_tensor', torch.FloatTensor(1)) + + @staticmethod + def get_embedding(num_embeddings, embedding_dim, padding_idx=None): + """Build sinusoidal embeddings. + + This matches the implementation in tensor2tensor, but differs slightly + from the description in Section 3.5 of "Attention Is All You Need". + """ + half_dim = embedding_dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb) + emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0) + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1) + if embedding_dim % 2 == 1: + # zero pad + emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1) + if padding_idx is not None: + emb[padding_idx, :] = 0 + return emb + + def forward(self, x, incremental_state=None, timestep=None, positions=None): + """Input is expected to be of size [bsz x seqlen].""" + bsz, seq_len = x.shape[:2] + max_pos = self.padding_idx + 1 + seq_len + if self.weights is None or max_pos > self.weights.size(0): + # recompute/expand embeddings if needed + self.weights = SinusoidalPositionalEmbedding.get_embedding( + max_pos, + self.embedding_dim, + self.padding_idx, + ) + self.weights = self.weights.to(self._float_tensor) + + if incremental_state is not None: + # positions is the same for every token when decoding a single step + pos = timestep.view(-1)[0] + 1 if timestep is not None else seq_len + return self.weights[self.padding_idx + pos, :].expand(bsz, 1, -1) + + positions = utils.make_positions(x, self.padding_idx) if positions is None else positions + return self.weights.index_select(0, positions.view(-1)).view(bsz, seq_len, -1).detach() + + @staticmethod + def max_positions(): + """Maximum number of supported positions.""" + return int(1e5) # an arbitrary large number + + +class SwiGLU(nn.Module): + # Swish-Applies the gated linear unit function. + def __init__(self, dim=-1): + super().__init__() + self.dim = dim + + def forward(self, x): + # out, gate = x.chunk(2, dim=self.dim) + # Using torch.split instead of chunk for ONNX export compatibility. + out, gate = torch.split(x, x.size(self.dim) // 2, dim=self.dim) + gate = F.silu(gate) + if x.dtype == torch.float16: + out_min, out_max = torch.aminmax(out.detach()) + gate_min, gate_max = torch.aminmax(gate.detach()) + max_abs_out = torch.max(-out_min, out_max).float() + max_abs_gate = torch.max(-gate_min, gate_max).float() + max_abs_value = max_abs_out * max_abs_gate + if max_abs_value > 1000: + ratio = (1000 / max_abs_value).half() + gate = gate * ratio + return (out * gate).clamp(-1000 * ratio, 1000 * ratio) / ratio + return out * gate + + +class ATanGLUFunction(torch.autograd.Function): + @staticmethod + def forward(ctx, out, gate): + atan_gate = torch.atan(gate) + decay_out = out / gate.square().add(1.0) + ctx.save_for_backward(decay_out, atan_gate) + return out * atan_gate + + @staticmethod + def backward(ctx, grad_output): + decay_out, atan_gate = ctx.saved_tensors + grad_out_part = grad_output * atan_gate + grad_gate_part = grad_output * decay_out + return grad_out_part, grad_gate_part + + +class ATanGLU(nn.Module): + # ArcTan-Applies the gated linear unit function. + def __init__(self, dim=-1): + super().__init__() + self.dim = dim + + def forward(self, x): + # out, gate = x.chunk(2, dim=self.dim) + # Using torch.split instead of chunk for ONNX export compatibility. + out, gate = torch.split(x, x.size(self.dim) // 2, dim=self.dim) + if self.training: + return ATanGLUFunction.apply(out, gate) + else: + return out * torch.atan(gate) + + +class AdamWConv1d(torch.nn.Conv1d): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + nn.init.kaiming_normal_(self.weight) + + +class KaimingNormalConv1d(torch.nn.Conv1d): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + nn.init.kaiming_normal_(self.weight) + + +class Transpose(nn.Module): + def __init__(self, dims): + super().__init__() + assert len(dims) == 2, 'dims must be a tuple of two dimensions' + self.dims = dims + + def forward(self, x): + return x.transpose(*self.dims) + + +class Mixed_LayerNorm(nn.Module): + def __init__( + self, + channels: int, + condition_channels: int, + beta_distribution_concentration: float = 0.2, + eps: float = 1e-5, + bias: bool = True + ): + super().__init__() + self.channels = channels + self.eps = eps + + self.beta_distribution = torch.distributions.Beta( + beta_distribution_concentration, + beta_distribution_concentration + ) + + self.affine = XavierUniformInitLinear(condition_channels, channels * 2, bias=bias) + if self.affine.bias is not None: + self.affine.bias.data[:channels] = 0 # betas (shift) + self.affine.bias.data[channels:] = 1 # gammas (scale) + + def forward( + self, + x: torch.FloatTensor, + condition: torch.FloatTensor # -> shape [Batch, Cond_d] + ) -> torch.FloatTensor: + x = F.layer_norm(x, normalized_shape=(self.channels,), weight=None, bias=None, eps=self.eps) + + affine_params = self.affine(condition) + if affine_params.ndim == 2: + affine_params = affine_params.unsqueeze(1) + betas, gammas = torch.split(affine_params, self.channels, dim=-1) + + if not self.training or x.size(0) == 1: + return gammas * x + betas + + shuffle_indices = torch.randperm(x.size(0), device=x.device) + shuffled_betas = betas[shuffle_indices] + shuffled_gammas = gammas[shuffle_indices] + + beta_samples = self.beta_distribution.sample((x.size(0), 1, 1)).to(x.device) + mixed_betas = beta_samples * betas + (1 - beta_samples) * shuffled_betas + mixed_gammas = beta_samples * gammas + (1 - beta_samples) * shuffled_gammas + + return mixed_gammas * x + mixed_betas + + +class TransformerFFNLayer(nn.Module): + def __init__(self, hidden_size, filter_size, kernel_size=1, dropout=0., act='gelu'): + super().__init__() + self.kernel_size = kernel_size + self.dropout = dropout + self.act = act + filter_size_1 = filter_size + if self.act == 'relu': + self.act_fn = ReLU() + elif self.act == 'gelu': + self.act_fn = GELU() + elif self.act == 'swish': + self.act_fn = SiLU() + elif self.act == 'swiglu': + self.act_fn = SwiGLU() + filter_size_1 = filter_size * 2 + elif self.act == 'atanglu': + self.act_fn = ATanGLU() + filter_size_1 = filter_size * 2 + else: + raise ValueError(f'{act} is not a valid activation') + self.ffn_1 = nn.Conv1d(hidden_size, filter_size_1, kernel_size, padding=kernel_size // 2) + self.ffn_2 = XavierUniformInitLinear(filter_size, hidden_size) + + def forward(self, x): + # x: B x T x C + x = self.ffn_1(x.transpose(1, 2)).transpose(1, 2) + x = x * self.kernel_size ** -0.5 + + x = self.act_fn(x) + x = F.dropout(x, self.dropout, training=self.training) + x = self.ffn_2(x) + return x + + +class MultiheadSelfAttentionWithRoPE(nn.Module): + def __init__(self, embed_dim, num_heads, dropout=0.1, bias=False, rotary_embed=None): + super().__init__() + assert embed_dim % num_heads == 0, "Embedding dimension must be divisible by number of heads" + + self.embed_dim = embed_dim + self.num_heads = num_heads + self.head_dim = embed_dim // num_heads + + # Linear layers for Q, K, V projections + self.in_proj = nn.Linear(embed_dim, embed_dim * 3, bias=bias) + + # Final linear layer after concatenation + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + + # Dropout layer + self.dropout = nn.Dropout(dropout) + + # Rotary Embeddings + self.rotary_embed = rotary_embed + + # Initialization parameters + nn.init.xavier_uniform_(self.in_proj.weight) + nn.init.xavier_uniform_(self.out_proj.weight) + if bias: + nn.init.constant_(self.in_proj.bias, 0.0) + nn.init.constant_(self.out_proj.bias, 0.0) + + def forward(self, x, key_padding_mask=None): + # x: (B, L, C) + # key_padding_mask: (B, L) + batch_size, seq_len, embed_dim = x.size() + + # Project inputs to Q, K, V + Q, K, V = torch.split(self.in_proj(x), self.embed_dim, dim=-1) + + # Reshape Q, K, V for multi-head attention + Q = Q.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2) # (B, H, L, D) + K = K.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2) # (B, H, L, D) + V = V.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2) # (B, H, L, D) + + # Apply RoPE + if self.rotary_embed is not None: + Q = self.rotary_embed.rotate_queries_or_keys(Q) + K = self.rotary_embed.rotate_queries_or_keys(K) + + # Compute attention scores + scores = torch.matmul(Q, K.transpose(-2, -1)) / math.sqrt(self.head_dim) # (B, H, L, L) + + # Apply key padding mask if provided + if key_padding_mask is not None: + # Expand mask to match attention scores shape + mask = key_padding_mask.unsqueeze(1).unsqueeze(1) # (B, 1, 1, L) + scores = scores.masked_fill(mask == 1, -np.inf) # Masked positions are set to -inf + + # Compute attention weights + attn_weights = F.softmax(scores, dim=-1) # (B, H, L, L) + attn_weights = self.dropout(attn_weights) + + # Apply attention weights to V + attn_output = torch.matmul(attn_weights, V) # (B, H, L, D) + + # Reshape and concatenate heads + attn_output = attn_output.transpose(1, 2).contiguous().view(batch_size, seq_len, embed_dim) # (B, L, C) + + # Final linear projection + output = self.out_proj(attn_output) # (B, L, C) + + return output + + +class EncSALayer(nn.Module): + def __init__(self, c, num_heads, dropout, attention_dropout=0.1, + relu_dropout=0.1, kernel_size=9, act='gelu', rotary_embed=None, + layer_idx=None, mix_ln_layer=None + ): + super().__init__() + self.dropout = dropout + self.use_mix_ln = ( + layer_idx is not None + and mix_ln_layer is not None + and layer_idx in mix_ln_layer + ) + if self.use_mix_ln: + self.layer_norm1 = Mixed_LayerNorm(c, c) + else: + self.layer_norm1 = LayerNorm(c) + # Always use the in-house manual attention. With rotary_embed=None this + # is a plain multi-head self-attention that is ONNX-export safe across + # dynamic sequence lengths. Using torch.nn.MultiheadAttention here was + # the source of the "Reshape baked tgt_len" bug on PyTorch >= 2.0 + # because its SDPA-branched implementation specializes tgt_len to a + # Python int and re-injects it into the output Reshape. + self.self_attn = MultiheadSelfAttentionWithRoPE( + c, num_heads, dropout=attention_dropout, bias=False, rotary_embed=rotary_embed + ) + if self.use_mix_ln: + self.layer_norm2 = Mixed_LayerNorm(c, c) + else: + self.layer_norm2 = LayerNorm(c) + self.ffn = TransformerFFNLayer( + c, 4 * c, kernel_size=kernel_size, dropout=relu_dropout, act=act + ) + + def forward(self, x, encoder_padding_mask=None, cond=None, **kwargs): + layer_norm_training = kwargs.get('layer_norm_training', None) + if layer_norm_training is not None: + self.layer_norm1.training = layer_norm_training + self.layer_norm2.training = layer_norm_training + residual = x + if self.use_mix_ln: + x = self.layer_norm1(x, cond) + else: + x = self.layer_norm1(x) + x = self.self_attn(x, key_padding_mask=encoder_padding_mask) + x = F.dropout(x, self.dropout, training=self.training) + x = residual + x + x = x * (1 - encoder_padding_mask.float())[..., None] + + residual = x + if self.use_mix_ln: + x = self.layer_norm2(x, cond) + else: + x = self.layer_norm2(x) + x = self.ffn(x) + x = F.dropout(x, self.dropout, training=self.training) + x = residual + x + x = x * (1 - encoder_padding_mask.float())[..., None] + return x + + +class SinusoidalPosEmb(nn.Module): + def __init__(self, dim): + super().__init__() + self.dim = dim + + def forward(self, x): + device = x.device + half_dim = self.dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, device=device) * -emb) + emb = x.unsqueeze(-1) * emb.unsqueeze(0) + emb = torch.cat((emb.sin(), emb.cos()), dim=-1) + return emb diff --git a/modules/commons/espnet_positional_embedding.py b/modules/commons/espnet_positional_embedding.py new file mode 100644 index 0000000..26ac372 --- /dev/null +++ b/modules/commons/espnet_positional_embedding.py @@ -0,0 +1,113 @@ +import math +import torch + + +class PositionalEncoding(torch.nn.Module): + """Positional encoding. + Args: + d_model (int): Embedding dimension. + dropout_rate (float): Dropout rate. + max_len (int): Maximum input length. + reverse (bool): Whether to reverse the input position. + """ + + def __init__(self, d_model, dropout_rate, max_len=5000, reverse=False): + """Construct an PositionalEncoding object.""" + super(PositionalEncoding, self).__init__() + self.d_model = d_model + self.reverse = reverse + self.xscale = math.sqrt(self.d_model) + self.dropout = torch.nn.Dropout(p=dropout_rate) + self.pe = None + self.extend_pe(torch.tensor(0.0).expand(1, max_len)) + + def extend_pe(self, x): + """Reset the positional encodings.""" + if self.pe is not None: + if self.pe.size(1) >= x.size(1): + if self.pe.dtype != x.dtype or self.pe.device != x.device: + self.pe = self.pe.to(dtype=x.dtype, device=x.device) + return + if self.reverse: + position = torch.arange( + x.size(1) - 1, -1, -1.0, dtype=torch.float32 + ).unsqueeze(1) + else: + position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1) + div_term = torch.exp( + torch.arange(0, self.d_model, 2, dtype=torch.float32) + * -(math.log(10000.0) / self.d_model) + ) + pe = torch.stack([ + torch.sin(position * div_term), + torch.cos(position * div_term) + ], dim=2).view(-1, self.d_model).unsqueeze(0) + self.pe = pe.to(device=x.device, dtype=x.dtype) + + def forward(self, x: torch.Tensor): + """Add positional encoding. + Args: + x (torch.Tensor): Input tensor (batch, time, `*`). + Returns: + torch.Tensor: Encoded tensor (batch, time, `*`). + """ + self.extend_pe(x) + x = x * self.xscale + self.pe[:, : x.size(1)] + return self.dropout(x) + + +class ScaledPositionalEncoding(PositionalEncoding): + """Scaled positional encoding module. + See Sec. 3.2 https://arxiv.org/abs/1809.08895 + Args: + d_model (int): Embedding dimension. + dropout_rate (float): Dropout rate. + max_len (int): Maximum input length. + """ + + def __init__(self, d_model, dropout_rate, max_len=5000): + """Initialize class.""" + super().__init__(d_model=d_model, dropout_rate=dropout_rate, max_len=max_len) + self.alpha = torch.nn.Parameter(torch.tensor(1.0)) + + def reset_parameters(self): + """Reset parameters.""" + self.alpha.data = torch.tensor(1.0) + + def forward(self, x): + """Add positional encoding. + Args: + x (torch.Tensor): Input tensor (batch, time, `*`). + Returns: + torch.Tensor: Encoded tensor (batch, time, `*`). + """ + self.extend_pe(x) + x = x + self.alpha * self.pe[:, : x.size(1)] + return self.dropout(x) + + +class RelPositionalEncoding(PositionalEncoding): + """Relative positional encoding module. + See : Appendix B in https://arxiv.org/abs/1901.02860 + Args: + d_model (int): Embedding dimension. + dropout_rate (float): Dropout rate. + max_len (int): Maximum input length. + """ + + def __init__(self, d_model, dropout_rate, max_len=5000): + """Initialize class.""" + super().__init__(d_model, dropout_rate, max_len, reverse=True) + + def forward(self, x): + """Compute positional encoding. + Args: + x (torch.Tensor): Input tensor (batch, time, `*`). + Returns: + torch.Tensor: Encoded tensor (batch, time, `*`). + torch.Tensor: Positional embedding tensor (1, time, `*`). + """ + self.extend_pe(x) + x = x * self.xscale + pos_emb = self.pe[:, : x.size(1)] + return self.dropout(x) + self.dropout(pos_emb) diff --git a/modules/commons/rotary_embedding_torch.py b/modules/commons/rotary_embedding_torch.py new file mode 100644 index 0000000..1a1fa19 --- /dev/null +++ b/modules/commons/rotary_embedding_torch.py @@ -0,0 +1,63 @@ +import torch +from einops import rearrange, repeat +from torch import einsum, Tensor +from torch.nn import Module + + +def rotate_half(x: Tensor, interleaved=True) -> Tensor: + if not interleaved: + # x_half1, x_half2 = x.chunk(2, dim=-1) + # Using torch.split instead of chunk for ONNX export compatibility. + x1, x2 = torch.split(x, x.size(-1) // 2, dim=-1) + return torch.cat((-x2, x1), dim=-1) + else: + x = rearrange(x, '... (d r) -> ... d r', r=2) + x1, x2 = x.unbind(dim=-1) + x = torch.stack((-x2, x1), dim=-1) + return rearrange(x, '... d r -> ... (d r)') + + +def apply_rotary_emb(freqs: Tensor, t: Tensor, interleaved=True) -> Tensor: + rot_dim = freqs.shape[-1] + t_to_rotate = t[..., :rot_dim] + t_pass_through = t[..., rot_dim:] + + t_rotated = (t_to_rotate * freqs.cos()) + (rotate_half(t_to_rotate, interleaved) * freqs.sin()) + + return torch.cat((t_rotated, t_pass_through), dim=-1) + + +class RotaryEmbedding(Module): + def __init__( + self, + dim, + theta=10000, + max_seq_len=8192, + interleaved: bool = True + ): + super().__init__() + self.interleaved = interleaved + self.cached_freqs_seq_len = max_seq_len + inv_freq = 1. / (theta ** (torch.arange(0, dim, 2).float() / dim)) + self.register_buffer('inv_freq', inv_freq, persistent=False) + self.register_buffer('cached_freqs', self._precompute_cache(max_seq_len), persistent=False) + + def _precompute_cache(self, seq_len: int): + seq = torch.arange(seq_len, device=self.inv_freq.device, dtype=self.inv_freq.dtype) + freqs = einsum('i, j -> i j', seq, self.inv_freq) + if self.interleaved: + freqs = repeat(freqs, '... n -> ... (n r)', r=2) + else: + freqs = torch.cat((freqs, freqs), dim=-1) + return freqs + + def forward(self, seq_len: int) -> Tensor: + if seq_len > self.cached_freqs_seq_len: + raise RuntimeError("sequence exceeds RoPE max_seq_len!") + return self.cached_freqs[0: seq_len].detach() + + def rotate_queries_or_keys(self, t: Tensor) -> Tensor: + device, dtype, seq_len = t.device, t.dtype, t.shape[-2] + freqs = self.forward(seq_len=seq_len) + + return apply_rotary_emb(freqs.to(device=device, dtype=dtype), t, self.interleaved) diff --git a/modules/compat.py b/modules/compat.py new file mode 100644 index 0000000..8311b16 --- /dev/null +++ b/modules/compat.py @@ -0,0 +1,24 @@ +def get_backbone_type(root_config: dict, nested_config: dict = None): + if nested_config is None: + nested_config = root_config + return nested_config.get( + 'backbone_type', + root_config.get( + 'backbone_type', + root_config.get('diff_decoder_type', 'wavenet') + ) + ) + + +def get_backbone_args(config: dict, backbone_type: str): + args = config.get('backbone_args') + if args is not None: + return args + elif backbone_type == 'wavenet': + return { + 'num_layers': config.get('residual_layers'), + 'num_channels': config.get('residual_channels'), + 'dilation_cycle_length': config.get('dilation_cycle_length'), + } + else: + return None diff --git a/modules/core/__init__.py b/modules/core/__init__.py new file mode 100644 index 0000000..ab38ee1 --- /dev/null +++ b/modules/core/__init__.py @@ -0,0 +1,2 @@ +from .ddpm import GaussianDiffusion, PitchDiffusion, MultiVarianceDiffusion +from .reflow import RectifiedFlow, PitchRectifiedFlow, MultiVarianceRectifiedFlow diff --git a/modules/core/ddpm.py b/modules/core/ddpm.py new file mode 100644 index 0000000..85b1284 --- /dev/null +++ b/modules/core/ddpm.py @@ -0,0 +1,509 @@ +from __future__ import annotations + +from collections import deque +from functools import partial +from typing import List, Tuple + +import numpy as np +import torch +from torch import nn +from tqdm import tqdm + +from modules.backbones import build_backbone +from utils.hparams import hparams + + +def extract(a, t, x_shape): + b, *_ = t.shape + out = a.gather(-1, t) + return out.reshape(b, *((1,) * (len(x_shape) - 1))) + + +def noise_like(shape, device, repeat=False): + repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) + noise = lambda: torch.randn(shape, device=device) + return repeat_noise() if repeat else noise() + + +def linear_beta_schedule(timesteps, max_beta=0.01): + """ + linear schedule + """ + betas = np.linspace(1e-4, max_beta, timesteps) + return betas + + +def cosine_beta_schedule(timesteps, s=0.008): + """ + cosine schedule + as proposed in https://openreview.net/forum?id=-NEXDKk8gZ + """ + steps = timesteps + 1 + x = np.linspace(0, steps, steps) + alphas_cumprod = np.cos(((x / steps) + s) / (1 + s) * np.pi * 0.5) ** 2 + alphas_cumprod = alphas_cumprod / alphas_cumprod[0] + betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1]) + return np.clip(betas, a_min=0, a_max=0.999) + + +beta_schedule = { + "cosine": cosine_beta_schedule, + "linear": linear_beta_schedule, +} + + +class GaussianDiffusion(nn.Module): + def __init__(self, out_dims, num_feats=1, timesteps=1000, k_step=1000, + backbone_type=None, backbone_args=None, betas=None, + spec_min=None, spec_max=None): + super().__init__() + self.denoise_fn: nn.Module = build_backbone(out_dims, num_feats, backbone_type, backbone_args) + self.out_dims = out_dims + self.num_feats = num_feats + + if betas is not None: + betas = betas.detach().cpu().numpy() if isinstance(betas, torch.Tensor) else betas + else: + schedule_args = {} + if hparams['schedule_type'] == 'linear': + schedule_args['max_beta'] = hparams.get('max_beta', 0.01) + betas = beta_schedule[hparams['schedule_type']](timesteps, **schedule_args) + + alphas = 1. - betas + alphas_cumprod = np.cumprod(alphas, axis=0) + alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) + + self.use_shallow_diffusion = hparams.get('use_shallow_diffusion', False) + if self.use_shallow_diffusion: + assert k_step <= timesteps, 'K_step should not be larger than timesteps.' + self.timesteps = timesteps + self.k_step = k_step if self.use_shallow_diffusion else timesteps + self.noise_list = deque(maxlen=4) + + to_torch = partial(torch.tensor, dtype=torch.float32) + + self.register_buffer('betas', to_torch(betas)) + self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) + self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) + self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) + self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) + self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) + self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) + + # calculations for posterior q(x_{t-1} | x_t, x_0) + posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod) + # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t) + self.register_buffer('posterior_variance', to_torch(posterior_variance)) + # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain + self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20)))) + self.register_buffer('posterior_mean_coef1', to_torch( + betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))) + self.register_buffer('posterior_mean_coef2', to_torch( + (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod))) + + # spec: [B, T, M] or [B, F, T, M] + # spec_min and spec_max: [1, 1, M] or [1, 1, F, M] => transpose(-3, -2) => [1, 1, M] or [1, F, 1, M] + spec_min = torch.FloatTensor(spec_min)[None, None, :out_dims].transpose(-3, -2) + spec_max = torch.FloatTensor(spec_max)[None, None, :out_dims].transpose(-3, -2) + self.register_buffer('spec_min', spec_min) + self.register_buffer('spec_max', spec_max) + + # for compatibility with ONNX continuous acceleration + self.time_scale_factor = self.timesteps + self.t_start = 1 - self.k_step / self.timesteps + factors = torch.LongTensor([i for i in range(1, self.timesteps + 1) if self.timesteps % i == 0]) + self.register_buffer('timestep_factors', factors, persistent=False) + + def q_mean_variance(self, x_start, t): + mean = extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + variance = extract(1. - self.alphas_cumprod, t, x_start.shape) + log_variance = extract(self.log_one_minus_alphas_cumprod, t, x_start.shape) + return mean, variance, log_variance + + def predict_start_from_noise(self, x_t, t, noise): + return ( + extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - + extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise + ) + + def q_posterior(self, x_start, x_t, t): + posterior_mean = ( + extract(self.posterior_mean_coef1, t, x_t.shape) * x_start + + extract(self.posterior_mean_coef2, t, x_t.shape) * x_t + ) + posterior_variance = extract(self.posterior_variance, t, x_t.shape) + posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, x_t.shape) + return posterior_mean, posterior_variance, posterior_log_variance_clipped + + def p_mean_variance(self, x, t, cond): + noise_pred = self.denoise_fn(x, t, cond=cond) + x_recon = self.predict_start_from_noise(x, t=t, noise=noise_pred) + + # This is previously inherited from original DiffSinger repository + # and disabled due to some loudness issues when speedup = 1. + # x_recon.clamp_(-1., 1.) + + model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) + return model_mean, posterior_variance, posterior_log_variance + + @torch.no_grad() + def p_sample(self, x, t, cond, clip_denoised=True, repeat_noise=False): + b, *_, device = *x.shape, x.device + model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, cond=cond) + noise = noise_like(x.shape, device, repeat_noise) + # no noise when t == 0 + nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise + + @torch.no_grad() + def p_sample_ddim(self, x, t, interval, cond): + a_t = extract(self.alphas_cumprod, t, x.shape) + a_prev = extract(self.alphas_cumprod, torch.max(t - interval, torch.zeros_like(t)), x.shape) + + noise_pred = self.denoise_fn(x, t, cond=cond) + x_prev = a_prev.sqrt() * ( + x / a_t.sqrt() + (((1 - a_prev) / a_prev).sqrt() - ((1 - a_t) / a_t).sqrt()) * noise_pred + ) + return x_prev + + @torch.no_grad() + def p_sample_plms(self, x, t, interval, cond, clip_denoised=True, repeat_noise=False): + """ + Use the PLMS method from + [Pseudo Numerical Methods for Diffusion Models on Manifolds](https://arxiv.org/abs/2202.09778). + """ + + def get_x_pred(x, noise_t, t): + a_t = extract(self.alphas_cumprod, t, x.shape) + a_prev = extract(self.alphas_cumprod, torch.max(t - interval, torch.zeros_like(t)), x.shape) + a_t_sq, a_prev_sq = a_t.sqrt(), a_prev.sqrt() + + x_delta = (a_prev - a_t) * ((1 / (a_t_sq * (a_t_sq + a_prev_sq))) * x - 1 / ( + a_t_sq * (((1 - a_prev) * a_t).sqrt() + ((1 - a_t) * a_prev).sqrt())) * noise_t) + x_pred = x + x_delta + + return x_pred + + noise_list = self.noise_list + noise_pred = self.denoise_fn(x, t, cond=cond) + + if len(noise_list) == 0: + x_pred = get_x_pred(x, noise_pred, t) + noise_pred_prev = self.denoise_fn(x_pred, max(t - interval, 0), cond=cond) + noise_pred_prime = (noise_pred + noise_pred_prev) / 2 + elif len(noise_list) == 1: + noise_pred_prime = (3 * noise_pred - noise_list[-1]) / 2 + elif len(noise_list) == 2: + noise_pred_prime = (23 * noise_pred - 16 * noise_list[-1] + 5 * noise_list[-2]) / 12 + else: + noise_pred_prime = (55 * noise_pred - 59 * noise_list[-1] + 37 * noise_list[-2] - 9 * noise_list[-3]) / 24 + + x_prev = get_x_pred(x, noise_pred_prime, t) + noise_list.append(noise_pred) + + return x_prev + + def q_sample(self, x_start, t, noise): + return ( + extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + + extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise + ) + + def p_losses(self, x_start, t, cond, noise=None): + if noise is None: + noise = torch.randn_like(x_start) + + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + x_recon = self.denoise_fn(x_noisy, t, cond) + + return x_recon, noise + + def inference(self, cond, b=1, x_start=None, device=None): + depth = hparams.get('K_step_infer', self.k_step) + speedup = hparams['diff_speedup'] + if speedup > 0: + assert depth % speedup == 0, f'Acceleration ratio must be a factor of diffusion depth {depth}.' + + noise = torch.randn(b, self.num_feats, self.out_dims, cond.shape[2], device=device) + if self.use_shallow_diffusion: + t_max = min(depth, self.k_step) + else: + t_max = self.k_step + + if t_max >= self.timesteps: + x = noise + elif t_max > 0: + assert x_start is not None, 'Missing shallow diffusion source.' + x = self.q_sample( + x_start, torch.full((b,), t_max - 1, device=device, dtype=torch.long), noise + ) + else: + assert x_start is not None, 'Missing shallow diffusion source.' + x = x_start + + if speedup > 1 and t_max > 0: + algorithm = hparams['diff_accelerator'] + if algorithm == 'dpm-solver': + from inference.dpm_solver_pytorch import NoiseScheduleVP, model_wrapper, DPM_Solver + # 1. Define the noise schedule. + noise_schedule = NoiseScheduleVP(schedule='discrete', betas=self.betas[:t_max]) + + # 2. Convert your discrete-time `model` to the continuous-time + # noise prediction model. Here is an example for a diffusion model + # `model` with the noise prediction type ("noise") . + def my_wrapper(fn): + def wrapped(x, t, **kwargs): + ret = fn(x, t, **kwargs) + self.bar.update(1) + return ret + + return wrapped + + model_fn = model_wrapper( + my_wrapper(self.denoise_fn), + noise_schedule, + model_type="noise", # or "x_start" or "v" or "score" + model_kwargs={"cond": cond} + ) + + # 3. Define dpm-solver and sample by singlestep DPM-Solver. + # (We recommend singlestep DPM-Solver for unconditional sampling) + # You can adjust the `steps` to balance the computation + # costs and the sample quality. + dpm_solver = DPM_Solver(model_fn, noise_schedule, algorithm_type="dpmsolver++") + + steps = t_max // hparams["diff_speedup"] + self.bar = tqdm(desc="sample time step", total=steps, disable=not hparams['infer'], leave=False) + x = dpm_solver.sample( + x, + steps=steps, + order=2, + skip_type="time_uniform", + method="multistep", + ) + self.bar.close() + elif algorithm == 'unipc': + from inference.uni_pc import NoiseScheduleVP, model_wrapper, UniPC + # 1. Define the noise schedule. + noise_schedule = NoiseScheduleVP(schedule='discrete', betas=self.betas[:t_max]) + + # 2. Convert your discrete-time `model` to the continuous-time + # noise prediction model. Here is an example for a diffusion model + # `model` with the noise prediction type ("noise") . + def my_wrapper(fn): + def wrapped(x, t, **kwargs): + ret = fn(x, t, **kwargs) + self.bar.update(1) + return ret + + return wrapped + + model_fn = model_wrapper( + my_wrapper(self.denoise_fn), + noise_schedule, + model_type="noise", # or "x_start" or "v" or "score" + model_kwargs={"cond": cond} + ) + + # 3. Define uni_pc and sample by multistep UniPC. + # You can adjust the `steps` to balance the computation + # costs and the sample quality. + uni_pc = UniPC(model_fn, noise_schedule, variant='bh2') + + steps = t_max // hparams["diff_speedup"] + self.bar = tqdm(desc="sample time step", total=steps, disable=not hparams['infer'], leave=False) + x = uni_pc.sample( + x, + steps=steps, + order=2, + skip_type="time_uniform", + method="multistep", + ) + self.bar.close() + elif algorithm == 'pndm': + self.noise_list = deque(maxlen=4) + iteration_interval = speedup + for i in tqdm( + reversed(range(0, t_max, iteration_interval)), desc='sample time step', + total=t_max // iteration_interval, disable=not hparams['infer'], leave=False + ): + x = self.p_sample_plms( + x, torch.full((b,), i, device=device, dtype=torch.long), + iteration_interval, cond=cond + ) + elif algorithm == 'ddim': + iteration_interval = speedup + for i in tqdm( + reversed(range(0, t_max, iteration_interval)), desc='sample time step', + total=t_max // iteration_interval, disable=not hparams['infer'], leave=False + ): + x = self.p_sample_ddim( + x, torch.full((b,), i, device=device, dtype=torch.long), + iteration_interval, cond=cond + ) + else: + raise ValueError(f"Unsupported acceleration algorithm for DDPM: {algorithm}.") + else: + for i in tqdm(reversed(range(0, t_max)), desc='sample time step', total=t_max, + disable=not hparams['infer'], leave=False): + x = self.p_sample(x, torch.full((b,), i, device=device, dtype=torch.long), cond) + x = x.transpose(2, 3).squeeze(1) # [B, F, M, T] => [B, T, M] or [B, F, T, M] + return x + + def forward(self, condition, gt_spec=None, src_spec=None, infer=True): + """ + conditioning diffusion, use fastspeech2 encoder output as the condition + """ + cond = condition.transpose(1, 2) + b, device = condition.shape[0], condition.device + + if not infer: + # gt_spec: [B, T, M] or [B, F, T, M] + spec = self.norm_spec(gt_spec).transpose(-2, -1) # [B, M, T] or [B, F, M, T] + if self.num_feats == 1: + spec = spec[:, None, :, :] # [B, F=1, M, T] + t = torch.randint(0, self.k_step, (b,), device=device).long() + x_recon, noise = self.p_losses(spec, t, cond=cond) + return x_recon, noise + else: + # src_spec: [B, T, M] or [B, F, T, M] + if src_spec is not None: + spec = self.norm_spec(src_spec).transpose(-2, -1) + if self.num_feats == 1: + spec = spec[:, None, :, :] + else: + spec = None + x = self.inference(cond, b=b, x_start=spec, device=device) + return self.denorm_spec(x) + + def norm_spec(self, x): + return (x - self.spec_min) / (self.spec_max - self.spec_min) * 2 - 1 + + def denorm_spec(self, x): + return (x + 1) / 2 * (self.spec_max - self.spec_min) + self.spec_min + + +class RepetitiveDiffusion(GaussianDiffusion): + def __init__(self, vmin: float | int | list, vmax: float | int | list, + repeat_bins: int, timesteps=1000, k_step=1000, + backbone_type=None, backbone_args=None, + betas=None): + assert (isinstance(vmin, (float, int)) and isinstance(vmax, (float, int))) or len(vmin) == len(vmax) + num_feats = 1 if isinstance(vmin, (float, int)) else len(vmin) + spec_min = [vmin] if num_feats == 1 else [[v] for v in vmin] + spec_max = [vmax] if num_feats == 1 else [[v] for v in vmax] + self.repeat_bins = repeat_bins + super().__init__( + out_dims=repeat_bins, num_feats=num_feats, + timesteps=timesteps, k_step=k_step, + backbone_type=backbone_type, backbone_args=backbone_args, + betas=betas, spec_min=spec_min, spec_max=spec_max + ) + + def norm_spec(self, x): + """ + + :param x: [B, T] or [B, F, T] + :return [B, T, R] or [B, F, T, R] + """ + if self.num_feats == 1: + repeats = [1, 1, self.repeat_bins] + else: + repeats = [1, 1, 1, self.repeat_bins] + return super().norm_spec(x.unsqueeze(-1).repeat(repeats)) + + def denorm_spec(self, x): + """ + + :param x: [B, T, R] or [B, F, T, R] + :return [B, T] or [B, F, T] + """ + return super().denorm_spec(x).mean(dim=-1) + + +class PitchDiffusion(RepetitiveDiffusion): + def __init__(self, vmin: float, vmax: float, + cmin: float, cmax: float, repeat_bins, + timesteps=1000, k_step=1000, + backbone_type=None, backbone_args=None, + betas=None): + self.vmin = vmin # norm min + self.vmax = vmax # norm max + self.cmin = cmin # clip min + self.cmax = cmax # clip max + super().__init__( + vmin=vmin, vmax=vmax, repeat_bins=repeat_bins, + timesteps=timesteps, k_step=k_step, + backbone_type=backbone_type, backbone_args=backbone_args, + betas=betas + ) + + def norm_spec(self, x): + return super().norm_spec(x.clamp(min=self.cmin, max=self.cmax)) + + def denorm_spec(self, x): + return super().denorm_spec(x).clamp(min=self.cmin, max=self.cmax) + + +class MultiVarianceDiffusion(RepetitiveDiffusion): + def __init__( + self, ranges: List[Tuple[float, float]], + clamps: List[Tuple[float | None, float | None] | None], + repeat_bins, timesteps=1000, k_step=1000, + backbone_type=None, backbone_args=None, + betas=None + ): + assert len(ranges) == len(clamps) + self.clamps = clamps + vmin = [r[0] for r in ranges] + vmax = [r[1] for r in ranges] + if len(vmin) == 1: + vmin = vmin[0] + if len(vmax) == 1: + vmax = vmax[0] + super().__init__( + vmin=vmin, vmax=vmax, repeat_bins=repeat_bins, + timesteps=timesteps, k_step=k_step, + backbone_type=backbone_type, backbone_args=backbone_args, + betas=betas + ) + + def clamp_spec(self, xs: list | tuple): + clamped = [] + for x, c in zip(xs, self.clamps): + if c is None: + clamped.append(x) + continue + clamped.append(x.clamp(min=c[0], max=c[1])) + return clamped + + def norm_spec(self, xs: list | tuple): + """ + + :param xs: sequence of [B, T] + :return: [B, F, T] => super().norm_spec(xs) => [B, F, T, R] + """ + assert len(xs) == self.num_feats + clamped = self.clamp_spec(xs) + xs = torch.stack(clamped, dim=1) # [B, F, T] + if self.num_feats == 1: + xs = xs.squeeze(1) # [B, T] + return super().norm_spec(xs) + + def denorm_spec(self, xs): + """ + + :param xs: [B, T, R] or [B, F, T, R] => super().denorm_spec(xs) => [B, T] or [B, F, T] + :return: sequence of [B, T] + """ + xs = super().denorm_spec(xs) + if self.num_feats == 1: + xs = [xs] + else: + xs = xs.unbind(dim=1) + assert len(xs) == self.num_feats + return self.clamp_spec(xs) + diff --git a/modules/core/reflow.py b/modules/core/reflow.py new file mode 100644 index 0000000..c2f9f1e --- /dev/null +++ b/modules/core/reflow.py @@ -0,0 +1,262 @@ +from __future__ import annotations + +from typing import List, Tuple + +import torch +import torch.nn as nn +from tqdm import tqdm + +from modules.backbones import build_backbone +from utils.hparams import hparams + + +class RectifiedFlow(nn.Module): + def __init__(self, out_dims, num_feats=1, t_start=0., time_scale_factor=1000, + backbone_type=None, backbone_args=None, + spec_min=None, spec_max=None): + super().__init__() + self.velocity_fn: nn.Module = build_backbone(out_dims, num_feats, backbone_type, backbone_args) + self.out_dims = out_dims + self.num_feats = num_feats + self.use_shallow_diffusion = hparams.get('use_shallow_diffusion', False) + if self.use_shallow_diffusion: + assert 0. <= t_start <= 1., 'T_start should be in [0, 1].' + else: + t_start = 0. + self.t_start = t_start + self.time_scale_factor = time_scale_factor + + # spec: [B, T, M] or [B, F, T, M] + # spec_min and spec_max: [1, 1, M] or [1, 1, F, M] => transpose(-3, -2) => [1, 1, M] or [1, F, 1, M] + spec_min = torch.FloatTensor(spec_min)[None, None, :out_dims].transpose(-3, -2) + spec_max = torch.FloatTensor(spec_max)[None, None, :out_dims].transpose(-3, -2) + self.register_buffer('spec_min', spec_min, persistent=False) + self.register_buffer('spec_max', spec_max, persistent=False) + + def p_losses(self, x_end, t, cond): + x_start = torch.randn_like(x_end) + x_t = x_start + t[:, None, None, None] * (x_end - x_start) + v_pred = self.velocity_fn(x_t, t * self.time_scale_factor, cond) + + return v_pred, x_end - x_start + + def forward(self, condition, gt_spec=None, src_spec=None, infer=True): + cond = condition.transpose(1, 2) + b, device = condition.shape[0], condition.device + + if not infer: + # gt_spec: [B, T, M] or [B, F, T, M] + spec = self.norm_spec(gt_spec).transpose(-2, -1) # [B, M, T] or [B, F, M, T] + if self.num_feats == 1: + spec = spec[:, None, :, :] # [B, F=1, M, T] + t = self.t_start + (1.0 - self.t_start) * torch.rand((b,), device=device) + v_pred, v_gt = self.p_losses(spec, t, cond=cond) + return v_pred, v_gt, t + else: + # src_spec: [B, T, M] or [B, F, T, M] + if src_spec is not None: + spec = self.norm_spec(src_spec).transpose(-2, -1) + if self.num_feats == 1: + spec = spec[:, None, :, :] + else: + spec = None + x = self.inference(cond, b=b, x_end=spec, device=device) + return self.denorm_spec(x) + + @torch.no_grad() + def sample_euler(self, x, t, dt, cond): + x += self.velocity_fn(x, self.time_scale_factor * t, cond) * dt + t += dt + return x, t + + @torch.no_grad() + def sample_rk2(self, x, t, dt, cond): + k_1 = self.velocity_fn(x, self.time_scale_factor * t, cond) + k_2 = self.velocity_fn(x + 0.5 * k_1 * dt, self.time_scale_factor * (t + 0.5 * dt), cond) + x += k_2 * dt + t += dt + return x, t + + @torch.no_grad() + def sample_rk4(self, x, t, dt, cond): + k_1 = self.velocity_fn(x, self.time_scale_factor * t, cond) + k_2 = self.velocity_fn(x + 0.5 * k_1 * dt, self.time_scale_factor * (t + 0.5 * dt), cond) + k_3 = self.velocity_fn(x + 0.5 * k_2 * dt, self.time_scale_factor * (t + 0.5 * dt), cond) + k_4 = self.velocity_fn(x + k_3 * dt, self.time_scale_factor * (t + dt), cond) + x += (k_1 + 2 * k_2 + 2 * k_3 + k_4) * dt / 6 + t += dt + return x, t + + @torch.no_grad() + def sample_rk5(self, x, t, dt, cond): + k_1 = self.velocity_fn(x, self.time_scale_factor * t, cond) + k_2 = self.velocity_fn(x + 0.25 * k_1 * dt, self.time_scale_factor * (t + 0.25 * dt), cond) + k_3 = self.velocity_fn(x + 0.125 * (k_2 + k_1) * dt, self.time_scale_factor * (t + 0.25 * dt), cond) + k_4 = self.velocity_fn(x + 0.5 * (-k_2 + 2 * k_3) * dt, self.time_scale_factor * (t + 0.5 * dt), cond) + k_5 = self.velocity_fn(x + 0.0625 * (3 * k_1 + 9 * k_4) * dt, self.time_scale_factor * (t + 0.75 * dt), cond) + k_6 = self.velocity_fn(x + (-3 * k_1 + 2 * k_2 + 12 * k_3 - 12 * k_4 + 8 * k_5) * dt / 7, + self.time_scale_factor * (t + dt), + cond) + x += (7 * k_1 + 32 * k_3 + 12 * k_4 + 32 * k_5 + 7 * k_6) * dt / 90 + t += dt + return x, t + + @torch.no_grad() + def inference(self, cond, b=1, x_end=None, device=None): + noise = torch.randn(b, self.num_feats, self.out_dims, cond.shape[2], device=device) + t_start = hparams.get('T_start_infer', self.t_start) + if self.use_shallow_diffusion and t_start > 0: + assert x_end is not None, 'Missing shallow diffusion source.' + if t_start >= 1.: + t_start = 1. + x = x_end + else: + x = t_start * x_end + (1 - t_start) * noise + else: + t_start = 0. + x = noise + + algorithm = hparams['sampling_algorithm'] + infer_step = hparams['sampling_steps'] + + if t_start < 1: + dt = (1.0 - t_start) / max(1, infer_step) + algorithm_fn = { + 'euler': self.sample_euler, + 'rk2': self.sample_rk2, + 'rk4': self.sample_rk4, + 'rk5': self.sample_rk5, + }.get(algorithm) + if algorithm_fn is None: + raise ValueError(f'Unsupported algorithm for Rectified Flow: {algorithm}.') + dts = torch.tensor([dt]).to(x) + for i in tqdm(range(infer_step), desc='sample time step', total=infer_step, + disable=not hparams['infer'], leave=False): + x, _ = algorithm_fn(x, t_start + i * dts, dt, cond) + x = x.float() + x = x.transpose(2, 3).squeeze(1) # [B, F, M, T] => [B, T, M] or [B, F, T, M] + return x + + def norm_spec(self, x): + return (x - self.spec_min) / (self.spec_max - self.spec_min) * 2 - 1 + + def denorm_spec(self, x): + return (x + 1) / 2 * (self.spec_max - self.spec_min) + self.spec_min + + +class RepetitiveRectifiedFlow(RectifiedFlow): + def __init__(self, vmin: float | int | list, vmax: float | int | list, + repeat_bins: int, time_scale_factor=1000, + backbone_type=None, backbone_args=None): + assert (isinstance(vmin, (float, int)) and isinstance(vmax, (float, int))) or len(vmin) == len(vmax) + num_feats = 1 if isinstance(vmin, (float, int)) else len(vmin) + spec_min = [vmin] if num_feats == 1 else [[v] for v in vmin] + spec_max = [vmax] if num_feats == 1 else [[v] for v in vmax] + self.repeat_bins = repeat_bins + super().__init__( + out_dims=repeat_bins, num_feats=num_feats, + time_scale_factor=time_scale_factor, + backbone_type=backbone_type, backbone_args=backbone_args, + spec_min=spec_min, spec_max=spec_max + ) + + def norm_spec(self, x): + """ + + :param x: [B, T] or [B, F, T] + :return [B, T, R] or [B, F, T, R] + """ + if self.num_feats == 1: + repeats = [1, 1, self.repeat_bins] + else: + repeats = [1, 1, 1, self.repeat_bins] + return super().norm_spec(x.unsqueeze(-1).repeat(repeats)) + + def denorm_spec(self, x): + """ + + :param x: [B, T, R] or [B, F, T, R] + :return [B, T] or [B, F, T] + """ + return super().denorm_spec(x).mean(dim=-1) + + +class PitchRectifiedFlow(RepetitiveRectifiedFlow): + def __init__(self, vmin: float, vmax: float, + cmin: float, cmax: float, repeat_bins, + time_scale_factor=1000, + backbone_type=None, backbone_args=None): + self.vmin = vmin # norm min + self.vmax = vmax # norm max + self.cmin = cmin # clip min + self.cmax = cmax # clip max + super().__init__( + vmin=vmin, vmax=vmax, repeat_bins=repeat_bins, + time_scale_factor=time_scale_factor, + backbone_type=backbone_type, backbone_args=backbone_args + ) + + def norm_spec(self, x): + return super().norm_spec(x.clamp(min=self.cmin, max=self.cmax)) + + def denorm_spec(self, x): + return super().denorm_spec(x).clamp(min=self.cmin, max=self.cmax) + + +class MultiVarianceRectifiedFlow(RepetitiveRectifiedFlow): + def __init__( + self, ranges: List[Tuple[float, float]], + clamps: List[Tuple[float | None, float | None] | None], + repeat_bins, time_scale_factor=1000, + backbone_type=None, backbone_args=None + ): + assert len(ranges) == len(clamps) + self.clamps = clamps + vmin = [r[0] for r in ranges] + vmax = [r[1] for r in ranges] + if len(vmin) == 1: + vmin = vmin[0] + if len(vmax) == 1: + vmax = vmax[0] + super().__init__( + vmin=vmin, vmax=vmax, repeat_bins=repeat_bins, + time_scale_factor=time_scale_factor, + backbone_type=backbone_type, backbone_args=backbone_args + ) + + def clamp_spec(self, xs: list | tuple): + clamped = [] + for x, c in zip(xs, self.clamps): + if c is None: + clamped.append(x) + continue + clamped.append(x.clamp(min=c[0], max=c[1])) + return clamped + + def norm_spec(self, xs: list | tuple): + """ + + :param xs: sequence of [B, T] + :return: [B, F, T] => super().norm_spec(xs) => [B, F, T, R] + """ + assert len(xs) == self.num_feats + clamped = self.clamp_spec(xs) + xs = torch.stack(clamped, dim=1) # [B, F, T] + if self.num_feats == 1: + xs = xs.squeeze(1) # [B, T] + return super().norm_spec(xs) + + def denorm_spec(self, xs): + """ + + :param xs: [B, T, R] or [B, F, T, R] => super().denorm_spec(xs) => [B, T] or [B, F, T] + :return: sequence of [B, T] + """ + xs = super().denorm_spec(xs) + if self.num_feats == 1: + xs = [xs] + else: + xs = xs.unbind(dim=1) + assert len(xs) == self.num_feats + return self.clamp_spec(xs) + diff --git a/modules/fastspeech/acoustic_encoder.py b/modules/fastspeech/acoustic_encoder.py new file mode 100644 index 0000000..cbd0480 --- /dev/null +++ b/modules/fastspeech/acoustic_encoder.py @@ -0,0 +1,185 @@ +import torch +import torch.nn as nn +from torch.nn import functional as F + +from modules.commons.common_layers import ( + NormalInitEmbedding as Embedding, + SinusoidalPosEmb, + AdamWLinear, +) +from modules.fastspeech.tts_modules import FastSpeech2Encoder, mel2ph_to_dur, StretchRegulator +from utils.hparams import hparams +from utils.phoneme_utils import PAD_INDEX + + +class FastSpeech2Acoustic(nn.Module): + def __init__(self, vocab_size): + super().__init__() + self.txt_embed = Embedding(vocab_size, hparams['hidden_size'], PAD_INDEX) + self.use_lang_id = hparams.get('use_lang_id', False) + if self.use_lang_id: + self.lang_embed = Embedding(hparams['num_lang'] + 1, hparams['hidden_size'], padding_idx=0) + + self.use_stretch_embed = hparams.get('use_stretch_embed', False) + if self.use_stretch_embed: + self.sr = StretchRegulator() + self.stretch_embed = nn.Sequential( + SinusoidalPosEmb(hparams['hidden_size']), + nn.Linear(hparams['hidden_size'], hparams['hidden_size'] * 4), + nn.GELU(), + nn.Linear(hparams['hidden_size'] * 4, hparams['hidden_size']), + ) + self.stretch_embed_rnn = nn.GRU(hparams['hidden_size'], hparams['hidden_size'], 1, batch_first=True) + self._stretch_embed_rnn_flattened = False + + self.dur_embed = AdamWLinear(1, hparams['hidden_size']) + self.use_mix_ln = hparams.get('use_mix_ln', False) + if self.use_mix_ln: + self.mix_ln_layer = hparams['mix_ln_layer'] + else: + self.mix_ln_layer = [] + self.encoder = FastSpeech2Encoder( + hidden_size=hparams['hidden_size'], num_layers=hparams['enc_layers'], + ffn_kernel_size=hparams['enc_ffn_kernel_size'], ffn_act=hparams['ffn_act'], + dropout=hparams['dropout'], num_heads=hparams['num_heads'], + use_pos_embed=hparams['use_pos_embed'], rel_pos=hparams.get('rel_pos', False), + use_rope=hparams.get('use_rope', False), rope_interleaved=hparams.get('rope_interleaved', True), + mix_ln_layer=self.mix_ln_layer + ) + + self.pitch_embed = AdamWLinear(1, hparams['hidden_size']) + self.variance_embed_list = [] + self.use_energy_embed = hparams.get('use_energy_embed', False) + self.use_breathiness_embed = hparams.get('use_breathiness_embed', False) + self.use_voicing_embed = hparams.get('use_voicing_embed', False) + self.use_tension_embed = hparams.get('use_tension_embed', False) + if self.use_energy_embed: + self.variance_embed_list.append('energy') + if self.use_breathiness_embed: + self.variance_embed_list.append('breathiness') + if self.use_voicing_embed: + self.variance_embed_list.append('voicing') + if self.use_tension_embed: + self.variance_embed_list.append('tension') + + self.use_variance_embeds = len(self.variance_embed_list) > 0 + if self.use_variance_embeds: + self.variance_embeds = nn.ModuleDict({ + v_name: AdamWLinear(1, hparams['hidden_size']) + for v_name in self.variance_embed_list + }) + + self.use_variance_scaling = hparams.get('use_variance_scaling', False) + if self.use_variance_scaling: + self.variance_scaling_factor = { + 'energy': 1. / 96, # 96 dB — max dynamic range of 16-bit audio + 'breathiness': 1. / 96, + 'voicing': 1. / 96, + 'tension': 0.1, # 1 / 10; tension logits are roughly [-10, 10] + 'key_shift': 1. / 12, # one octave — max key shift in most editors + 'speed': 1. + } + else: + self.variance_scaling_factor = { + 'energy': 1., + 'breathiness': 1., + 'voicing': 1., + 'tension': 1., + 'key_shift': 1., + 'speed': 1. + } + + self.use_key_shift_embed = hparams.get('use_key_shift_embed', False) + if self.use_key_shift_embed: + self.key_shift_embed = AdamWLinear(1, hparams['hidden_size']) + + self.use_speed_embed = hparams.get('use_speed_embed', False) + if self.use_speed_embed: + self.speed_embed = AdamWLinear(1, hparams['hidden_size']) + + self.use_spk_id = hparams['use_spk_id'] + if self.use_spk_id: + self.spk_embed = Embedding(hparams['num_spk'], hparams['hidden_size']) + + def forward_variance_embedding(self, condition, key_shift=None, speed=None, **variances): + if self.use_variance_embeds: + variance_embeds = torch.stack([ + self.variance_embeds[v_name](variances[v_name][:, :, None] * self.variance_scaling_factor[v_name]) + for v_name in self.variance_embed_list + ], dim=-1).sum(-1) + condition += variance_embeds + + if self.use_key_shift_embed: + key_shift_embed = self.key_shift_embed(key_shift[:, :, None] * self.variance_scaling_factor['key_shift']) + condition += key_shift_embed + + if self.use_speed_embed: + speed_embed = self.speed_embed(speed[:, :, None] * self.variance_scaling_factor['speed']) + condition += speed_embed + + return condition + + def forward( + self, txt_tokens, mel2ph, f0, + key_shift=None, speed=None, + spk_embed_id=None, languages=None, + **kwargs + ): + spk_embed = None + if self.use_spk_id: + spk_mix_embed = kwargs.get('spk_mix_embed') + if spk_mix_embed is not None: + spk_embed = spk_mix_embed + else: + spk_embed = self.spk_embed(spk_embed_id)[:, None, :] + txt_embed = self.txt_embed(txt_tokens) + dur = mel2ph_to_dur(mel2ph, txt_tokens.shape[1]) + if self.use_variance_scaling: + dur_embed = self.dur_embed(torch.log(1 + dur[:, :, None].float())) + else: + dur_embed = self.dur_embed(dur[:, :, None].float()) + if self.use_lang_id: + lang_embed = self.lang_embed(languages) + extra_embed = dur_embed + lang_embed + else: + extra_embed = dur_embed + encoder_out = self.encoder(txt_embed, extra_embed, txt_tokens == 0, spk_embed) + + encoder_out = F.pad(encoder_out, [0, 0, 1, 0]) + mel2ph_ = mel2ph[..., None].repeat([1, 1, encoder_out.shape[-1]]) + condition = torch.gather(encoder_out, 1, mel2ph_) + + if self.use_stretch_embed: + stretch = torch.round(1000 * self.sr(mel2ph, dur)) + if self.training and stretch.numel() > 1000: + # construct a phoneme stretching index lookup table with a total of 1001 indexes (0~1000) + table = self.stretch_embed(torch.arange(0, 1001, device=stretch.device)) + stretch_embed = torch.index_select(table, 0, stretch.view(-1).long()).view_as(condition) + else: + stretch_embed = self.stretch_embed(stretch) + condition += stretch_embed + # flatten_parameters fuses the GRU weights into a contiguous buffer for cuDNN. + # It only needs to happen once after weight init, device change, or load_state_dict. + # We guard with a flag to avoid the redundant call on every forward. + # Limitation: the flag lives on this module and is invisible to PyTorch. After + # load_state_dict() or model.to(device) replaces the GRU weights, the flag stays + # True and flatten_parameters is skipped — cuDNN will fall back to the slower path. + # To restore the fast path, reset the flag manually: model._stretch_embed_rnn_flattened = False + if not self._stretch_embed_rnn_flattened: + self.stretch_embed_rnn.flatten_parameters() + self._stretch_embed_rnn_flattened = True + stretch_embed_rnn_out, _ = self.stretch_embed_rnn(condition) + condition = condition + stretch_embed_rnn_out + + if self.use_spk_id: + condition += spk_embed + + f0_mel = (1 + f0 / 700).log() + pitch_embed = self.pitch_embed(f0_mel[:, :, None]) + condition += pitch_embed + + condition = self.forward_variance_embedding( + condition, key_shift=key_shift, speed=speed, **kwargs + ) + + return condition diff --git a/modules/fastspeech/param_adaptor.py b/modules/fastspeech/param_adaptor.py new file mode 100644 index 0000000..77ebb83 --- /dev/null +++ b/modules/fastspeech/param_adaptor.py @@ -0,0 +1,95 @@ +from __future__ import annotations + +import torch + +import modules.compat as compat +from modules.core.ddpm import MultiVarianceDiffusion +from utils import filter_kwargs +from utils.hparams import hparams + +VARIANCE_CHECKLIST = ['energy', 'breathiness', 'voicing', 'tension'] + + +class ParameterAdaptorModule(torch.nn.Module): + def __init__(self): + super().__init__() + self.variance_prediction_list = [] + self.predict_energy = hparams.get('predict_energy', False) + self.predict_breathiness = hparams.get('predict_breathiness', False) + self.predict_voicing = hparams.get('predict_voicing', False) + self.predict_tension = hparams.get('predict_tension', False) + if self.predict_energy: + self.variance_prediction_list.append('energy') + if self.predict_breathiness: + self.variance_prediction_list.append('breathiness') + if self.predict_voicing: + self.variance_prediction_list.append('voicing') + if self.predict_tension: + self.variance_prediction_list.append('tension') + self.predict_variances = len(self.variance_prediction_list) > 0 + + def build_adaptor(self, cls=MultiVarianceDiffusion): + ranges = [] + clamps = [] + + if self.predict_energy: + ranges.append(( + hparams['energy_db_min'], + hparams['energy_db_max'] + )) + clamps.append((hparams['energy_db_min'], 0.)) + + if self.predict_breathiness: + ranges.append(( + hparams['breathiness_db_min'], + hparams['breathiness_db_max'] + )) + clamps.append((hparams['breathiness_db_min'], 0.)) + + if self.predict_voicing: + ranges.append(( + hparams['voicing_db_min'], + hparams['voicing_db_max'] + )) + clamps.append((hparams['voicing_db_min'], 0.)) + + if self.predict_tension: + ranges.append(( + hparams['tension_logit_min'], + hparams['tension_logit_max'] + )) + clamps.append(( + hparams['tension_logit_min'], + hparams['tension_logit_max'] + )) + + variances_hparams = hparams['variances_prediction_args'] + total_repeat_bins = variances_hparams['total_repeat_bins'] + assert total_repeat_bins % len(self.variance_prediction_list) == 0, \ + f'Total number of repeat bins must be divisible by number of ' \ + f'variance parameters ({len(self.variance_prediction_list)}).' + repeat_bins = total_repeat_bins // len(self.variance_prediction_list) + backbone_type = compat.get_backbone_type(hparams, nested_config=variances_hparams) + backbone_args = compat.get_backbone_args(variances_hparams, backbone_type=backbone_type) + kwargs = filter_kwargs( + { + 'ranges': ranges, + 'clamps': clamps, + 'repeat_bins': repeat_bins, + 'timesteps': hparams.get('timesteps'), + 'time_scale_factor': hparams.get('time_scale_factor'), + 'backbone_type': backbone_type, + 'backbone_args': backbone_args + }, + cls + ) + return cls(**kwargs) + + def collect_variance_inputs(self, **kwargs) -> list: + return [kwargs.get(name) for name in self.variance_prediction_list] + + def collect_variance_outputs(self, variances: list | tuple) -> dict: + return { + name: pred + for name, pred in zip(self.variance_prediction_list, variances) + } diff --git a/modules/fastspeech/tts_modules.py b/modules/fastspeech/tts_modules.py new file mode 100644 index 0000000..10f7741 --- /dev/null +++ b/modules/fastspeech/tts_modules.py @@ -0,0 +1,455 @@ +import math + +import torch +import torch.nn as nn +from torch.nn import functional as F +from modules.commons.rotary_embedding_torch import RotaryEmbedding +from modules.commons.common_layers import SinusoidalPositionalEmbedding, EncSALayer, AdamWLinear +from modules.commons.espnet_positional_embedding import RelPositionalEncoding + +DEFAULT_MAX_SOURCE_POSITIONS = 2000 +DEFAULT_MAX_TARGET_POSITIONS = 2000 + + +class TransformerEncoderLayer(nn.Module): + def __init__(self, hidden_size, dropout, kernel_size=None, act='gelu', num_heads=2, rotary_embed=None, + layer_idx=None, mix_ln_layer=None): + super().__init__() + self.op = EncSALayer( + hidden_size, num_heads, dropout=dropout, + attention_dropout=0.0, relu_dropout=dropout, + kernel_size=kernel_size, + act=act, rotary_embed=rotary_embed, + layer_idx=layer_idx, mix_ln_layer=mix_ln_layer + ) + + def forward(self, x, **kwargs): + return self.op(x, **kwargs) + + +###################### +# fastspeech modules +###################### +class LayerNorm(torch.nn.LayerNorm): + """Layer normalization module. + :param int nout: output dim size + :param int dim: dimension to be normalized + """ + + def __init__(self, nout, dim=-1): + """Construct an LayerNorm object.""" + super(LayerNorm, self).__init__(nout, eps=1e-12) + self.dim = dim + + def forward(self, x): + """Apply layer normalization. + :param torch.Tensor x: input tensor + :return: layer normalized tensor + :rtype torch.Tensor + """ + if self.dim == -1: + return super(LayerNorm, self).forward(x) + return super(LayerNorm, self).forward(x.transpose(1, -1)).transpose(1, -1) + + +class DurationPredictor(torch.nn.Module): + """Duration predictor module. + This is a module of duration predictor described in `FastSpeech: Fast, Robust and Controllable Text to Speech`_. + The duration predictor predicts a duration of each frame in log domain from the hidden embeddings of encoder. + .. _`FastSpeech: Fast, Robust and Controllable Text to Speech`: + https://arxiv.org/pdf/1905.09263.pdf + Note: + The calculation domain of outputs is different between in `forward` and in `inference`. In `forward`, + the outputs are calculated in log domain but in `inference`, those are calculated in linear domain. + """ + + def __init__(self, in_dims, n_layers=2, n_chans=384, kernel_size=3, + dropout_rate=0.1, offset=1.0, dur_loss_type='mse', arch='resnet'): + """Initialize duration predictor module. + Args: + in_dims (int): Input dimension. + n_layers (int, optional): Number of convolutional layers. + n_chans (int, optional): Number of channels of convolutional layers. + kernel_size (int, optional): Kernel size of convolutional layers. + dropout_rate (float, optional): Dropout rate. + offset (float, optional): Offset value to avoid nan in log domain. + """ + super(DurationPredictor, self).__init__() + self.offset = offset + self.conv = torch.nn.ModuleList() + self.kernel_size = kernel_size + self.use_resnet = (arch == 'resnet') + for idx in range(n_layers): + in_chans = in_dims if idx == 0 else n_chans + if self.use_resnet: + self.conv.append(nn.Sequential( + LayerNorm(in_chans, dim=1), + nn.Conv1d(in_chans, n_chans, kernel_size, stride=1, padding=kernel_size // 2), + nn.ReLU(), + nn.Conv1d(n_chans, n_chans, 1), + nn.Dropout(dropout_rate) + )) + else: + self.conv.append(nn.Sequential( + nn.Identity(), # this is a placeholder for ConstantPad1d which is now merged into Conv1d + nn.Conv1d(in_chans, n_chans, kernel_size, stride=1, padding=kernel_size // 2), + nn.ReLU(), + LayerNorm(n_chans, dim=1), + nn.Dropout(dropout_rate) + )) + if self.use_resnet and in_dims != n_chans: + self.res_conv = nn.Conv1d(in_dims, n_chans, 1) + else: + self.res_conv = None + self.loss_type = dur_loss_type + if self.loss_type in ['mse', 'huber']: + self.out_dims = 1 + # elif hparams['dur_loss_type'] == 'mog': + # out_dims = 15 + # elif hparams['dur_loss_type'] == 'crf': + # out_dims = 32 + # from torchcrf import CRF + # self.crf = CRF(out_dims, batch_first=True) + else: + raise NotImplementedError() + self.linear = AdamWLinear(n_chans, self.out_dims) + + def out2dur(self, xs): + if self.loss_type in ['mse', 'huber']: + # NOTE: calculate loss in log domain + dur = xs.squeeze(-1).exp() - self.offset # (B, Tmax) + # elif hparams['dur_loss_type'] == 'crf': + # dur = torch.LongTensor(self.crf.decode(xs)).cuda() + else: + raise NotImplementedError() + return dur + + def forward(self, xs, x_masks=None, infer=True): + """Calculate forward propagation. + Args: + xs (Tensor): Batch of input sequences (B, Tmax, idim). + x_masks (BoolTensor, optional): Batch of masks indicating padded part (B, Tmax). + infer (bool): Whether inference + Returns: + (train) FloatTensor, (infer) LongTensor: Batch of predicted durations in linear domain (B, Tmax). + """ + xs = xs.transpose(1, -1) # (B, idim, Tmax) + masks = 1 - x_masks.float() + masks_ = masks[:, None, :] + for idx, f in enumerate(self.conv): + if self.use_resnet: + residual = self.res_conv(xs) if idx == 0 and self.res_conv is not None else xs + xs = residual + f(xs) + else: + xs = f(xs) + if x_masks is not None: + xs = xs * masks_ + xs = self.linear(xs.transpose(1, -1)) # [B, T, C] + xs = xs * masks[:, :, None] # (B, T, C) + + dur_pred = self.out2dur(xs) + if infer: + dur_pred = dur_pred.clamp(min=0.) # avoid negative value + return dur_pred + + +class VariancePredictor(torch.nn.Module): + def __init__(self, vmin, vmax, in_dims, + n_layers=5, n_chans=512, kernel_size=5, + dropout_rate=0.1): + """Initialize variance predictor module. + Args: + in_dims (int): Input dimension. + n_layers (int, optional): Number of convolutional layers. + n_chans (int, optional): Number of channels of convolutional layers. + kernel_size (int, optional): Kernel size of convolutional layers. + dropout_rate (float, optional): Dropout rate. + """ + super(VariancePredictor, self).__init__() + + self.vmin = vmin + self.vmax = vmax + self.conv = torch.nn.ModuleList() + self.kernel_size = kernel_size + for idx in range(n_layers): + in_chans = in_dims if idx == 0 else n_chans + self.conv.append(torch.nn.Sequential( + torch.nn.Conv1d(in_chans, n_chans, kernel_size, stride=1, padding=kernel_size // 2), + torch.nn.ReLU(), + LayerNorm(n_chans, dim=1), + torch.nn.Dropout(dropout_rate) + )) + self.linear = torch.nn.Linear(n_chans, 1) + self.embed_positions = SinusoidalPositionalEmbedding(in_dims, 0, init_size=4096) + self.pos_embed_alpha = nn.Parameter(torch.Tensor([1])) + + def out2value(self, xs): + return (xs + 1) / 2 * (self.vmax - self.vmin) + self.vmin + + def forward(self, xs, infer=True): + """ + :param xs: [B, T, H] + :param infer: whether inference + :return: [B, T] + """ + positions = self.pos_embed_alpha * self.embed_positions(xs[..., 0]) + xs = xs + positions + xs = xs.transpose(1, -1) # (B, idim, Tmax) + for f in self.conv: + xs = f(xs) # (B, C, Tmax) + xs = self.linear(xs.transpose(1, -1)).squeeze(-1) # (B, Tmax) + if infer: + xs = self.out2value(xs) + return xs + + +class PitchPredictor(torch.nn.Module): + def __init__(self, vmin, vmax, num_bins, deviation, + in_dims, n_layers=5, n_chans=384, kernel_size=5, + dropout_rate=0.1): + """Initialize pitch predictor module. + Args: + in_dims (int): Input dimension. + n_layers (int, optional): Number of convolutional layers. + n_chans (int, optional): Number of channels of convolutional layers. + kernel_size (int, optional): Kernel size of convolutional layers. + dropout_rate (float, optional): Dropout rate. + """ + super(PitchPredictor, self).__init__() + self.vmin = vmin + self.vmax = vmax + self.interval = (vmax - vmin) / (num_bins - 1) # align with centers of bins + self.sigma = deviation / self.interval + self.register_buffer('x', torch.arange(num_bins).float().reshape(1, 1, -1)) # [1, 1, N] + + self.base_pitch_embed = torch.nn.Linear(1, in_dims) + self.conv = torch.nn.ModuleList() + self.kernel_size = kernel_size + for idx in range(n_layers): + in_chans = in_dims if idx == 0 else n_chans + self.conv.append(torch.nn.Sequential( + torch.nn.Conv1d(in_chans, n_chans, kernel_size, stride=1, padding=kernel_size // 2), + torch.nn.ReLU(), + LayerNorm(n_chans, dim=1), + torch.nn.Dropout(dropout_rate) + )) + self.linear = torch.nn.Linear(n_chans, num_bins) + self.embed_positions = SinusoidalPositionalEmbedding(in_dims, 0, init_size=4096) + self.pos_embed_alpha = nn.Parameter(torch.Tensor([1])) + + def bins_to_values(self, bins): + return bins * self.interval + self.vmin + + def out2pitch(self, probs): + logits = probs.sigmoid() # [B, T, N] + # return logits + # logits_sum = logits.sum(dim=2) # [B, T] + bins = torch.sum(self.x * logits, dim=2) / torch.sum(logits, dim=2) # [B, T] + pitch = self.bins_to_values(bins) + # uv = logits_sum / (self.sigma * math.sqrt(2 * math.pi)) < 0.3 + # pitch[uv] = torch.nan + return pitch + + def forward(self, xs, base): + """ + :param xs: [B, T, H] + :param base: [B, T] + :return: [B, T, N] + """ + xs = xs + self.base_pitch_embed(base[..., None]) + positions = self.pos_embed_alpha * self.embed_positions(xs[..., 0]) + xs = xs + positions + xs = xs.transpose(1, -1) # (B, idim, Tmax) + for f in self.conv: + xs = f(xs) # (B, C, Tmax) + xs = self.linear(xs.transpose(1, -1)) # (B, Tmax, H) + return self.out2pitch(xs) + base, xs + + +class RhythmRegulator(torch.nn.Module): + def __init__(self, eps=1e-5): + super().__init__() + self.eps = eps + + def forward(self, ph_dur, ph2word, word_dur): + """ + Example (no batch dim version): + 1. ph_dur = [4,2,3,2] + 2. word_dur = [3,4,2], ph2word = [1,2,2,3] + 3. word_dur_in = [4,5,2] + 4. alpha_w = [0.75,0.8,1], alpha_ph = [0.75,0.8,0.8,1] + 5. ph_dur_out = [3,1.6,2.4,2] + :param ph_dur: [B, T_ph] + :param ph2word: [B, T_ph] + :param word_dur: [B, T_w] + """ + ph_dur = ph_dur.float() * (ph2word > 0) + word_dur = word_dur.float() + word_dur_in = ph_dur.new_zeros(ph_dur.shape[0], ph2word.max() + 1).scatter_add( + 1, ph2word, ph_dur + )[:, 1:] # [B, T_ph] => [B, T_w] + alpha_w = word_dur / word_dur_in.clamp(min=self.eps) # avoid dividing by zero + alpha_ph = torch.gather(F.pad(alpha_w, [1, 0]), 1, ph2word) # [B, T_w] => [B, T_ph] + ph_dur_out = ph_dur * alpha_ph + return ph_dur_out.round().long() + + +class LengthRegulator(torch.nn.Module): + # noinspection PyMethodMayBeStatic + def forward(self, dur, dur_padding=None, alpha=None): + """ + Example (no batch dim version): + 1. dur = [2,2,3] + 2. token_idx = [[1],[2],[3]], dur_cumsum = [2,4,7], dur_cumsum_prev = [0,2,4] + 3. token_mask = [[1,1,0,0,0,0,0], + [0,0,1,1,0,0,0], + [0,0,0,0,1,1,1]] + 4. token_idx * token_mask = [[1,1,0,0,0,0,0], + [0,0,2,2,0,0,0], + [0,0,0,0,3,3,3]] + 5. (token_idx * token_mask).sum(0) = [1,1,2,2,3,3,3] + + :param dur: Batch of durations of each frame (B, T_txt) + :param dur_padding: Batch of padding of each frame (B, T_txt) + :param alpha: duration rescale coefficient + :return: + mel2ph (B, T_speech) + """ + assert alpha is None or alpha > 0 + if alpha is not None: + dur = torch.round(dur.float() * alpha).long() + if dur_padding is not None: + dur = dur * (1 - dur_padding.long()) + token_idx = torch.arange(1, dur.shape[1] + 1)[None, :, None].to(dur.device) + dur_cumsum = torch.cumsum(dur, 1) + dur_cumsum_prev = F.pad(dur_cumsum, [1, -1], mode='constant', value=0) + + pos_idx = torch.arange(dur.sum(-1).max())[None, None].to(dur.device) + token_mask = (pos_idx >= dur_cumsum_prev[:, :, None]) & (pos_idx < dur_cumsum[:, :, None]) + mel2ph = (token_idx * token_mask.long()).sum(1) + return mel2ph + + +class StretchRegulator(torch.nn.Module): + # noinspection PyMethodMayBeStatic + def forward(self, mel2ph, dur=None): + """ + Example (no batch dim version): + 1. dur = [2,4,3] + 2. mel2ph = [1,1,2,2,2,2,3,3,3] + 3. mel2dur = [2,2,4,4,4,4,3,3,3] + 4. bound_mask = [0,1,0,0,0,1,0,0,1] + 5. 1 - bound_mask * mel2dur = [1,-1,1,1,1,-3,1,1,-2] => pad => [0,1,-1,1,1,1,-3,1,1] + 6. stretch_denorm = [0,1,0,1,2,3,0,1,2] + + :param dur: Batch of durations of each frame (B, T_txt) + :param mel2ph: Batch of mel2ph (B, T_speech) + :return: + stretch (B, T_speech) + """ + if dur is None: + dur = mel2ph_to_dur(mel2ph, mel2ph.max()) + dur = torch.cat([torch.ones_like(dur[:, :1]), dur], dim=1) # Avoid dividing by zero + mel2dur = torch.gather(dur, 1, mel2ph) + bound_mask = torch.gt(mel2ph[:, 1:], mel2ph[:, :-1]) + stretch_delta = 1 - bound_mask * mel2dur[:, :-1] + stretch_delta = F.pad(stretch_delta, [1, 0]) + stretch_denorm = torch.cumsum(stretch_delta, dim=1) + stretch = stretch_denorm.float() / mel2dur + return stretch * (mel2ph > 0) + + +def mel2ph_to_dur(mel2ph, T_txt, max_dur=None): + B, _ = mel2ph.shape + dur = mel2ph.new_zeros(B, T_txt + 1).scatter_add(1, mel2ph, torch.ones_like(mel2ph)) + dur = dur[:, 1:] + if max_dur is not None: + dur = dur.clamp(max=max_dur) + return dur + + +class FastSpeech2Encoder(nn.Module): + def __init__( + self, hidden_size, num_layers, + ffn_kernel_size=9, ffn_act='gelu', + dropout=None, num_heads=2, use_pos_embed=True, rel_pos=True, + use_rope=False, rope_interleaved=True, mix_ln_layer=None + ): + super().__init__() + self.num_layers = num_layers + embed_dim = self.hidden_size = hidden_size + self.dropout = dropout + self.use_pos_embed = use_pos_embed + if use_pos_embed and use_rope: + if embed_dim % (num_heads * 2) != 0: + raise ValueError( + "RoPE requires the hidden size to be multiple of " + f"num_heads * 2 = {num_heads * 2}, but got {embed_dim}." + ) + rotary_embed = RotaryEmbedding(dim=embed_dim // num_heads, interleaved=rope_interleaved) + else: + rotary_embed = None + self.layers = nn.ModuleList([ + TransformerEncoderLayer( + self.hidden_size, self.dropout, + kernel_size=ffn_kernel_size, act=ffn_act, + num_heads=num_heads, rotary_embed=rotary_embed, + layer_idx=i, mix_ln_layer=mix_ln_layer + ) + for i in range(self.num_layers) + ]) + self.layer_norm = nn.LayerNorm(embed_dim) + + self.embed_scale = math.sqrt(hidden_size) + self.padding_idx = 0 + self.rel_pos = rel_pos + if use_rope: + self.embed_positions = None + elif self.rel_pos: + self.embed_positions = RelPositionalEncoding(hidden_size, dropout_rate=0.0) + else: + self.embed_positions = SinusoidalPositionalEmbedding( + hidden_size, self.padding_idx, init_size=DEFAULT_MAX_TARGET_POSITIONS, + ) + + def forward_embedding(self, main_embed, extra_embed=None, padding_mask=None): + # embed tokens and positions + x = self.embed_scale * main_embed + if extra_embed is not None: + x = x + extra_embed + if self.use_pos_embed and self.embed_positions is not None: + if self.rel_pos: + x = self.embed_positions(x) + else: + positions = self.embed_positions(~padding_mask) + x = x + positions + x = F.dropout(x, p=self.dropout, training=self.training) + return x + + def forward(self, main_embed, extra_embed, padding_mask, spk_embed=None, attn_mask=None, return_hiddens=False): + x = self.forward_embedding(main_embed, extra_embed, padding_mask=padding_mask) # [B, T, H] + nonpadding_mask_BT = 1 - padding_mask.float()[:, :, None] # [B, T, 1] + + # NOTICE: + # The following codes are commented out because + # `self.use_pos_embed` is always False in the older versions, + # and this argument did not compat with `hparams['use_pos_embed']`, + # which defaults to True. The new version fixed this inconsistency, + # resulting in temporary removal of pos_embed_alpha, which has actually + # never been used before. + + # if self.use_pos_embed: + # positions = self.pos_embed_alpha * self.embed_positions(x[..., 0]) + # x = x + positions + # x = F.dropout(x, p=self.dropout, training=self.training) + + x = x * nonpadding_mask_BT + hiddens = [] + for layer in self.layers: + x = layer(x, encoder_padding_mask=padding_mask, cond=spk_embed, attn_mask=attn_mask) * nonpadding_mask_BT + if return_hiddens: + hiddens.append(x) + x = self.layer_norm(x) * nonpadding_mask_BT + if return_hiddens: + x = torch.stack(hiddens, 0) # [L, B, T, C] + return x diff --git a/modules/fastspeech/variance_encoder.py b/modules/fastspeech/variance_encoder.py new file mode 100644 index 0000000..7129648 --- /dev/null +++ b/modules/fastspeech/variance_encoder.py @@ -0,0 +1,158 @@ +import torch +import torch.nn as nn +from torch.nn import functional as F + +from modules.commons.common_layers import ( + NormalInitEmbedding as Embedding, + XavierUniformInitLinear as Linear, + AdamWLinear, +) +from modules.fastspeech.tts_modules import FastSpeech2Encoder, DurationPredictor +from utils.hparams import hparams +from utils.phoneme_utils import PAD_INDEX + + +class FastSpeech2Variance(nn.Module): + def __init__(self, vocab_size): + super().__init__() + self.predict_dur = hparams['predict_dur'] + self.linguistic_mode = 'word' if hparams['predict_dur'] else 'phoneme' + self.use_lang_id = hparams['use_lang_id'] + self.use_variance_scaling = hparams.get('use_variance_scaling', False) + self.txt_embed = Embedding(vocab_size, hparams['hidden_size'], PAD_INDEX) + if self.use_lang_id: + self.lang_embed = Embedding(hparams['num_lang'] + 1, hparams['hidden_size'], padding_idx=0) + + if self.predict_dur: + self.onset_embed = Embedding(2, hparams['hidden_size']) + self.word_dur_embed = AdamWLinear(1, hparams['hidden_size']) + else: + self.ph_dur_embed = AdamWLinear(1, hparams['hidden_size']) + + self.encoder = FastSpeech2Encoder( + hidden_size=hparams['hidden_size'], num_layers=hparams['enc_layers'], + ffn_kernel_size=hparams['enc_ffn_kernel_size'], ffn_act=hparams['ffn_act'], + dropout=hparams['dropout'], num_heads=hparams['num_heads'], + use_pos_embed=hparams['use_pos_embed'], rel_pos=hparams.get('rel_pos', False), + use_rope=hparams.get('use_rope', False), rope_interleaved=hparams.get('rope_interleaved', True) + ) + + dur_hparams = hparams['dur_prediction_args'] + if self.predict_dur: + self.midi_embed = Embedding(128, hparams['hidden_size']) + self.dur_predictor = DurationPredictor( + in_dims=hparams['hidden_size'], + n_chans=dur_hparams['hidden_size'], + n_layers=dur_hparams['num_layers'], + dropout_rate=dur_hparams['dropout'], + kernel_size=dur_hparams['kernel_size'], + offset=dur_hparams['log_offset'], + dur_loss_type=dur_hparams['loss_type'], + arch=dur_hparams['arch'] + ) + + def forward( + self, txt_tokens, midi, ph2word, + ph_dur=None, word_dur=None, + spk_embed=None, languages=None, + infer=True + ): + """ + :param txt_tokens: (train, infer) [B, T_ph] + :param midi: (train, infer) [B, T_ph] + :param ph2word: (train, infer) [B, T_ph] + :param ph_dur: (train, [infer]) [B, T_ph] + :param word_dur: (infer) [B, T_w] + :param spk_embed: (train) [B, T_ph, H] + :param languages (train, infer) [B, T_ph] + :param infer: whether inference + :return: encoder_out, ph_dur_pred + """ + txt_embed = self.txt_embed(txt_tokens) + if self.linguistic_mode == 'word': + b = txt_tokens.shape[0] + onset = torch.diff(ph2word, dim=1, prepend=ph2word.new_zeros(b, 1)) > 0 + onset_embed = self.onset_embed(onset.long()) # [B, T_ph, H] + + if word_dur is None or not infer: + word_dur = ph_dur.new_zeros(b, ph2word.max() + 1).scatter_add( + 1, ph2word, ph_dur + )[:, 1:] # [B, T_ph] => [B, T_w] + word_dur = torch.gather(F.pad(word_dur, [1, 0], value=0), 1, ph2word) # [B, T_w] => [B, T_ph] + word_dur_embed = self.word_dur_embed(word_dur.float()[:, :, None]) + extra_embed = onset_embed + word_dur_embed + elif self.use_variance_scaling: + extra_embed = self.ph_dur_embed(torch.log(1 + ph_dur.float())[:, :, None]) + else: + extra_embed = self.ph_dur_embed(ph_dur.float()[:, :, None]) + + if self.use_lang_id: + lang_embed = self.lang_embed(languages) + extra_embed += lang_embed + encoder_out = self.encoder(txt_embed, extra_embed, txt_tokens == 0) + + if self.predict_dur: + midi_embed = self.midi_embed(midi) # => [B, T_ph, H] + dur_cond = encoder_out + midi_embed + if spk_embed is not None: + dur_cond += spk_embed + ph_dur_pred = self.dur_predictor(dur_cond, x_masks=txt_tokens == PAD_INDEX, infer=infer) + + return encoder_out, ph_dur_pred + else: + return encoder_out, None + + +class MelodyEncoder(nn.Module): + def __init__(self, enc_hparams: dict): + super().__init__() + + def get_hparam(key): + return enc_hparams.get(key, hparams.get(key)) + + # MIDI inputs + hidden_size = get_hparam('hidden_size') + self.use_variance_scaling = hparams.get('use_variance_scaling', False) + self.note_midi_embed = AdamWLinear(1, hidden_size) + self.note_dur_embed = AdamWLinear(1, hidden_size) + + # ornament inputs + self.use_glide_embed = hparams['use_glide_embed'] + self.glide_embed_scale = hparams['glide_embed_scale'] + if self.use_glide_embed: + # 0: none, 1: up, 2: down + self.note_glide_embed = Embedding(len(hparams['glide_types']) + 1, hidden_size, padding_idx=0) + + self.encoder = FastSpeech2Encoder( + hidden_size=hidden_size, num_layers=get_hparam('enc_layers'), + ffn_kernel_size=get_hparam('enc_ffn_kernel_size'), ffn_act=get_hparam('ffn_act'), + dropout=get_hparam('dropout'), num_heads=get_hparam('num_heads'), + use_pos_embed=get_hparam('use_pos_embed'), rel_pos=get_hparam('rel_pos'), + use_rope=get_hparam('use_rope'), rope_interleaved=hparams.get('rope_interleaved', True) + ) + self.out_proj = Linear(hidden_size, hparams['hidden_size']) + + def forward(self, note_midi, note_rest, note_dur, glide=None): + """ + :param note_midi: float32 [B, T_n], -1: padding + :param note_rest: bool [B, T_n] + :param note_dur: int64 [B, T_n] + :param glide: int64 [B, T_n] + :return: [B, T_n, H] + """ + if self.use_variance_scaling: + midi_embed = self.note_midi_embed(note_midi[:, :, None] / 128) + dur_embed = self.note_dur_embed(torch.log(1 + note_dur.float())[:, :, None]) + else: + midi_embed = self.note_midi_embed(note_midi[:, :, None]) + dur_embed = self.note_dur_embed(note_dur.float()[:, :, None]) + midi_embed *= ~note_rest[:, :, None] + ornament_embed = 0 + if self.use_glide_embed: + ornament_embed += self.note_glide_embed(glide) * self.glide_embed_scale + encoder_out = self.encoder( + midi_embed, dur_embed + ornament_embed, + padding_mask=note_midi < 0 + ) + encoder_out = self.out_proj(encoder_out) + return encoder_out diff --git a/modules/hnsep/__init__.py b/modules/hnsep/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/modules/hnsep/vr/__init__.py b/modules/hnsep/vr/__init__.py new file mode 100644 index 0000000..c6d4840 --- /dev/null +++ b/modules/hnsep/vr/__init__.py @@ -0,0 +1,35 @@ +import pathlib + +import torch +import yaml + +from .nets import CascadedNet + + +class DotDict(dict): + def __getattr__(*args): + val = dict.get(*args) + return DotDict(val) if type(val) is dict else val + + __setattr__ = dict.__setitem__ + __delattr__ = dict.__delitem__ + + +def load_sep_model(model_path, device='cpu'): + model_path = pathlib.Path(model_path) + config_file = model_path.with_name('config.yaml') + with open(config_file, "r") as config: + args = yaml.safe_load(config) + args = DotDict(args) + model = CascadedNet( + args.n_fft, + args.hop_length, + args.n_out, + args.n_out_lstm, + True, + is_mono=args.is_mono + ) + model.to(device) + model.load_state_dict(torch.load(model_path, map_location='cpu')) + model.eval() + return model diff --git a/modules/hnsep/vr/layers.py b/modules/hnsep/vr/layers.py new file mode 100644 index 0000000..4b4a2ec --- /dev/null +++ b/modules/hnsep/vr/layers.py @@ -0,0 +1,166 @@ +import torch +from torch import nn +import torch.nn.functional as F + + +def crop_center(h1, h2): + h1_shape = h1.size() + h2_shape = h2.size() + + if h1_shape[3] == h2_shape[3]: + return h1 + elif h1_shape[3] < h2_shape[3]: + raise ValueError('h1_shape[3] must be greater than h2_shape[3]') + + # s_freq = (h2_shape[2] - h1_shape[2]) // 2 + # e_freq = s_freq + h1_shape[2] + s_time = (h1_shape[3] - h2_shape[3]) // 2 + e_time = s_time + h2_shape[3] + h1 = h1[:, :, :, s_time:e_time] + + return h1 + + +class Conv2DBNActiv(nn.Module): + + def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): + super(Conv2DBNActiv, self).__init__() + self.conv = nn.Sequential( + nn.Conv2d( + nin, nout, + kernel_size=ksize, + stride=stride, + padding=pad, + dilation=dilation, + bias=False + ), + nn.BatchNorm2d(nout), + activ() + ) + + def forward(self, x): + return self.conv(x) + + +class Encoder(nn.Module): + + def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU): + super(Encoder, self).__init__() + self.conv1 = Conv2DBNActiv(nin, nout, ksize, stride, pad, activ=activ) + self.conv2 = Conv2DBNActiv(nout, nout, ksize, 1, pad, activ=activ) + + def forward(self, x): + h = self.conv1(x) + h = self.conv2(h) + + return h + + +class Decoder(nn.Module): + + def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False): + super(Decoder, self).__init__() + self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) + # self.conv2 = Conv2DBNActiv(nout, nout, ksize, 1, pad, activ=activ) + self.dropout = nn.Dropout2d(0.1) if dropout else None + + def forward(self, x, skip=None, fixed_length=True): + if fixed_length: + x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True) + else: + _, _, h, w = x.size() + x = F.pad(x, (0, 1, 0, 1), mode='replicate') + x = F.interpolate(x, size=(2*h+1,2*w+1), mode='bilinear', align_corners=True) + x = x[:, :, :-1, :-1] + + if skip is not None: + skip = crop_center(skip, x) + x = torch.cat([x, skip], dim=1) + + h = self.conv1(x) + # h = self.conv2(h) + + if self.dropout is not None: + h = self.dropout(h) + + return h + + +class Mean(nn.Module): + def __init__(self, dim, keepdims=False): + super(Mean, self).__init__() + self.dim = dim + self.keepdims = keepdims + + def forward(self, x): + return x.mean(self.dim, keepdims=self.keepdims) + + +class ASPPModule(nn.Module): + + def __init__(self, nin, nout, dilations=(4, 8, 12), activ=nn.ReLU, dropout=False): + super(ASPPModule, self).__init__() + self.conv1 = nn.Sequential( + Mean(dim=-2, keepdims=True), # nn.AdaptiveAvgPool2d((1, None)), + Conv2DBNActiv(nin, nout, 1, 1, 0, activ=activ) + ) + self.conv2 = Conv2DBNActiv( + nin, nout, 1, 1, 0, activ=activ + ) + self.conv3 = Conv2DBNActiv( + nin, nout, 3, 1, dilations[0], dilations[0], activ=activ + ) + self.conv4 = Conv2DBNActiv( + nin, nout, 3, 1, dilations[1], dilations[1], activ=activ + ) + self.conv5 = Conv2DBNActiv( + nin, nout, 3, 1, dilations[2], dilations[2], activ=activ + ) + self.bottleneck = Conv2DBNActiv( + nout * 5, nout, 1, 1, 0, activ=activ + ) + self.dropout = nn.Dropout2d(0.1) if dropout else None + + def forward(self, x): + _, _, h, w = x.size() + # feat1 = F.interpolate(self.conv1(x), size=(h, w), mode='bilinear', align_corners=True) + feat1 = self.conv1(x).repeat(1, 1, h, 1) + feat2 = self.conv2(x) + feat3 = self.conv3(x) + feat4 = self.conv4(x) + feat5 = self.conv5(x) + out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1) + out = self.bottleneck(out) + + if self.dropout is not None: + out = self.dropout(out) + + return out + + +class LSTMModule(nn.Module): + + def __init__(self, nin_conv, nin_lstm, nout_lstm): + super(LSTMModule, self).__init__() + self.conv = Conv2DBNActiv(nin_conv, 1, 1, 1, 0) + self.lstm = nn.LSTM( + input_size=nin_lstm, + hidden_size=nout_lstm // 2, + bidirectional=True + ) + self.dense = nn.Sequential( + nn.Linear(nout_lstm, nin_lstm), + nn.BatchNorm1d(nin_lstm), + nn.ReLU() + ) + + def forward(self, x): + N, _, nbins, nframes = x.size() + h = self.conv(x)[:, 0] # N, nbins, nframes + h = h.permute(2, 0, 1) # nframes, N, nbins + h, _ = self.lstm(h) + h = self.dense(h.reshape(-1, h.size()[-1])) # nframes * N, nbins + h = h.reshape(nframes, N, 1, nbins) + h = h.permute(1, 2, 3, 0) + + return h diff --git a/modules/hnsep/vr/nets.py b/modules/hnsep/vr/nets.py new file mode 100644 index 0000000..f9da1d8 --- /dev/null +++ b/modules/hnsep/vr/nets.py @@ -0,0 +1,202 @@ +import torch +from torch import nn +import torch.nn.functional as F + +from . import layers + + +class BaseNet(nn.Module): + + def __init__(self, nin, nout, nin_lstm, nout_lstm, dilations=((4, 2), (8, 4), (12, 6)), fixed_length=True): + super(BaseNet, self).__init__() + self.enc1 = layers.Conv2DBNActiv(nin, nout, 3, 1, 1) + self.enc2 = layers.Encoder(nout, nout * 2, 3, 2, 1) + self.enc3 = layers.Encoder(nout * 2, nout * 4, 3, 2, 1) + self.enc4 = layers.Encoder(nout * 4, nout * 6, 3, 2, 1) + self.enc5 = layers.Encoder(nout * 6, nout * 8, 3, 2, 1) + + self.aspp = layers.ASPPModule(nout * 8, nout * 8, dilations, dropout=True) + + self.dec4 = layers.Decoder(nout * (6 + 8), nout * 6, 3, 1, 1) + self.dec3 = layers.Decoder(nout * (4 + 6), nout * 4, 3, 1, 1) + self.dec2 = layers.Decoder(nout * (2 + 4), nout * 2, 3, 1, 1) + self.lstm_dec2 = layers.LSTMModule(nout * 2, nin_lstm, nout_lstm) + self.dec1 = layers.Decoder(nout * (1 + 2) + 1, nout * 1, 3, 1, 1) + + self.fixed_length = fixed_length + + def __call__(self, x): + e1 = self.enc1(x) + e2 = self.enc2(e1) + e3 = self.enc3(e2) + e4 = self.enc4(e3) + e5 = self.enc5(e4) + + h = self.aspp(e5) + + h = self.dec4(h, e4, fixed_length=self.fixed_length) + h = self.dec3(h, e3, fixed_length=self.fixed_length) + h = self.dec2(h, e2, fixed_length=self.fixed_length) + h = torch.cat([h, self.lstm_dec2(h)], dim=1) + h = self.dec1(h, e1, fixed_length=self.fixed_length) + + return h + + +class CascadedNet(nn.Module): + + def __init__(self, n_fft, hop_length, nout=32, nout_lstm=128, is_complex=False, is_mono=False, fixed_length=True): + super(CascadedNet, self).__init__() + self.n_fft = n_fft + self.hop_length = hop_length + self.seg_length = 32 * hop_length + self.is_complex = is_complex + self.is_mono = is_mono + self.register_buffer("window", torch.hann_window(n_fft), persistent=False) + self.max_bin = n_fft // 2 + self.output_bin = n_fft // 2 + 1 + self.nin_lstm = self.max_bin // 2 + self.offset = 64 + + nin = 4 if is_complex else 2 + if is_mono: + nin = nin // 2 + + self.stg1_low_band_net = nn.Sequential( + BaseNet(nin, nout // 2, self.nin_lstm // 2, nout_lstm, fixed_length=fixed_length), + layers.Conv2DBNActiv(nout // 2, nout // 4, 1, 1, 0) + ) + self.stg1_high_band_net = BaseNet( + nin, nout // 4, self.nin_lstm // 2, nout_lstm // 2, fixed_length=fixed_length + ) + + self.stg2_low_band_net = nn.Sequential( + BaseNet(nout // 4 + nin, nout, self.nin_lstm // 2, nout_lstm, fixed_length=fixed_length), + layers.Conv2DBNActiv(nout, nout // 2, 1, 1, 0) + ) + self.stg2_high_band_net = BaseNet( + nout // 4 + nin, nout // 2, self.nin_lstm // 2, nout_lstm // 2, fixed_length=fixed_length + ) + + self.stg3_full_band_net = BaseNet( + 3 * nout // 4 + nin, nout, self.nin_lstm, nout_lstm, fixed_length=fixed_length + ) + + self.out = nn.Conv2d(nout, nin, 1, bias=False) + self.aux_out = nn.Conv2d(3 * nout // 4, nin, 1, bias=False) + + def forward(self, x): + if self.is_complex: + x = torch.cat([x.real, x.imag], dim=1) + + x = x[:, :, :self.max_bin] + + bandw = x.size()[2] // 2 + l1_in = x[:, :, :bandw] + h1_in = x[:, :, bandw:] + l1 = self.stg1_low_band_net(l1_in) + h1 = self.stg1_high_band_net(h1_in) + aux1 = torch.cat([l1, h1], dim=2) + + l2_in = torch.cat([l1_in, l1], dim=1) + h2_in = torch.cat([h1_in, h1], dim=1) + l2 = self.stg2_low_band_net(l2_in) + h2 = self.stg2_high_band_net(h2_in) + aux2 = torch.cat([l2, h2], dim=2) + + f3_in = torch.cat([x, aux1, aux2], dim=1) + f3 = self.stg3_full_band_net(f3_in) + + if self.is_complex: + mask = self.out(f3) + if self.is_mono: + mask = torch.complex(mask[:, :1], mask[:, 1:]) + else: + mask = torch.complex(mask[:, :2], mask[:, 2:]) + mask = self.bounded_mask(mask) + else: + mask = torch.sigmoid(self.out(f3)) + + mask = F.pad( + input=mask, + pad=(0, 0, 0, self.output_bin - mask.size()[2]), + mode='replicate' + ) + + return mask + + def bounded_mask(self, mask, eps=1e-8): + mask_mag = torch.abs(mask) + mask = torch.tanh(mask_mag) * mask / (mask_mag + eps) + return mask + + def predict_mask(self, x): + mask = self.forward(x) + + if self.offset > 0: + mask = mask[:, :, :, self.offset:-self.offset] + assert mask.size()[3] > 0 + + return mask + + def predict(self, x): + mask = self.forward(x) + pred = x * mask + + if self.offset > 0: + pred = pred[:, :, :, self.offset:-self.offset] + assert pred.size()[3] > 0 + + return pred + + def audio2spec(self, x, use_pad=False): + B, C, T = x.shape + x = x.reshape(B * C, T) + if use_pad: + T1 = T + self.hop_length + T_pad = self.seg_length * ((T1 - 1) // self.seg_length + 1) - T1 + nl_pad = T_pad // 2 // self.hop_length + Tl_pad = nl_pad * self.hop_length + x = F.pad(x, (Tl_pad, T_pad - Tl_pad)) + spec = torch.stft( + x, + n_fft=self.n_fft, + hop_length=self.hop_length, + return_complex=True, + window=self.window, + pad_mode='constant' + ) + spec = spec.reshape(B, C, spec.shape[-2], spec.shape[-1]) + return spec + + def spec2audio(self, x): + B, C, N, T = x.shape + x = x.reshape(-1, N, T) + x = torch.istft(x, self.n_fft, self.hop_length, window=self.window) + x = x.reshape(B, C, -1) + return x + + def predict_from_audio(self, x): + B, C, T = x.shape + x = x.reshape(B * C, T) + T1 = T + self.hop_length + T_pad = self.seg_length * ((T1 - 1) // self.seg_length + 1) - T1 + nl_pad = T_pad // 2 // self.hop_length + Tl_pad = nl_pad * self.hop_length + x = F.pad(x, (Tl_pad, T_pad - Tl_pad)) + spec = torch.stft( + x, + n_fft=self.n_fft, + hop_length=self.hop_length, + return_complex=True, + window=self.window, + pad_mode='constant' + ) + spec = spec.reshape(B, C, spec.shape[-2], spec.shape[-1]) + mask = self.forward(spec) + spec_pred = spec * mask + spec_pred = spec_pred.reshape(B * C, spec.shape[-2], spec.shape[-1]) + x_pred = torch.istft(spec_pred, self.n_fft, self.hop_length, window=self.window) + x_pred = x_pred[:, Tl_pad: Tl_pad + T] + x_pred = x_pred.reshape(B, C, T) + return x_pred diff --git a/modules/losses/__init__.py b/modules/losses/__init__.py new file mode 100644 index 0000000..2a9bd82 --- /dev/null +++ b/modules/losses/__init__.py @@ -0,0 +1,3 @@ +from .diff_loss import DiffusionLoss +from .reflow_loss import RectifiedFlowLoss +from .dur_loss import DurationLoss diff --git a/modules/losses/diff_loss.py b/modules/losses/diff_loss.py new file mode 100644 index 0000000..860714b --- /dev/null +++ b/modules/losses/diff_loss.py @@ -0,0 +1,34 @@ +import torch.nn as nn +from torch import Tensor + + +class DiffusionLoss(nn.Module): + def __init__(self, loss_type): + super().__init__() + self.loss_type = loss_type + if self.loss_type == 'l1': + self.loss = nn.L1Loss(reduction='none') + elif self.loss_type == 'l2': + self.loss = nn.MSELoss(reduction='none') + else: + raise NotImplementedError() + + @staticmethod + def _mask_non_padding(x_recon, noise, non_padding=None): + if non_padding is not None: + non_padding = non_padding.transpose(1, 2).unsqueeze(1) + return x_recon * non_padding, noise * non_padding + else: + return x_recon, noise + + def _forward(self, x_recon, noise): + return self.loss(x_recon, noise) + + def forward(self, x_recon: Tensor, noise: Tensor, non_padding: Tensor = None) -> Tensor: + """ + :param x_recon: [B, 1, M, T] + :param noise: [B, 1, M, T] + :param non_padding: [B, T, M] + """ + x_recon, noise = self._mask_non_padding(x_recon, noise, non_padding) + return self._forward(x_recon, noise).mean() diff --git a/modules/losses/dur_loss.py b/modules/losses/dur_loss.py new file mode 100644 index 0000000..5aec3c9 --- /dev/null +++ b/modules/losses/dur_loss.py @@ -0,0 +1,56 @@ +import torch +import torch.nn as nn +from torch import Tensor + + +class DurationLoss(nn.Module): + """ + Loss module as combination of phone duration loss, word duration loss and sentence duration loss. + """ + + def __init__(self, offset, loss_type, + lambda_pdur=0.6, lambda_wdur=0.3, lambda_sdur=0.1): + super().__init__() + self.loss_type = loss_type + if self.loss_type == 'mse': + self.loss = nn.MSELoss() + elif self.loss_type == 'huber': + self.loss = nn.HuberLoss() + else: + raise NotImplementedError() + self.offset = offset + + self.lambda_pdur = lambda_pdur + self.lambda_wdur = lambda_wdur + self.lambda_sdur = lambda_sdur + + def linear2log(self, any_dur): + return torch.log(any_dur + self.offset) + + def forward(self, dur_pred: Tensor, dur_gt: Tensor, ph2word: Tensor) -> Tensor: + dur_gt = dur_gt.to(dtype=dur_pred.dtype) + + # pdur_loss + pdur_loss = self.lambda_pdur * self.loss(self.linear2log(dur_pred), self.linear2log(dur_gt)) + + dur_pred = dur_pred.clamp(min=0.) # clip to avoid NaN loss + + # wdur loss + shape = dur_pred.shape[0], ph2word.max() + 1 + wdur_pred = dur_pred.new_zeros(*shape).scatter_add( + 1, ph2word, dur_pred + )[:, 1:] # [B, T_ph] => [B, T_w] + wdur_gt = dur_gt.new_zeros(*shape).scatter_add( + 1, ph2word, dur_gt + )[:, 1:] # [B, T_ph] => [B, T_w] + wdur_loss = self.lambda_wdur * self.loss(self.linear2log(wdur_pred), self.linear2log(wdur_gt)) + + # sdur loss + sdur_pred = dur_pred.sum(dim=1) + sdur_gt = dur_gt.sum(dim=1) + sdur_loss = self.lambda_sdur * self.loss(self.linear2log(sdur_pred), self.linear2log(sdur_gt)) + + # combine + dur_loss = pdur_loss + wdur_loss + sdur_loss + + return dur_loss diff --git a/modules/losses/reflow_loss.py b/modules/losses/reflow_loss.py new file mode 100644 index 0000000..4917dce --- /dev/null +++ b/modules/losses/reflow_loss.py @@ -0,0 +1,50 @@ +import torch +import torch.nn as nn +from torch import Tensor + + +class RectifiedFlowLoss(nn.Module): + def __init__(self, loss_type, log_norm=True): + super().__init__() + self.loss_type = loss_type + self.log_norm = log_norm + if self.loss_type == 'l1': + self.loss = nn.L1Loss(reduction='none') + elif self.loss_type == 'l2': + self.loss = nn.MSELoss(reduction='none') + else: + raise NotImplementedError() + + @staticmethod + def _mask_non_padding(v_pred, v_gt, non_padding=None): + if non_padding is not None: + non_padding = non_padding.transpose(1, 2).unsqueeze(1) + return v_pred * non_padding, v_gt * non_padding + else: + return v_pred, v_gt + + @staticmethod + def get_weights(t): + eps = 1e-7 + t = t.float() + t = torch.clip(t, 0 + eps, 1 - eps) + weights = 0.398942 / t / (1 - t) * torch.exp( + -0.5 * torch.log(t / (1 - t)) ** 2 + ) + eps + return weights[:, None, None, None] + + def _forward(self, v_pred, v_gt, t=None): + if self.log_norm: + return self.get_weights(t) * self.loss(v_pred, v_gt) + else: + return self.loss(v_pred, v_gt) + + def forward(self, v_pred: Tensor, v_gt: Tensor, t: Tensor, non_padding: Tensor = None) -> Tensor: + """ + :param v_pred: [B, 1, M, T] + :param v_gt: [B, 1, M, T] + :param t: [B,] + :param non_padding: [B, T, M] + """ + v_pred, v_gt = self._mask_non_padding(v_pred, v_gt, non_padding) + return self._forward(v_pred, v_gt, t=t).mean() diff --git a/modules/metrics/__init__.py b/modules/metrics/__init__.py new file mode 100644 index 0000000..02cedd7 --- /dev/null +++ b/modules/metrics/__init__.py @@ -0,0 +1,2 @@ +from .curve import RawCurveAccuracy, RawCurveR2Score +from .duration import RhythmCorrectness, PhonemeDurationAccuracy diff --git a/modules/metrics/curve.py b/modules/metrics/curve.py new file mode 100644 index 0000000..8ae78fd --- /dev/null +++ b/modules/metrics/curve.py @@ -0,0 +1,73 @@ +import torch +import torchmetrics +from torch import Tensor + + +class RawCurveAccuracy(torchmetrics.Metric): + def __init__(self, *, tolerance, **kwargs): + super().__init__(**kwargs) + self.tolerance = tolerance + self.add_state('close', default=torch.tensor(0, dtype=torch.int), dist_reduce_fx='sum') + self.add_state('total', default=torch.tensor(0, dtype=torch.int), dist_reduce_fx='sum') + + def update(self, pred: Tensor, target: Tensor, mask=None) -> None: + """ + + :param pred: predicted curve + :param target: reference curve + :param mask: valid or non-padding mask + """ + if mask is None: + assert pred.shape == target.shape, f'shapes of pred and target mismatch: {pred.shape}, {target.shape}' + else: + assert pred.shape == target.shape == mask.shape, \ + f'shapes of pred, target and mask mismatch: {pred.shape}, {target.shape}, {mask.shape}' + close = torch.abs(pred - target) <= self.tolerance + if mask is not None: + close &= mask + + self.close += close.sum() + self.total += pred.numel() if mask is None else mask.sum() + + def compute(self) -> Tensor: + return self.close / self.total + + +class RawCurveR2Score(torchmetrics.Metric): + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.add_state('sum_squared_error', default=torch.tensor(0.0), dist_reduce_fx='sum') + self.add_state('sum_error', default=torch.tensor(0.0), dist_reduce_fx='sum') + self.add_state('residual', default=torch.tensor(0.0), dist_reduce_fx='sum') + self.add_state('total', default=torch.tensor(0), dist_reduce_fx='sum') + + def update(self, pred: Tensor, target: Tensor, mask=None) -> None: + """ + + :param pred: predicted curve + :param target: reference curve + :param mask: valid or non-padding mask + """ + if mask is None: + assert pred.shape == target.shape, f'shapes of pred and target mismatch: {pred.shape}, {target.shape}' + else: + assert pred.shape == target.shape == mask.shape, \ + f'shapes of pred, target and mask mismatch: {pred.shape}, {target.shape}, {mask.shape}' + pred = pred[mask] + target = target[mask] + pred = pred.flatten() + target = target.flatten() + + sum_error = torch.sum(target) + sum_squared_error = torch.sum(target * target) + residual = target - pred + rss = torch.sum(residual * residual) + total = target.numel() if mask is None else mask.sum() + + self.sum_squared_error += sum_squared_error + self.sum_error += sum_error + self.residual += rss + self.total += total + + def compute(self) -> Tensor: + return 1 - self.residual / (self.sum_squared_error - self.sum_error ** 2 / self.total) diff --git a/modules/metrics/duration.py b/modules/metrics/duration.py new file mode 100644 index 0000000..8043811 --- /dev/null +++ b/modules/metrics/duration.py @@ -0,0 +1,97 @@ +import torch +import torchmetrics +from torch import Tensor + +from modules.fastspeech.tts_modules import RhythmRegulator + + +def linguistic_checks(pred, target, ph2word, mask=None): + if mask is None: + assert pred.shape == target.shape == ph2word.shape, \ + f'shapes of pred, target and ph2word mismatch: {pred.shape}, {target.shape}, {ph2word.shape}' + else: + assert pred.shape == target.shape == ph2word.shape == mask.shape, \ + f'shapes of pred, target and mask mismatch: {pred.shape}, {target.shape}, {ph2word.shape}, {mask.shape}' + assert pred.ndim == 2, f'all inputs should be 2D, but got {pred.shape}' + assert torch.any(ph2word > 0), 'empty word sequence' + assert torch.all(ph2word >= 0), 'unexpected negative word index' + assert ph2word.max() <= pred.shape[1], f'word index out of range: {ph2word.max()} > {pred.shape[1]}' + assert torch.all(pred >= 0.), f'unexpected negative ph_dur prediction' + assert torch.all(target >= 0.), f'unexpected negative ph_dur target' + + +class RhythmCorrectness(torchmetrics.Metric): + def __init__(self, *, tolerance, **kwargs): + super().__init__(**kwargs) + assert 0. < tolerance < 1., 'tolerance should be within (0, 1)' + self.tolerance = tolerance + self.add_state('correct', default=torch.tensor(0, dtype=torch.int), dist_reduce_fx='sum') + self.add_state('total', default=torch.tensor(0, dtype=torch.int), dist_reduce_fx='sum') + + def update(self, pdur_pred: Tensor, pdur_target: Tensor, ph2word: Tensor, mask=None) -> None: + """ + + :param pdur_pred: predicted ph_dur + :param pdur_target: reference ph_dur + :param ph2word: word division sequence + :param mask: valid or non-padding mask + """ + linguistic_checks(pdur_pred, pdur_target, ph2word, mask=mask) + + shape = pdur_pred.shape[0], ph2word.max() + 1 + wdur_pred = pdur_pred.new_zeros(*shape).scatter_add( + 1, ph2word, pdur_pred + )[:, 1:] # [B, T_ph] => [B, T_w] + wdur_target = pdur_target.new_zeros(*shape).scatter_add( + 1, ph2word, pdur_target + )[:, 1:] # [B, T_ph] => [B, T_w] + if mask is None: + wdur_mask = torch.ones_like(wdur_pred, dtype=torch.bool) + else: + wdur_mask = mask.new_zeros(*shape).scatter_add( + 1, ph2word, mask + )[:, 1:].bool() # [B, T_ph] => [B, T_w] + + correct = torch.abs(wdur_pred - wdur_target) <= wdur_target * self.tolerance + correct &= wdur_mask + + self.correct += correct.sum() + self.total += wdur_mask.sum() + + def compute(self) -> Tensor: + return self.correct / self.total + + +class PhonemeDurationAccuracy(torchmetrics.Metric): + def __init__(self, *, tolerance, **kwargs): + super().__init__(**kwargs) + self.tolerance = tolerance + self.rr = RhythmRegulator() + self.add_state('accurate', default=torch.tensor(0, dtype=torch.int), dist_reduce_fx='sum') + self.add_state('total', default=torch.tensor(0, dtype=torch.int), dist_reduce_fx='sum') + + def update(self, pdur_pred: Tensor, pdur_target: Tensor, ph2word: Tensor, mask=None) -> None: + """ + + :param pdur_pred: predicted ph_dur + :param pdur_target: reference ph_dur + :param ph2word: word division sequence + :param mask: valid or non-padding mask + """ + linguistic_checks(pdur_pred, pdur_target, ph2word, mask=mask) + + shape = pdur_pred.shape[0], ph2word.max() + 1 + wdur_target = pdur_target.new_zeros(*shape).scatter_add( + 1, ph2word, pdur_target + )[:, 1:] # [B, T_ph] => [B, T_w] + pdur_align = self.rr(pdur_pred, ph2word=ph2word, word_dur=wdur_target) + + accurate = torch.abs(pdur_align - pdur_target) <= pdur_target * self.tolerance + if mask is not None: + accurate &= mask + + self.accurate += accurate.sum() + self.total += pdur_pred.numel() if mask is None else mask.sum() + + def compute(self) -> Tensor: + return self.accurate / self.total diff --git a/modules/nsf_hifigan/env.py b/modules/nsf_hifigan/env.py new file mode 100644 index 0000000..04abfd9 --- /dev/null +++ b/modules/nsf_hifigan/env.py @@ -0,0 +1,32 @@ +class AttrDict(dict): + """A dictionary with attribute-style access. It maps attribute access to + the real dictionary. """ + def __init__(self, *args, **kwargs): + dict.__init__(self, *args, **kwargs) + + def __getstate__(self): + return self.__dict__.items() + + def __setstate__(self, items): + for key, val in items: + self.__dict__[key] = val + + def __repr__(self): + return "%s(%s)" % (self.__class__.__name__, dict.__repr__(self)) + + def __setitem__(self, key, value): + return super(AttrDict, self).__setitem__(key, value) + + def __getitem__(self, name): + if name not in super(AttrDict, self).keys(): + return None + return super(AttrDict, self).__getitem__(name) + + def __delitem__(self, name): + return super(AttrDict, self).__delitem__(name) + + __getattr__ = __getitem__ + __setattr__ = __setitem__ + + def copy(self): + return AttrDict(self) diff --git a/modules/nsf_hifigan/models.py b/modules/nsf_hifigan/models.py new file mode 100644 index 0000000..0849498 --- /dev/null +++ b/modules/nsf_hifigan/models.py @@ -0,0 +1,303 @@ +import json +import pathlib + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from lightning.pytorch.utilities.rank_zero import rank_zero_info +from torch.nn import Conv1d, ConvTranspose1d +from torch.nn.utils import weight_norm, remove_weight_norm + +from .env import AttrDict +from .utils import init_weights, get_padding + +LRELU_SLOPE = 0.1 + + +def load_model(model_path: pathlib.Path): + config_file = model_path.with_name('config.json') + with open(config_file) as f: + data = f.read() + + json_config = json.loads(data) + h = AttrDict(json_config) + + generator = Generator(h) + + cp_dict = torch.load(model_path, map_location='cpu') + generator.load_state_dict(cp_dict['generator']) + generator.eval() + generator.remove_weight_norm() + del cp_dict + return generator, h + + +class ResBlock1(torch.nn.Module): + def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)): + super(ResBlock1, self).__init__() + self.h = h + self.convs1 = nn.ModuleList([ + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], + padding=get_padding(kernel_size, dilation[2]))) + ]) + self.convs1.apply(init_weights) + + self.convs2 = nn.ModuleList([ + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, + padding=get_padding(kernel_size, 1))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, + padding=get_padding(kernel_size, 1))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, + padding=get_padding(kernel_size, 1))) + ]) + self.convs2.apply(init_weights) + + def forward(self, x): + for c1, c2 in zip(self.convs1, self.convs2): + xt = F.leaky_relu(x, LRELU_SLOPE) + xt = c1(xt) + xt = F.leaky_relu(xt, LRELU_SLOPE) + xt = c2(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for l in self.convs1: + remove_weight_norm(l) + for l in self.convs2: + remove_weight_norm(l) + + +class ResBlock2(torch.nn.Module): + def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)): + super(ResBlock2, self).__init__() + self.h = h + self.convs = nn.ModuleList([ + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]))), + weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]))) + ]) + self.convs.apply(init_weights) + + def forward(self, x): + for c in self.convs: + xt = F.leaky_relu(x, LRELU_SLOPE) + xt = c(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for l in self.convs: + remove_weight_norm(l) + + +class SineGen(torch.nn.Module): + """ Definition of sine generator + SineGen(samp_rate, harmonic_num = 0, + sine_amp = 0.1, noise_std = 0.003, + voiced_threshold = 0, + flag_for_pulse=False) + samp_rate: sampling rate in Hz + harmonic_num: number of harmonic overtones (default 0) + sine_amp: amplitude of sine-waveform (default 0.1) + noise_std: std of Gaussian noise (default 0.003) + voiced_threshold: F0 threshold for U/V classification (default 0) + flag_for_pulse: this SinGen is used inside PulseGen (default False) + Note: when flag_for_pulse is True, the first time step of a voiced + segment is always sin(np.pi) or cos(0) + """ + + def __init__(self, samp_rate, harmonic_num=0, + sine_amp=0.1, noise_std=0.003, + voiced_threshold=0): + super(SineGen, self).__init__() + self.sine_amp = sine_amp + self.noise_std = noise_std + self.harmonic_num = harmonic_num + self.dim = self.harmonic_num + 1 + self.sampling_rate = samp_rate + self.voiced_threshold = voiced_threshold + + def _f02uv(self, f0): + # generate uv signal + uv = torch.ones_like(f0) + uv = uv * (f0 > self.voiced_threshold) + return uv + + def _f02sine(self, f0, upp): + """ f0: (batchsize, length, dim) + where dim indicates fundamental tone and overtones + """ + rad = f0 / self.sampling_rate * torch.arange(1, upp + 1, device=f0.device) + rad2 = torch.fmod(rad[..., -1:].float() + 0.5, 1.0) - 0.5 + rad_acc = rad2.cumsum(dim=1).fmod(1.0).to(f0) + rad += F.pad(rad_acc[:, :-1, :], (0, 0, 1, 0)) + rad = rad.reshape(f0.shape[0], -1, 1) + rad = torch.multiply(rad, torch.arange(1, self.dim + 1, device=f0.device).reshape(1, 1, -1)) + rand_ini = torch.rand(1, 1, self.dim, device=f0.device) + rand_ini[..., 0] = 0 + rad += rand_ini + sines = torch.sin(2 * np.pi * rad) + return sines + + @torch.no_grad() + def forward(self, f0, upp): + """ sine_tensor, uv = forward(f0) + input F0: tensor(batchsize=1, length, dim=1) + f0 for unvoiced steps should be 0 + output sine_tensor: tensor(batchsize=1, length, dim) + output uv: tensor(batchsize=1, length, 1) + """ + f0 = f0.unsqueeze(-1) + sine_waves = self._f02sine(f0, upp) * self.sine_amp + uv = (f0 > self.voiced_threshold).float() + uv = F.interpolate(uv.transpose(2, 1), scale_factor=upp, mode='nearest').transpose(2, 1) + noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3 + noise = noise_amp * torch.randn_like(sine_waves) + sine_waves = sine_waves * uv + noise + return sine_waves + + +class SourceModuleHnNSF(torch.nn.Module): + """ SourceModule for hn-nsf + SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1, + add_noise_std=0.003, voiced_threshod=0) + sampling_rate: sampling_rate in Hz + harmonic_num: number of harmonic above F0 (default: 0) + sine_amp: amplitude of sine source signal (default: 0.1) + add_noise_std: std of additive Gaussian noise (default: 0.003) + note that amplitude of noise in unvoiced is decided + by sine_amp + voiced_threshold: threhold to set U/V given F0 (default: 0) + Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) + F0_sampled (batchsize, length, 1) + Sine_source (batchsize, length, 1) + noise_source (batchsize, length 1) + uv (batchsize, length, 1) + """ + + def __init__(self, sampling_rate, harmonic_num=0, sine_amp=0.1, + add_noise_std=0.003, voiced_threshold=0): + super(SourceModuleHnNSF, self).__init__() + + self.sine_amp = sine_amp + self.noise_std = add_noise_std + + # to produce sine waveforms + self.l_sin_gen = SineGen(sampling_rate, harmonic_num, + sine_amp, add_noise_std, voiced_threshold) + + # to merge source harmonics into a single excitation + self.l_linear = torch.nn.Linear(harmonic_num + 1, 1) + self.l_tanh = torch.nn.Tanh() + + def forward(self, x, upp): + sine_wavs = self.l_sin_gen(x, upp) + sine_merge = self.l_tanh(self.l_linear(sine_wavs)) + return sine_merge + + +class Generator(torch.nn.Module): + def __init__(self, h): + super(Generator, self).__init__() + self.h = h + self.num_kernels = len(h.resblock_kernel_sizes) + self.num_upsamples = len(h.upsample_rates) + self.mini_nsf = h.mini_nsf + self.noise_sigma = h.noise_sigma + + if h.mini_nsf: + self.source_sr = h.sampling_rate / int(np.prod(h.upsample_rates[2: ])) + self.upp = int(np.prod(h.upsample_rates[: 2])) + else: + self.source_sr = h.sampling_rate + self.upp = int(np.prod(h.upsample_rates)) + self.m_source = SourceModuleHnNSF( + sampling_rate=h.sampling_rate, + harmonic_num=8 + ) + self.noise_convs = nn.ModuleList() + + self.conv_pre = weight_norm(Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3)) + + self.ups = nn.ModuleList() + self.resblocks = nn.ModuleList() + resblock = ResBlock1 if h.resblock == '1' else ResBlock2 + ch = h.upsample_initial_channel + for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)): + ch //= 2 + self.ups.append(weight_norm(ConvTranspose1d(2 * ch, ch, k, u, padding=(k - u) // 2))) + for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)): + self.resblocks.append(resblock(h, ch, k, d)) + if not h.mini_nsf: + if i + 1 < len(h.upsample_rates): # + stride_f0 = int(np.prod(h.upsample_rates[i + 1:])) + self.noise_convs.append(Conv1d( + 1, ch, kernel_size=stride_f0 * 2, stride=stride_f0, padding=stride_f0 // 2)) + else: + self.noise_convs.append(Conv1d(1, ch, kernel_size=1)) + elif i == 1: + self.source_conv = Conv1d(1, ch, 1) + self.source_conv.apply(init_weights) + + self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) + + self.ups.apply(init_weights) + self.conv_post.apply(init_weights) + + def fastsinegen(self, f0): + n = torch.arange(1, self.upp + 1, device=f0.device) + s0 = f0.unsqueeze(-1) / self.source_sr + ds0 = F.pad(s0[:, 1:, :] - s0[:, :-1, :], (0, 0, 0, 1)) + rad = s0 * n + 0.5 * ds0 * n * (n - 1) / self.upp + rad2 = torch.fmod(rad[..., -1:].float() + 0.5, 1.0) - 0.5 + rad_acc = rad2.cumsum(dim=1).fmod(1.0).to(f0) + rad += F.pad(rad_acc[:, :-1, :], (0, 0, 1, 0)) + rad = rad.reshape(f0.shape[0], 1, -1) + sines = torch.sin(2 * np.pi * rad) + return sines + + def forward(self, x, f0): + if self.mini_nsf: + har_source = self.fastsinegen(f0) + else: + har_source = self.m_source(f0, self.upp).transpose(1, 2) + x = self.conv_pre(x) + if self.noise_sigma is not None and self.noise_sigma > 0: + x += self.noise_sigma * torch.randn_like(x) + for i in range(self.num_upsamples): + x = F.leaky_relu(x, LRELU_SLOPE) + x = self.ups[i](x) + if not self.mini_nsf: + x_source = self.noise_convs[i](har_source) + x = x + x_source + elif i == 1: + x_source = self.source_conv(har_source) + x = x + x_source + xs = None + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i * self.num_kernels + j](x) + else: + xs += self.resblocks[i * self.num_kernels + j](x) + x = xs / self.num_kernels + x = F.leaky_relu(x) + x = self.conv_post(x) + x = torch.tanh(x) + return x + + def remove_weight_norm(self): + # rank_zero_info('Removing weight norm...') + print('Removing weight norm...') + for l in self.ups: + remove_weight_norm(l) + for l in self.resblocks: + l.remove_weight_norm() + remove_weight_norm(self.conv_pre) + remove_weight_norm(self.conv_post) \ No newline at end of file diff --git a/modules/nsf_hifigan/nvSTFT.py b/modules/nsf_hifigan/nvSTFT.py new file mode 100644 index 0000000..d65abb4 --- /dev/null +++ b/modules/nsf_hifigan/nvSTFT.py @@ -0,0 +1,87 @@ +import os + +os.environ["LRU_CACHE_CAPACITY"] = "3" +import torch +import torch.utils.data +import numpy as np +from librosa.filters import mel as librosa_mel_fn +import torch.nn.functional as F + + +def dynamic_range_compression(x, C=1, clip_val=1e-5): + return np.log(np.clip(x, a_min=clip_val, a_max=None) * C) + + +def dynamic_range_decompression(x, C=1): + return np.exp(x) / C + + +def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): + return torch.log(torch.clamp(x, min=clip_val) * C) + + +def dynamic_range_decompression_torch(x, C=1): + return torch.exp(x) / C + + +class STFT: + def __init__( + self, sr=22050, + n_mels=80, n_fft=1024, win_size=1024, hop_length=256, + fmin=20, fmax=11025, clip_val=1e-5, + device=None + ): + self.target_sr = sr + + self.n_mels = n_mels + self.n_fft = n_fft + self.win_size = win_size + self.hop_length = hop_length + self.fmin = fmin + self.fmax = fmax + self.clip_val = clip_val + + if device is None: + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + self.device = device + + mel_basis = librosa_mel_fn(sr=sr, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax) + self.mel_basis = torch.from_numpy(mel_basis).float().to(device) + + def get_mel(self, y, keyshift=0, speed=1, center=False): + + factor = 2 ** (keyshift / 12) + n_fft_new = int(np.round(self.n_fft * factor)) + win_size_new = int(np.round(self.win_size * factor)) + hop_length_new = int(np.round(self.hop_length * speed)) + + if torch.min(y) < -1.: + print('min value is ', torch.min(y)) + if torch.max(y) > 1.: + print('max value is ', torch.max(y)) + + window = torch.hann_window(win_size_new, device=self.device) + + y = torch.nn.functional.pad(y.unsqueeze(1), ( + (win_size_new - hop_length_new) // 2, + (win_size_new - hop_length_new + 1) // 2 + ), mode='reflect') + y = y.squeeze(1) + + spec = torch.stft( + y, n_fft_new, hop_length=hop_length_new, + win_length=win_size_new, window=window, + center=center, pad_mode='reflect', + normalized=False, onesided=True, return_complex=True + ).abs() + if keyshift != 0: + size = self.n_fft // 2 + 1 + resize = spec.size(1) + if resize < size: + spec = F.pad(spec, (0, 0, 0, size - resize)) + spec = spec[:, :size, :] * self.win_size / win_size_new + + spec = torch.matmul(self.mel_basis, spec) + spec = dynamic_range_compression_torch(spec, clip_val=self.clip_val) + + return spec diff --git a/modules/nsf_hifigan/utils.py b/modules/nsf_hifigan/utils.py new file mode 100644 index 0000000..ea12791 --- /dev/null +++ b/modules/nsf_hifigan/utils.py @@ -0,0 +1,13 @@ +import matplotlib + +matplotlib.use("Agg") + + +def init_weights(m, mean=0.0, std=0.01): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + m.weight.data.normal_(mean, std) + + +def get_padding(kernel_size, dilation=1): + return int((kernel_size*dilation - dilation)/2) diff --git a/modules/optimizer/chained_optimizer.py b/modules/optimizer/chained_optimizer.py new file mode 100644 index 0000000..816280c --- /dev/null +++ b/modules/optimizer/chained_optimizer.py @@ -0,0 +1,132 @@ +import torch +from torch import Tensor +from torch.optim import Optimizer +from torch.optim.optimizer import ParamsT +from dataclasses import dataclass +from typing import Any, Dict, List, Type, Callable, Optional + + +@dataclass +class OptimizerSpec: + """Spec for creating an optimizer that is part of a `ChainedOptimizer`.""" + + class_type: Type[Optimizer] + init_args: Dict[str, Any] + param_filter: Optional[Callable[[Tensor], bool]] + + +class ChainedOptimizer(Optimizer): + """ + A wrapper around multiple optimizers that allows for chaining them together. + The optimizers are applied in the order they are passed in the constructor. + Each optimizer is responsible for updating a subset of the parameters, which + is determined by the `param_filter` function. If no optimizer is found for a + parameter group, an exception is raised. + """ + + def __init__( + self, + params: ParamsT, + optimizer_specs: List[OptimizerSpec], + lr: float, + weight_decay: float = 0.0, + optimizer_selection_callback: Optional[Callable[[Tensor, int], None]] = None, + **common_kwargs, + ): + self.optimizer_specs = optimizer_specs + self.optimizer_selection_callback = optimizer_selection_callback + self.optimizers: List[Optimizer] = [] + defaults = dict(lr=lr, weight_decay=weight_decay) + super().__init__(params, defaults) + + # Split the params for each optimizer + params_for_optimizers = [[] for _ in optimizer_specs] + for param_group in self.param_groups: + params = param_group["params"] + indices = param_group["optimizer_and_param_group_indices"] = set() + for param in params: + assert isinstance(param, Tensor), f"Expected a Tensor, got {type(param)}" + found_optimizer = False + for index, spec in enumerate(optimizer_specs): + if spec.param_filter is None or spec.param_filter(param): + if self.optimizer_selection_callback is not None: + self.optimizer_selection_callback(param, index) + params_for_optimizers[index].append(param) + indices.add((index, 0)) + found_optimizer = True + break + if not found_optimizer: + raise ValueError("No valid optimizer found for the given parameter") + + # Initialize the optimizers + for spec, selected_params in zip(optimizer_specs, params_for_optimizers): + optimizer_args = { + 'lr': lr, + 'weight_decay': weight_decay, + } + optimizer_args.update(common_kwargs) + optimizer_args.update(spec.init_args) + optimizer = spec.class_type(selected_params, **optimizer_args) + self.optimizers.append(optimizer) + + def state_dict(self) -> Dict[str, Any]: + return { + "optimizers": [opt.state_dict() for opt in self.optimizers], + **super().state_dict(), + } + + def load_state_dict(self, state_dict: Dict[str, Any]) -> None: + optimizers = state_dict.pop("optimizers") + super().load_state_dict(state_dict) + for i in range(len(self.optimizers)): + self.optimizers[i].load_state_dict(optimizers[i]) + + def zero_grad(self, set_to_none: bool = True) -> None: + for opt in self.optimizers: + opt.zero_grad(set_to_none=set_to_none) + + def _copy_lr_to_optimizers(self) -> None: + for param_group in self.param_groups: + indices = param_group["optimizer_and_param_group_indices"] + for optimizer_idx, param_group_idx in indices: + self.optimizers[optimizer_idx].param_groups[param_group_idx]["lr"] = param_group["lr"] + + def step(self, closure=None) -> None: + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + self._copy_lr_to_optimizers() + for opt in self.optimizers: + opt.step(closure=None) + return loss + + def add_param_group(self, param_group: Dict[str, Any]) -> None: + super().add_param_group(param_group) + + # If optimizer has not been initialized, skip adding the param groups + if not self.optimizers: + return + + # Split the params for each optimizer + params_for_optimizers = [[] for _ in self.optimizer_specs] + params = param_group["params"] + indices = param_group["optimizer_and_param_group_indices"] = set() + for param in params: + assert isinstance(param, Tensor), f"Expected a Tensor, got {type(param)}" + found_optimizer = False + for index, spec in enumerate(self.optimizer_specs): + if spec.param_filter is None or spec.param_filter(param): + if self.optimizer_selection_callback is not None: + self.optimizer_selection_callback(param, index) + params_for_optimizers[index].append(param) + indices.add((index, len(self.optimizers[index].param_groups))) + found_optimizer = True + break + if not found_optimizer: + raise ValueError("No valid optimizer found for the given parameter group") + + # Add the selected param group to the optimizers + for optimizer, selected_params in zip(self.optimizers, params_for_optimizers): + if selected_params: + optimizer.add_param_group({"params": selected_params}) diff --git a/modules/optimizer/muon.py b/modules/optimizer/muon.py new file mode 100644 index 0000000..8d6eec7 --- /dev/null +++ b/modules/optimizer/muon.py @@ -0,0 +1,200 @@ +import collections +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import Tensor +from torch.nn import Parameter +from typing import List +from .chained_optimizer import ChainedOptimizer, OptimizerSpec + +from modules.commons.common_layers import AdamWLinear, AdamWConv1d + + +def zeropower_via_newtonschulz5(G: Tensor, steps: int) -> Tensor: + """ + Newton-Schulz iteration to compute the zeroth power / orthogonalization of G. We opt to use a + quintic iteration whose coefficients are selected to maximize the slope at zero. For the purpose + of minimizing steps, it turns out to be empirically effective to keep increasing the slope at + zero even beyond the point where the iteration no longer converges all the way to one everywhere + on the interval. This iteration therefore does not produce UV^T but rather something like US'V^T + where S' is diagonal with S_{ii}' ~ Uniform(0.5, 1.5), which turns out not to hurt model + performance at all relative to UV^T, where USV^T = G is the SVD. + """ + assert G.ndim == 3 # batched Muon implementation by @scottjmaddox, and put into practice in the record by @YouJiacheng + a, b, c = (3.4445, -4.7750, 2.0315) + + X = G.to(torch.float32) + + # Ensure spectral norm is at most 1 + X = F.normalize(X, p=2.0, dim=(-2, -1), eps=1e-7) + + X = X.to(torch.float16) + + # Perform the NS iterations + if X.size(-2) < X.size(-1): + for _ in range(steps): + A = torch.bmm(X, X.mT) + A = torch.baddbmm(A, A, A, beta=b, alpha=c) + X = torch.baddbmm(X, A, X, beta=a, alpha=1) + else: + for _ in range(steps): + A = torch.bmm(X.mT, X) + A = torch.baddbmm(A, A, A, beta=b, alpha=c) + X = torch.baddbmm(X, X, A, beta=a, alpha=1) + + return X + + +def gram_newton_schulz(G: Tensor, steps: int) -> Tensor: + """ + Refer to: + Gram Newton-Schulz: A Fast, Hardware-Aware Newton-Schulz Algorithm for Muon + Authors: Jack Zhang, Noah Amsel, Berlin Chen, Tri Dao + Blogpost: https://dao-ailab.github.io/blog/2026/gram-newton-schulz/ + + Gram Newton-Schulz iteration to compute the orthogonalization of G. + Mathematically identical to standard Newton-Schulz but computes iterating + on the smaller NxN Gram matrix to save up to 50% FLOPs. + """ + assert G.ndim == 3 + reset_iterations = [2] + original_shape = G.shape + dtype = G.dtype + + X = G.to(torch.float32) + X = F.normalize(X, p=2.0, dim=(-2, -1), eps=1e-7) + should_transpose = X.size(-2) > X.size(-1) + if should_transpose: + X = X.mT + X = X.to(torch.float16) + + a, b, c = (3.4445, -4.7750, 2.0315) + + if X.size(-2) != X.size(-1): + R = torch.bmm(X, X.mT) + Q = None + for i in range(steps): + if i in reset_iterations and i != 0: + X = torch.bmm(Q, X) + R = torch.bmm(X, X.mT) + Q = None + Z = torch.baddbmm(R, R, R, beta=b, alpha=c) + if i != 0 and i not in reset_iterations: + Q = torch.baddbmm(Q, Q, Z, beta=a, alpha=1.0) + else: + Q = Z.clone() + Q.diagonal(dim1=-2, dim2=-1).add_(a) + if i < steps - 1 and (i + 1) not in reset_iterations: + RZ = torch.baddbmm(R, R, Z, beta=a, alpha=1.0) + R = torch.baddbmm(RZ, Z, RZ, beta=a, alpha=1.0) + X = torch.bmm(Q, X) if not should_transpose else torch.bmm(X.mT, Q) + else: + for _ in range(steps): + A = torch.bmm(X, X.mT) + B = torch.baddbmm(A, A, A, beta=b, alpha=c) + X = torch.baddbmm(X, B, X, beta=a, alpha=1.0) + + return X.to(dtype).view(original_shape) + + +class Muon(torch.optim.Optimizer): + """ + Muon - MomentUm Orthogonalized by Newton-schulz + + https://kellerjordan.github.io/posts/muon/ + + Muon internally runs standard SGD-momentum, and then performs an orthogonalization post- + processing step, in which each 2D parameter's update is replaced with the nearest orthogonal + matrix. To efficiently orthogonalize each update, we use a Newton-Schulz iteration, which has + the advantage that it can be stably run in float16 on the GPU. + + Some warnings: + - This optimizer should not be used for the embedding layer, the final fully connected layer, + or any {0,1}-D parameters; those should all be optimized by a standard method (e.g., AdamW). + - To use it with 4D convolutional filters, it works well to just flatten their last 3 dimensions. + + Arguments: + lr: The learning rate used by the internal SGD. + momentum: The momentum used by the internal SGD. + nesterov: Whether to use Nesterov-style momentum in the internal SGD. (recommended) + ns_steps: The number of Newton-Schulz iteration steps to use. + """ + + def __init__(self, params, lr=5e-4, weight_decay=0.1, momentum=0.95, nesterov=True, ns_steps=5): + defaults = dict(lr=lr, weight_decay=weight_decay, momentum=momentum, nesterov=nesterov, ns_steps=ns_steps) + super().__init__(params, defaults) + + @torch.no_grad() + def step(self, closure=None): + for group in self.param_groups: + shape_groups = {} + for p in filter(lambda p: p.grad is not None, group["params"]): + g = p.grad + state = self.state[p] + if "momentum_buffer" not in state: + state["momentum_buffer"] = torch.zeros_like(g) + key = (p.shape, p.device, p.dtype) + if key not in shape_groups: + shape_groups[key] = {"params": [], "grads": [], "buffers": []} + shape_groups[key]["params"].append(p) + shape_groups[key]["grads"].append(g) + shape_groups[key]["buffers"].append(state["momentum_buffer"]) + for key in shape_groups: + group_data = shape_groups[key] + p, g, buf, m = group_data["params"], group_data["grads"], group_data["buffers"], group["momentum"] + torch._foreach_lerp_(buf, g, 1-m) + if group["nesterov"]: + torch._foreach_lerp_(g, buf, m) + g = torch.stack(g) + else: + g = torch.stack(buf) + original_shape = g.shape + if g.ndim >= 4: # for the case of conv filters + g = g.view(g.size(0), g.size(1), -1) + g = gram_newton_schulz(g, steps=group["ns_steps"]) + + if group["weight_decay"] > 0: + torch._foreach_mul_(p, 1 - group["lr"] * group["weight_decay"]) + torch._foreach_add_(p, g.view(original_shape).unbind(0), alpha=-group["lr"] * max(g[0].size()) ** 0.5) + + +def get_params_for_muon(model) -> List[Parameter]: + """ + Filter parameters of a module into two groups: those that can be optimized by Muon, + and those that should be optimized by a standard optimizer. + Args: + module: The module to filter parameters for. + Returns: + A list of parameters that should be optimized with muon. + """ + excluded_module_classes = (nn.Embedding, AdamWLinear, AdamWConv1d) + muon_params = [] + # BFS through all submodules and exclude parameters from certain module types + queue = collections.deque([model]) + while queue: + module = queue.popleft() + if isinstance(module, excluded_module_classes): + continue + for param in module.parameters(recurse=False): + if not param.requires_grad: + continue + if param.ndim >= 2: + muon_params.append(param) + queue.extend(list(module.children())) + return muon_params + + +class Muon_AdamW(ChainedOptimizer): + def __init__(self, model, lr=0.0005, weight_decay=0.0, muon_args=None, adamw_args=None, verbose=False): + muon_args = {} if muon_args is None else muon_args + adamw_args = {} if adamw_args is None else adamw_args + muon_params_id_set = set(id(p) for p in get_params_for_muon(model)) + spec_muon = OptimizerSpec(Muon, muon_args, lambda param: id(param) in muon_params_id_set) + spec_adamw = OptimizerSpec(torch.optim.AdamW, adamw_args, None) + specs = [spec_muon, spec_adamw] + callback = None + if verbose: + callback = lambda p, spec_idx: print( + f"Adding param {p.shape} to optimizer{spec_idx} {str(specs[spec_idx].class_type)}" + ) + super().__init__(model.parameters(), specs, lr=lr, weight_decay=weight_decay, optimizer_selection_callback=callback) diff --git a/modules/pe/__init__.py b/modules/pe/__init__.py new file mode 100644 index 0000000..edf747a --- /dev/null +++ b/modules/pe/__init__.py @@ -0,0 +1,18 @@ +from utils import hparams + +from .pm import ParselmouthPE +from .pw import HarvestPE +from .rmvpe import RMVPE + + +def initialize_pe(): + pe = hparams['pe'] + pe_ckpt = hparams['pe_ckpt'] + if pe == 'parselmouth': + return ParselmouthPE() + elif pe == 'rmvpe': + return RMVPE(pe_ckpt) + elif pe == 'harvest': + return HarvestPE() + else: + raise ValueError(f" [x] Unknown f0 extractor: {pe}") diff --git a/modules/pe/pm.py b/modules/pe/pm.py new file mode 100644 index 0000000..206d646 --- /dev/null +++ b/modules/pe/pm.py @@ -0,0 +1,15 @@ +from basics.base_pe import BasePE +from utils.binarizer_utils import get_pitch_parselmouth + + +class ParselmouthPE(BasePE): + def get_pitch( + self,waveform, samplerate, length, + *, hop_size, f0_min=65, f0_max=1100, + speed=1, interp_uv=False + ): + return get_pitch_parselmouth( + waveform, samplerate=samplerate, length=length, + hop_size=hop_size, f0_min=f0_min, f0_max=f0_max, + speed=speed, interp_uv=interp_uv + ) diff --git a/modules/pe/pw.py b/modules/pe/pw.py new file mode 100644 index 0000000..6fe629f --- /dev/null +++ b/modules/pe/pw.py @@ -0,0 +1,29 @@ +from basics.base_pe import BasePE +import numpy as np +import pyworld as pw +from utils.pitch_utils import interp_f0 + + +class HarvestPE(BasePE): + def get_pitch( + self, waveform, samplerate, length, + *, hop_size, f0_min=65, f0_max=1100, + speed=1, interp_uv=False + ): + hop_size = int(np.round(hop_size * speed)) + time_step = 1000 * hop_size / samplerate + + f0, _ = pw.harvest( + waveform.astype(np.float64), samplerate, + f0_floor=f0_min, f0_ceil=f0_max, frame_period=time_step + ) + f0 = f0.astype(np.float32) + + if f0.size < length: + f0 = np.pad(f0, (0, length - f0.size)) + f0 = f0[:length] + uv = f0 == 0 + + if interp_uv: + f0, uv = interp_f0(f0, uv) + return f0, uv diff --git a/modules/pe/rmvpe/__init__.py b/modules/pe/rmvpe/__init__.py new file mode 100644 index 0000000..cf71a05 --- /dev/null +++ b/modules/pe/rmvpe/__init__.py @@ -0,0 +1,5 @@ +from .constants import * +from .model import E2E0 +from .utils import to_local_average_f0, to_viterbi_f0 +from .inference import RMVPE +from .spec import MelSpectrogram diff --git a/modules/pe/rmvpe/constants.py b/modules/pe/rmvpe/constants.py new file mode 100644 index 0000000..525a2a0 --- /dev/null +++ b/modules/pe/rmvpe/constants.py @@ -0,0 +1,9 @@ +SAMPLE_RATE = 16000 + +N_CLASS = 360 + +N_MELS = 128 +MEL_FMIN = 30 +MEL_FMAX = 8000 +WINDOW_LENGTH = 1024 +CONST = 1997.3794084376191 diff --git a/modules/pe/rmvpe/deepunet.py b/modules/pe/rmvpe/deepunet.py new file mode 100644 index 0000000..2e50d5e --- /dev/null +++ b/modules/pe/rmvpe/deepunet.py @@ -0,0 +1,173 @@ +import torch +import torch.nn as nn +from .constants import N_MELS + + +class ConvBlockRes(nn.Module): + def __init__(self, in_channels, out_channels, momentum=0.01): + super(ConvBlockRes, self).__init__() + self.conv = nn.Sequential( + nn.Conv2d(in_channels=in_channels, + out_channels=out_channels, + kernel_size=(3, 3), + stride=(1, 1), + padding=(1, 1), + bias=False), + nn.BatchNorm2d(out_channels, momentum=momentum), + nn.ReLU(), + + nn.Conv2d(in_channels=out_channels, + out_channels=out_channels, + kernel_size=(3, 3), + stride=(1, 1), + padding=(1, 1), + bias=False), + nn.BatchNorm2d(out_channels, momentum=momentum), + nn.ReLU(), + ) + if in_channels != out_channels: + self.shortcut = nn.Conv2d(in_channels, out_channels, (1, 1)) + self.is_shortcut = True + else: + self.is_shortcut = False + + def forward(self, x): + if self.is_shortcut: + return self.conv(x) + self.shortcut(x) + else: + return self.conv(x) + x + + +class ResEncoderBlock(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size, n_blocks=1, momentum=0.01): + super(ResEncoderBlock, self).__init__() + self.n_blocks = n_blocks + self.conv = nn.ModuleList() + self.conv.append(ConvBlockRes(in_channels, out_channels, momentum)) + for i in range(n_blocks - 1): + self.conv.append(ConvBlockRes(out_channels, out_channels, momentum)) + self.kernel_size = kernel_size + if self.kernel_size is not None: + self.pool = nn.AvgPool2d(kernel_size=kernel_size) + + def forward(self, x): + for i in range(self.n_blocks): + x = self.conv[i](x) + if self.kernel_size is not None: + return x, self.pool(x) + else: + return x + + +class ResDecoderBlock(nn.Module): + def __init__(self, in_channels, out_channels, stride, n_blocks=1, momentum=0.01): + super(ResDecoderBlock, self).__init__() + out_padding = (0, 1) if stride == (1, 2) else (1, 1) + self.n_blocks = n_blocks + self.conv1 = nn.Sequential( + nn.ConvTranspose2d(in_channels=in_channels, + out_channels=out_channels, + kernel_size=(3, 3), + stride=stride, + padding=(1, 1), + output_padding=out_padding, + bias=False), + nn.BatchNorm2d(out_channels, momentum=momentum), + nn.ReLU(), + ) + self.conv2 = nn.ModuleList() + self.conv2.append(ConvBlockRes(out_channels * 2, out_channels, momentum)) + for i in range(n_blocks-1): + self.conv2.append(ConvBlockRes(out_channels, out_channels, momentum)) + + def forward(self, x, concat_tensor): + x = self.conv1(x) + x = torch.cat((x, concat_tensor), dim=1) + for i in range(self.n_blocks): + x = self.conv2[i](x) + return x + + +class Encoder(nn.Module): + def __init__(self, in_channels, in_size, n_encoders, kernel_size, n_blocks, out_channels=16, momentum=0.01): + super(Encoder, self).__init__() + self.n_encoders = n_encoders + self.bn = nn.BatchNorm2d(in_channels, momentum=momentum) + self.layers = nn.ModuleList() + self.latent_channels = [] + for i in range(self.n_encoders): + self.layers.append(ResEncoderBlock(in_channels, out_channels, kernel_size, n_blocks, momentum=momentum)) + self.latent_channels.append([out_channels, in_size]) + in_channels = out_channels + out_channels *= 2 + in_size //= 2 + self.out_size = in_size + self.out_channel = out_channels + + def forward(self, x): + concat_tensors = [] + x = self.bn(x) + for i in range(self.n_encoders): + _, x = self.layers[i](x) + concat_tensors.append(_) + return x, concat_tensors + + +class Intermediate(nn.Module): + def __init__(self, in_channels, out_channels, n_inters, n_blocks, momentum=0.01): + super(Intermediate, self).__init__() + self.n_inters = n_inters + self.layers = nn.ModuleList() + self.layers.append(ResEncoderBlock(in_channels, out_channels, None, n_blocks, momentum)) + for i in range(self.n_inters-1): + self.layers.append(ResEncoderBlock(out_channels, out_channels, None, n_blocks, momentum)) + + def forward(self, x): + for i in range(self.n_inters): + x = self.layers[i](x) + return x + + +class Decoder(nn.Module): + def __init__(self, in_channels, n_decoders, stride, n_blocks, momentum=0.01): + super(Decoder, self).__init__() + self.layers = nn.ModuleList() + self.n_decoders = n_decoders + for i in range(self.n_decoders): + out_channels = in_channels // 2 + self.layers.append(ResDecoderBlock(in_channels, out_channels, stride, n_blocks, momentum)) + in_channels = out_channels + + def forward(self, x, concat_tensors): + for i in range(self.n_decoders): + x = self.layers[i](x, concat_tensors[-1-i]) + return x + + +class TimbreFilter(nn.Module): + def __init__(self, latent_rep_channels): + super(TimbreFilter, self).__init__() + self.layers = nn.ModuleList() + for latent_rep in latent_rep_channels: + self.layers.append(ConvBlockRes(latent_rep[0], latent_rep[0])) + + def forward(self, x_tensors): + out_tensors = [] + for i, layer in enumerate(self.layers): + out_tensors.append(layer(x_tensors[i])) + return out_tensors + + +class DeepUnet0(nn.Module): + def __init__(self, kernel_size, n_blocks, en_de_layers=5, inter_layers=4, in_channels=1, en_out_channels=16): + super(DeepUnet0, self).__init__() + self.encoder = Encoder(in_channels, N_MELS, en_de_layers, kernel_size, n_blocks, en_out_channels) + self.intermediate = Intermediate(self.encoder.out_channel // 2, self.encoder.out_channel, inter_layers, n_blocks) + self.tf = TimbreFilter(self.encoder.latent_channels) + self.decoder = Decoder(self.encoder.out_channel, en_de_layers, kernel_size, n_blocks) + + def forward(self, x): + x, concat_tensors = self.encoder(x) + x = self.intermediate(x) + x = self.decoder(x, concat_tensors) + return x diff --git a/modules/pe/rmvpe/inference.py b/modules/pe/rmvpe/inference.py new file mode 100644 index 0000000..f3b8ad5 --- /dev/null +++ b/modules/pe/rmvpe/inference.py @@ -0,0 +1,78 @@ +import numpy as np +import torch +import torch.nn.functional as F +from torchaudio.transforms import Resample + +from basics.base_pe import BasePE +from utils.infer_utils import resample_align_curve +from utils.pitch_utils import interp_f0 +from .constants import * +from .model import E2E0 +from .spec import MelSpectrogram +from .utils import to_local_average_f0, to_viterbi_f0 + + +class RMVPE(BasePE): + def __init__(self, model_path, hop_length=160): + self.resample_kernel = {} + self.device = 'cuda' if torch.cuda.is_available() else 'cpu' + self.model = E2E0(4, 1, (2, 2)).eval().to(self.device) + ckpt = torch.load(model_path, map_location=self.device) + self.model.load_state_dict(ckpt['model'], strict=False) + self.hop_length = hop_length + self.seg_length = 32 * hop_length + self.mel_extractor = MelSpectrogram( + N_MELS, SAMPLE_RATE, WINDOW_LENGTH, hop_length, None, MEL_FMIN, MEL_FMAX + ).to(self.device) + + @torch.no_grad() + def mel2hidden(self, mel): + n_frames = mel.shape[-1] + mel = F.pad(mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode='reflect') + hidden = self.model(mel) + return hidden[:, :n_frames] + + def decode(self, hidden, thred=0.03, use_viterbi=False): + if use_viterbi: + f0 = to_viterbi_f0(hidden, thred=thred) + else: + f0 = to_local_average_f0(hidden, thred=thred) + return f0 + + def infer_from_audio(self, audio, sample_rate=16000, thred=0.03, use_viterbi=False): + audio = torch.from_numpy(audio).float().unsqueeze(0).to(self.device) + if sample_rate == 16000: + audio_res = audio + else: + key_str = str(sample_rate) + if key_str not in self.resample_kernel: + self.resample_kernel[key_str] = Resample(sample_rate, 16000, lowpass_filter_width=128) + self.resample_kernel[key_str] = self.resample_kernel[key_str].to(self.device) + audio_res = self.resample_kernel[key_str](audio) + B, T = audio_res.shape + n_frames = T // self.hop_length + 1 + T1 = T + self.hop_length + T_pad = self.seg_length * ((T1 - 1) // self.seg_length + 1) - T1 + audio_res = F.pad(audio_res, (0, T_pad)) + mel = self.mel_extractor(audio_res, center=True) + with torch.no_grad(): + hidden = self.model(mel) + f0 = self.decode(hidden[:, :n_frames], thred=thred, use_viterbi=use_viterbi) + return f0 + + def get_pitch( + self, waveform, samplerate, length, + *, hop_size, f0_min=65, f0_max=1100, + speed=1, interp_uv=False + ): + f0 = self.infer_from_audio(waveform, sample_rate=samplerate) + uv = f0 == 0 + f0, uv = interp_f0(f0, uv) + + hop_size = int(np.round(hop_size * speed)) + time_step = hop_size / samplerate + f0_res = resample_align_curve(f0, 0.01, time_step, length) + uv_res = resample_align_curve(uv.astype(np.float32), 0.01, time_step, length) > 0.5 + if not interp_uv: + f0_res[uv_res] = 0 + return f0_res, uv_res diff --git a/modules/pe/rmvpe/model.py b/modules/pe/rmvpe/model.py new file mode 100644 index 0000000..5b2d72c --- /dev/null +++ b/modules/pe/rmvpe/model.py @@ -0,0 +1,32 @@ +from torch import nn + +from .constants import * +from .deepunet import DeepUnet0 +from .seq import BiGRU + + +class E2E0(nn.Module): + def __init__(self, n_blocks, n_gru, kernel_size, en_de_layers=5, inter_layers=4, in_channels=1, + en_out_channels=16): + super(E2E0, self).__init__() + self.unet = DeepUnet0(kernel_size, n_blocks, en_de_layers, inter_layers, in_channels, en_out_channels) + self.cnn = nn.Conv2d(en_out_channels, 3, (3, 3), padding=(1, 1)) + if n_gru: + self.fc = nn.Sequential( + BiGRU(3 * N_MELS, 256, n_gru), + nn.Linear(512, N_CLASS), + nn.Dropout(0.25), + nn.Sigmoid() + ) + else: + self.fc = nn.Sequential( + nn.Linear(3 * N_MELS, N_CLASS), + nn.Dropout(0.25), + nn.Sigmoid() + ) + + def forward(self, mel): + mel = mel.transpose(-1, -2).unsqueeze(1) + x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2) + x = self.fc(x) + return x diff --git a/modules/pe/rmvpe/seq.py b/modules/pe/rmvpe/seq.py new file mode 100644 index 0000000..9c4c8f8 --- /dev/null +++ b/modules/pe/rmvpe/seq.py @@ -0,0 +1,10 @@ +import torch.nn as nn + + +class BiGRU(nn.Module): + def __init__(self, input_features, hidden_features, num_layers): + super(BiGRU, self).__init__() + self.gru = nn.GRU(input_features, hidden_features, num_layers=num_layers, batch_first=True, bidirectional=True) + + def forward(self, x): + return self.gru(x)[0] diff --git a/modules/pe/rmvpe/spec.py b/modules/pe/rmvpe/spec.py new file mode 100644 index 0000000..4a38054 --- /dev/null +++ b/modules/pe/rmvpe/spec.py @@ -0,0 +1,68 @@ +import torch +import numpy as np +import torch.nn.functional as F +from librosa.filters import mel + + +class MelSpectrogram(torch.nn.Module): + def __init__( + self, + n_mel_channels, + sampling_rate, + win_length, + hop_length, + n_fft=None, + mel_fmin=0, + mel_fmax=None, + clamp=1e-5 + ): + super().__init__() + n_fft = win_length if n_fft is None else n_fft + self.hann_window = {} + mel_basis = mel( + sr=sampling_rate, + n_fft=n_fft, + n_mels=n_mel_channels, + fmin=mel_fmin, + fmax=mel_fmax, + htk=True) + mel_basis = torch.from_numpy(mel_basis).float() + self.register_buffer("mel_basis", mel_basis) + self.n_fft = win_length if n_fft is None else n_fft + self.hop_length = hop_length + self.win_length = win_length + self.sampling_rate = sampling_rate + self.n_mel_channels = n_mel_channels + self.clamp = clamp + + def forward(self, audio, keyshift=0, speed=1, center=True): + factor = 2 ** (keyshift / 12) + n_fft_new = int(np.round(self.n_fft * factor)) + win_length_new = int(np.round(self.win_length * factor)) + hop_length_new = int(np.round(self.hop_length * speed)) + + keyshift_key = str(keyshift) + '_' + str(audio.device) + if keyshift_key not in self.hann_window: + self.hann_window[keyshift_key] = torch.hann_window(win_length_new).to(audio.device) + + fft = torch.stft( + audio, + n_fft=n_fft_new, + hop_length=hop_length_new, + win_length=win_length_new, + window=self.hann_window[keyshift_key], + center=center, + return_complex=True + ) + magnitude = fft.abs() + + if keyshift != 0: + size = self.n_fft // 2 + 1 + resize = magnitude.size(1) + if resize < size: + magnitude = F.pad(magnitude, (0, 0, 0, size - resize)) + magnitude = magnitude[:, :size, :] * self.win_length / win_length_new + + mel_output = torch.matmul(self.mel_basis, magnitude) + log_mel_spec = torch.log(torch.clamp(mel_output, min=self.clamp)) + return log_mel_spec diff --git a/modules/pe/rmvpe/utils.py b/modules/pe/rmvpe/utils.py new file mode 100644 index 0000000..9cdf0b1 --- /dev/null +++ b/modules/pe/rmvpe/utils.py @@ -0,0 +1,43 @@ +import librosa +import numpy as np +import torch + +from .constants import * + + +def to_local_average_f0(hidden, center=None, thred=0.03): + idx = torch.arange(N_CLASS, device=hidden.device)[None, None, :] # [B=1, T=1, N] + idx_cents = idx * 20 + CONST # [B=1, N] + if center is None: + center = torch.argmax(hidden, dim=2, keepdim=True) # [B, T, 1] + start = torch.clip(center - 4, min=0) # [B, T, 1] + end = torch.clip(center + 5, max=N_CLASS) # [B, T, 1] + idx_mask = (idx >= start) & (idx < end) # [B, T, N] + weights = hidden * idx_mask # [B, T, N] + product_sum = torch.sum(weights * idx_cents, dim=2) # [B, T] + weight_sum = torch.sum(weights, dim=2) # [B, T] + cents = product_sum / (weight_sum + (weight_sum == 0)) # avoid dividing by zero, [B, T] + f0 = 10 * 2 ** (cents / 1200) + uv = hidden.max(dim=2)[0] < thred # [B, T] + f0 = f0 * ~uv + return f0.squeeze(0).cpu().numpy() + + +def to_viterbi_f0(hidden, thred=0.03): + # Create viterbi transition matrix + if not hasattr(to_viterbi_f0, 'transition'): + xx, yy = np.meshgrid(range(N_CLASS), range(N_CLASS)) + transition = np.maximum(30 - abs(xx - yy), 0) + transition = transition / transition.sum(axis=1, keepdims=True) + to_viterbi_f0.transition = transition + + # Convert to probability + prob = hidden.squeeze(0).cpu().numpy() + prob = prob.T + prob = prob / prob.sum(axis=0) + + # Perform viterbi decoding + path = librosa.sequence.viterbi(prob, to_viterbi_f0.transition).astype(np.int64) + center = torch.from_numpy(path).unsqueeze(0).unsqueeze(-1).to(hidden.device) + + return to_local_average_f0(hidden, center=center, thred=thred) diff --git a/modules/toplevel.py b/modules/toplevel.py new file mode 100644 index 0000000..4a97b3c --- /dev/null +++ b/modules/toplevel.py @@ -0,0 +1,366 @@ +from typing import Dict + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import Tensor + +import modules.compat as compat +from basics.base_module import CategorizedModule +from modules.aux_decoder import AuxDecoderAdaptor +from modules.commons.common_layers import ( + NormalInitEmbedding as Embedding, + SinusoidalPosEmb, AdamWLinear, +) +from modules.core import ( + GaussianDiffusion, PitchDiffusion, MultiVarianceDiffusion, + RectifiedFlow, PitchRectifiedFlow, MultiVarianceRectifiedFlow +) +from modules.fastspeech.acoustic_encoder import FastSpeech2Acoustic +from modules.fastspeech.param_adaptor import ParameterAdaptorModule +from modules.fastspeech.tts_modules import RhythmRegulator, LengthRegulator, StretchRegulator +from modules.fastspeech.variance_encoder import FastSpeech2Variance, MelodyEncoder +from utils.hparams import hparams + + +class ShallowDiffusionOutput: + def __init__(self, *, aux_out=None, diff_out=None): + self.aux_out = aux_out + self.diff_out = diff_out + + +class DiffSingerAcoustic(CategorizedModule, ParameterAdaptorModule): + @property + def category(self): + return 'acoustic' + + def __init__(self, vocab_size, out_dims): + CategorizedModule.__init__(self) + ParameterAdaptorModule.__init__(self) + self.fs2 = FastSpeech2Acoustic( + vocab_size=vocab_size + ) + + self.use_shallow_diffusion = hparams.get('use_shallow_diffusion', False) + self.shallow_args = hparams.get('shallow_diffusion_args', {}) + if self.use_shallow_diffusion: + self.train_aux_decoder = self.shallow_args['train_aux_decoder'] + self.train_diffusion = self.shallow_args['train_diffusion'] + self.aux_decoder_grad = self.shallow_args['aux_decoder_grad'] + self.aux_decoder = AuxDecoderAdaptor( + in_dims=hparams['hidden_size'], out_dims=out_dims, num_feats=1, + spec_min=hparams['spec_min'], spec_max=hparams['spec_max'], + aux_decoder_arch=self.shallow_args['aux_decoder_arch'], + aux_decoder_args=self.shallow_args['aux_decoder_args'] + ) + self.diffusion_type = hparams.get('diffusion_type', 'ddpm') + self.backbone_type = compat.get_backbone_type(hparams) + self.backbone_args = compat.get_backbone_args(hparams, self.backbone_type) + if self.diffusion_type == 'ddpm': + self.diffusion = GaussianDiffusion( + out_dims=out_dims, + num_feats=1, + timesteps=hparams['timesteps'], + k_step=hparams['K_step'], + backbone_type=self.backbone_type, + backbone_args=self.backbone_args, + spec_min=hparams['spec_min'], + spec_max=hparams['spec_max'] + ) + elif self.diffusion_type == 'reflow': + self.diffusion = RectifiedFlow( + out_dims=out_dims, + num_feats=1, + t_start=hparams['T_start'], + time_scale_factor=hparams['time_scale_factor'], + backbone_type=self.backbone_type, + backbone_args=self.backbone_args, + spec_min=hparams['spec_min'], + spec_max=hparams['spec_max'] + ) + else: + raise NotImplementedError(self.diffusion_type) + + def forward( + self, txt_tokens, mel2ph, f0, key_shift=None, speed=None, + spk_embed_id=None, languages=None, gt_mel=None, infer=True, **kwargs + ) -> ShallowDiffusionOutput: + condition = self.fs2( + txt_tokens, mel2ph, f0, key_shift=key_shift, speed=speed, + spk_embed_id=spk_embed_id, languages=languages, + **kwargs + ) + if infer: + if self.use_shallow_diffusion: + aux_mel_pred = self.aux_decoder(condition, infer=True) + aux_mel_pred *= ((mel2ph > 0).float()[:, :, None]) + if gt_mel is not None and self.shallow_args['val_gt_start']: + src_mel = gt_mel + else: + src_mel = aux_mel_pred + else: + aux_mel_pred = src_mel = None + mel_pred = self.diffusion(condition, src_spec=src_mel, infer=True) + mel_pred *= ((mel2ph > 0).float()[:, :, None]) + return ShallowDiffusionOutput(aux_out=aux_mel_pred, diff_out=mel_pred) + else: + if self.use_shallow_diffusion: + if self.train_aux_decoder: + aux_cond = condition * self.aux_decoder_grad + condition.detach() * (1 - self.aux_decoder_grad) + aux_out = self.aux_decoder(aux_cond, infer=False) + else: + aux_out = None + if self.train_diffusion: + diff_out = self.diffusion(condition, gt_spec=gt_mel, infer=False) + else: + diff_out = None + return ShallowDiffusionOutput(aux_out=aux_out, diff_out=diff_out) + + else: + aux_out = None + diff_out = self.diffusion(condition, gt_spec=gt_mel, infer=False) + return ShallowDiffusionOutput(aux_out=aux_out, diff_out=diff_out) + + +class DiffSingerVariance(CategorizedModule, ParameterAdaptorModule): + @property + def category(self): + return 'variance' + + def __init__(self, vocab_size): + CategorizedModule.__init__(self) + ParameterAdaptorModule.__init__(self) + self.predict_dur = hparams['predict_dur'] + self.predict_pitch = hparams['predict_pitch'] + + self.use_stretch_embed = hparams.get('use_stretch_embed', None) + assert self.use_stretch_embed is not None, "You may be loading an old version of the model checkpoint, which is incompatible with the new version due to some bug fixes. It is recommended to roll back to the old version (commit id: 6df0ee977c3728f14cb79c2db8b19df30b23a0bf)" + if self.use_stretch_embed and (self.predict_pitch or self.predict_variances): + self.sr = StretchRegulator() + self.stretch_embed = nn.Sequential( + SinusoidalPosEmb(hparams['hidden_size']), + nn.Linear(hparams['hidden_size'], hparams['hidden_size'] * 4), + nn.GELU(), + nn.Linear(hparams['hidden_size'] * 4, hparams['hidden_size']), + ) + self.stretch_embed_rnn = nn.GRU(hparams['hidden_size'], hparams['hidden_size'], 1, batch_first=True) + + self.use_spk_id = hparams['use_spk_id'] + if self.use_spk_id: + self.spk_embed = Embedding(hparams['num_spk'], hparams['hidden_size']) + + self.fs2 = FastSpeech2Variance( + vocab_size=vocab_size + ) + self.rr = RhythmRegulator() + self.lr = LengthRegulator() + self.diffusion_type = hparams.get('diffusion_type', 'ddpm') + if self.predict_pitch: + self.use_melody_encoder = hparams.get('use_melody_encoder', False) + if self.use_melody_encoder: + self.melody_encoder = MelodyEncoder(enc_hparams=hparams['melody_encoder_args']) + self.delta_pitch_embed = AdamWLinear(1, hparams['hidden_size']) + else: + self.base_pitch_embed = AdamWLinear(1, hparams['hidden_size']) + + self.pitch_retake_embed = Embedding(2, hparams['hidden_size']) + pitch_hparams = hparams['pitch_prediction_args'] + self.pitch_backbone_type = compat.get_backbone_type(hparams, nested_config=pitch_hparams) + self.pitch_backbone_args = compat.get_backbone_args(pitch_hparams, backbone_type=self.pitch_backbone_type) + if self.diffusion_type == 'ddpm': + self.pitch_predictor = PitchDiffusion( + vmin=pitch_hparams['pitd_norm_min'], + vmax=pitch_hparams['pitd_norm_max'], + cmin=pitch_hparams['pitd_clip_min'], + cmax=pitch_hparams['pitd_clip_max'], + repeat_bins=pitch_hparams['repeat_bins'], + timesteps=hparams['timesteps'], + k_step=hparams['K_step'], + backbone_type=self.pitch_backbone_type, + backbone_args=self.pitch_backbone_args + ) + elif self.diffusion_type == 'reflow': + self.pitch_predictor = PitchRectifiedFlow( + vmin=pitch_hparams['pitd_norm_min'], + vmax=pitch_hparams['pitd_norm_max'], + cmin=pitch_hparams['pitd_clip_min'], + cmax=pitch_hparams['pitd_clip_max'], + repeat_bins=pitch_hparams['repeat_bins'], + time_scale_factor=hparams['time_scale_factor'], + backbone_type=self.pitch_backbone_type, + backbone_args=self.pitch_backbone_args + ) + else: + raise ValueError(f"Invalid diffusion type: {self.diffusion_type}") + + if self.predict_variances: + self.pitch_embed = AdamWLinear(1, hparams['hidden_size']) + self.variance_embeds = nn.ModuleDict({ + v_name: AdamWLinear(1, hparams['hidden_size']) + for v_name in self.variance_prediction_list + }) + + if self.diffusion_type == 'ddpm': + self.variance_predictor = self.build_adaptor(cls=MultiVarianceDiffusion) + elif self.diffusion_type == 'reflow': + self.variance_predictor = self.build_adaptor(cls=MultiVarianceRectifiedFlow) + else: + raise NotImplementedError(self.diffusion_type) + + self.use_variance_scaling = hparams.get('use_variance_scaling', False) + self.custom_variance_scaling_factor = { + 'energy': 1. / 96, + 'breathiness': 1. / 96, + 'voicing': 1. / 96, + 'tension': 0.1, + 'key_shift': 1. / 12, + 'speed': 1. + } + self.default_variance_scaling_factor = { + 'energy': 1., + 'breathiness': 1., + 'voicing': 1., + 'tension': 1., + 'key_shift': 1., + 'speed': 1. + } + if self.use_variance_scaling: + self.variance_retake_scaling = self.custom_variance_scaling_factor + else: + self.variance_retake_scaling = self.default_variance_scaling_factor + + def forward( + self, txt_tokens, midi, ph2word, ph_dur=None, word_dur=None, mel2ph=None, + note_midi=None, note_rest=None, note_dur=None, note_glide=None, mel2note=None, + base_pitch=None, pitch=None, pitch_expr=None, pitch_retake=None, + variance_retake: Dict[str, Tensor] = None, + spk_id=None, languages=None, + infer=True, **kwargs + ): + if self.use_spk_id: + ph_spk_mix_embed = kwargs.get('ph_spk_mix_embed') + spk_mix_embed = kwargs.get('spk_mix_embed') + if ph_spk_mix_embed is not None and spk_mix_embed is not None: + ph_spk_embed = ph_spk_mix_embed + spk_embed = spk_mix_embed + else: + ph_spk_embed = spk_embed = self.spk_embed(spk_id)[:, None, :] # [B,] => [B, T=1, H] + else: + ph_spk_embed = spk_embed = None + + encoder_out, dur_pred_out = self.fs2( + txt_tokens, midi=midi, ph2word=ph2word, + ph_dur=ph_dur, word_dur=word_dur, + spk_embed=ph_spk_embed, languages=languages, + infer=infer + ) + + if not self.predict_pitch and not self.predict_variances: + return dur_pred_out, None, ({} if infer else None) + + if mel2ph is None and word_dur is not None: # inference from file + dur_pred_align = self.rr(dur_pred_out, ph2word, word_dur) + mel2ph = self.lr(dur_pred_align) + mel2ph = F.pad(mel2ph, [0, base_pitch.shape[1] - mel2ph.shape[1]]) + + encoder_out = F.pad(encoder_out, [0, 0, 1, 0]) + mel2ph_ = mel2ph[..., None].repeat([1, 1, hparams['hidden_size']]) + condition = torch.gather(encoder_out, 1, mel2ph_) + + if self.use_stretch_embed: + stretch = torch.round(1000 * self.sr(mel2ph, ph_dur)) + if self.training and stretch.numel() > 1000: + # construct a phoneme stretching index lookup table with a total of 1001 indexes (0~1000) + table = self.stretch_embed(torch.arange(0, 1001, device=stretch.device)) + stretch_embed = torch.index_select(table, 0, stretch.view(-1).long()).view_as(condition) + else: + stretch_embed = self.stretch_embed(stretch) + condition += stretch_embed + self.stretch_embed_rnn.flatten_parameters() + stretch_embed_rnn_out, _ = self.stretch_embed_rnn(condition) + condition = condition + stretch_embed_rnn_out + + if self.use_spk_id: + condition += spk_embed + + if self.predict_pitch: + if self.use_melody_encoder: + melody_encoder_out = self.melody_encoder( + note_midi, note_rest, note_dur, + glide=note_glide + ) + melody_encoder_out = F.pad(melody_encoder_out, [0, 0, 1, 0]) + mel2note_ = mel2note[..., None].repeat([1, 1, hparams['hidden_size']]) + melody_condition = torch.gather(melody_encoder_out, 1, mel2note_) + pitch_cond = condition + melody_condition + else: + pitch_cond = condition.clone() # preserve the original tensor to avoid further inplace operations + + retake_unset = pitch_retake is None + if retake_unset: + pitch_retake = torch.ones_like(mel2ph, dtype=torch.bool) + + if pitch_expr is None: + pitch_retake_embed = self.pitch_retake_embed(pitch_retake.long()) + else: + retake_true_embed = self.pitch_retake_embed( + torch.ones(1, 1, dtype=torch.long, device=txt_tokens.device) + ) # [B=1, T=1] => [B=1, T=1, H] + retake_false_embed = self.pitch_retake_embed( + torch.zeros(1, 1, dtype=torch.long, device=txt_tokens.device) + ) # [B=1, T=1] => [B=1, T=1, H] + pitch_expr = (pitch_expr * pitch_retake)[:, :, None] # [B, T, 1] + pitch_retake_embed = pitch_expr * retake_true_embed + (1. - pitch_expr) * retake_false_embed + + pitch_cond += pitch_retake_embed + if self.use_melody_encoder: + if retake_unset: # generate from scratch + delta_pitch_in = torch.zeros_like(base_pitch) + else: + delta_pitch_in = (pitch - base_pitch) * ~pitch_retake + if self.use_variance_scaling: + pitch_cond += self.delta_pitch_embed(delta_pitch_in[:, :, None] / 12) + else: + pitch_cond += self.delta_pitch_embed(delta_pitch_in[:, :, None]) + else: + if not retake_unset: # retake + base_pitch = base_pitch * pitch_retake + pitch * ~pitch_retake + if self.use_variance_scaling: + pitch_cond += self.base_pitch_embed(base_pitch[:, :, None] / 128) + else: + pitch_cond += self.base_pitch_embed(base_pitch[:, :, None]) + + if infer: + pitch_pred_out = self.pitch_predictor(pitch_cond, infer=True) + else: + pitch_pred_out = self.pitch_predictor(pitch_cond, pitch - base_pitch, infer=False) + else: + pitch_pred_out = None + + if not self.predict_variances: + return dur_pred_out, pitch_pred_out, ({} if infer else None) + + if pitch is None: + pitch = base_pitch + pitch_pred_out + if self.use_variance_scaling: + var_cond = condition + self.pitch_embed(pitch[:, :, None] / 12) + else: + var_cond = condition + self.pitch_embed(pitch[:, :, None]) + + variance_inputs = self.collect_variance_inputs(**kwargs) + + if variance_retake is not None: + variance_embeds = [ + self.variance_embeds[v_name](v_input[:, :, None] * self.variance_retake_scaling[v_name]) * ~variance_retake[v_name][:, :, None] + for v_name, v_input in zip(self.variance_prediction_list, variance_inputs) + ] + var_cond += torch.stack(variance_embeds, dim=-1).sum(-1) + + variance_outputs = self.variance_predictor(var_cond, variance_inputs, infer=infer) + + if infer: + variances_pred_out = self.collect_variance_outputs(variance_outputs) + else: + variances_pred_out = variance_outputs + + return dur_pred_out, pitch_pred_out, variances_pred_out diff --git a/modules/vocoders/__init__.py b/modules/vocoders/__init__.py new file mode 100644 index 0000000..607db7a --- /dev/null +++ b/modules/vocoders/__init__.py @@ -0,0 +1,2 @@ +from modules.vocoders import ddsp +from modules.vocoders import nsf_hifigan diff --git a/modules/vocoders/ddsp.py b/modules/vocoders/ddsp.py new file mode 100644 index 0000000..ddd228f --- /dev/null +++ b/modules/vocoders/ddsp.py @@ -0,0 +1,120 @@ +import pathlib + +import numpy as np +import torch +import torch.nn.functional as F +import yaml +from librosa.filters import mel as librosa_mel_fn + +from basics.base_vocoder import BaseVocoder +from modules.vocoders.registry import register_vocoder +from utils.hparams import hparams + + +class DotDict(dict): + def __getattr__(*args): + val = dict.get(*args) + return DotDict(val) if type(val) is dict else val + + __setattr__ = dict.__setitem__ + __delattr__ = dict.__delitem__ + + +def load_model(model_path: pathlib.Path, device='cpu'): + config_file = model_path.with_name('config.yaml') + with open(config_file, "r") as config: + args = yaml.safe_load(config) + args = DotDict(args) + + # load model + print(' [Loading] ' + str(model_path)) + model = torch.jit.load(model_path, map_location=torch.device(device)) + model.eval() + + return model, args + + +@register_vocoder +class DDSP(BaseVocoder): + def __init__(self, device='cpu'): + self.device = device + model_path = pathlib.Path(hparams['vocoder_ckpt']) + assert model_path.exists(), 'DDSP model file is not found!' + self.model, self.args = load_model(model_path, device=self.device) + + def to_device(self, device): + pass + + def get_device(self): + return self.device + + def spec2wav_torch(self, mel, f0): # mel: [B, T, bins] f0: [B, T] + if self.args.data.sampling_rate != hparams['audio_sample_rate']: + print('Mismatch parameters: hparams[\'audio_sample_rate\']=', hparams['audio_sample_rate'], '!=', + self.args.data.sampling_rate, '(vocoder)') + if self.args.data.n_mels != hparams['audio_num_mel_bins']: + print('Mismatch parameters: hparams[\'audio_num_mel_bins\']=', hparams['audio_num_mel_bins'], '!=', + self.args.data.n_mels, '(vocoder)') + if self.args.data.n_fft != hparams['fft_size']: + print('Mismatch parameters: hparams[\'fft_size\']=', hparams['fft_size'], '!=', self.args.data.n_fft, + '(vocoder)') + if self.args.data.win_length != hparams['win_size']: + print('Mismatch parameters: hparams[\'win_size\']=', hparams['win_size'], '!=', self.args.data.win_length, + '(vocoder)') + if self.args.data.block_size != hparams['hop_size']: + print('Mismatch parameters: hparams[\'hop_size\']=', hparams['hop_size'], '!=', self.args.data.block_size, + '(vocoder)') + if self.args.data.mel_fmin != hparams['fmin']: + print('Mismatch parameters: hparams[\'fmin\']=', hparams['fmin'], '!=', self.args.data.mel_fmin, + '(vocoder)') + if self.args.data.mel_fmax != hparams['fmax']: + print('Mismatch parameters: hparams[\'fmax\']=', hparams['fmax'], '!=', self.args.data.mel_fmax, + '(vocoder)') + with torch.no_grad(): + mel = mel.to(self.device) + mel_base = hparams.get('mel_base', 10) + if mel_base != 'e': + assert mel_base in [10, '10'], "mel_base must be 'e', '10' or 10." + else: + # log mel to log10 mel + mel = 0.434294 * mel + f0 = f0.unsqueeze(-1).to(self.device) + signal, _, (s_h, s_n) = self.model(mel, f0) + signal = signal.view(-1) + return signal + + def spec2wav(self, mel, f0): + if self.args.data.sampling_rate != hparams['audio_sample_rate']: + print('Mismatch parameters: hparams[\'audio_sample_rate\']=', hparams['audio_sample_rate'], '!=', + self.args.data.sampling_rate, '(vocoder)') + if self.args.data.n_mels != hparams['audio_num_mel_bins']: + print('Mismatch parameters: hparams[\'audio_num_mel_bins\']=', hparams['audio_num_mel_bins'], '!=', + self.args.data.n_mels, '(vocoder)') + if self.args.data.n_fft != hparams['fft_size']: + print('Mismatch parameters: hparams[\'fft_size\']=', hparams['fft_size'], '!=', self.args.data.n_fft, + '(vocoder)') + if self.args.data.win_length != hparams['win_size']: + print('Mismatch parameters: hparams[\'win_size\']=', hparams['win_size'], '!=', self.args.data.win_length, + '(vocoder)') + if self.args.data.block_size != hparams['hop_size']: + print('Mismatch parameters: hparams[\'hop_size\']=', hparams['hop_size'], '!=', self.args.data.block_size, + '(vocoder)') + if self.args.data.mel_fmin != hparams['fmin']: + print('Mismatch parameters: hparams[\'fmin\']=', hparams['fmin'], '!=', self.args.data.mel_fmin, + '(vocoder)') + if self.args.data.mel_fmax != hparams['fmax']: + print('Mismatch parameters: hparams[\'fmax\']=', hparams['fmax'], '!=', self.args.data.mel_fmax, + '(vocoder)') + with torch.no_grad(): + mel = torch.FloatTensor(mel).unsqueeze(0).to(self.device) + mel_base = hparams.get('mel_base', 10) + if mel_base != 'e': + assert mel_base in [10, '10'], "mel_base must be 'e', '10' or 10." + else: + # log mel to log10 mel + mel = 0.434294 * mel + f0 = torch.FloatTensor(f0).unsqueeze(0).unsqueeze(-1).to(self.device) + signal, _, (s_h, s_n) = self.model(mel, f0) + signal = signal.view(-1) + wav_out = signal.cpu().numpy() + return wav_out diff --git a/modules/vocoders/nsf_hifigan.py b/modules/vocoders/nsf_hifigan.py new file mode 100644 index 0000000..aed2c58 --- /dev/null +++ b/modules/vocoders/nsf_hifigan.py @@ -0,0 +1,104 @@ +import pathlib + +import torch + +try: + from lightning.pytorch.utilities.rank_zero import rank_zero_info +except ModuleNotFoundError: + rank_zero_info = print + +from modules.nsf_hifigan.models import load_model +from basics.base_vocoder import BaseVocoder +from modules.vocoders.registry import register_vocoder +from utils.hparams import hparams + + +@register_vocoder +class NsfHifiGAN(BaseVocoder): + def __init__(self): + model_path = pathlib.Path(hparams['vocoder_ckpt']) + if not model_path.exists(): + raise FileNotFoundError( + f'NSF-HiFiGAN vocoder model is not found at \'{model_path}\'. ' + 'Please follow instructions in docs/BestPractices.md#vocoders to get one.' + ) + rank_zero_info(f'| Load HifiGAN: {model_path}') + self.model, self.h = load_model(model_path) + + @property + def device(self): + return next(self.model.parameters()).device + + def to_device(self, device): + self.model.to(device) + + def get_device(self): + return self.device + + def spec2wav_torch(self, mel, **kwargs): # mel: [B, T, bins] + if self.h.sampling_rate != hparams['audio_sample_rate']: + print('Mismatch parameters: hparams[\'audio_sample_rate\']=', hparams['audio_sample_rate'], '!=', + self.h.sampling_rate, '(vocoder)') + if self.h.num_mels != hparams['audio_num_mel_bins']: + print('Mismatch parameters: hparams[\'audio_num_mel_bins\']=', hparams['audio_num_mel_bins'], '!=', + self.h.num_mels, '(vocoder)') + if self.h.n_fft != hparams['fft_size']: + print('Mismatch parameters: hparams[\'fft_size\']=', hparams['fft_size'], '!=', self.h.n_fft, '(vocoder)') + if self.h.win_size != hparams['win_size']: + print('Mismatch parameters: hparams[\'win_size\']=', hparams['win_size'], '!=', self.h.win_size, + '(vocoder)') + if self.h.hop_size != hparams['hop_size']: + print('Mismatch parameters: hparams[\'hop_size\']=', hparams['hop_size'], '!=', self.h.hop_size, + '(vocoder)') + if self.h.fmin != hparams['fmin']: + print('Mismatch parameters: hparams[\'fmin\']=', hparams['fmin'], '!=', self.h.fmin, '(vocoder)') + if self.h.fmax != hparams['fmax']: + print('Mismatch parameters: hparams[\'fmax\']=', hparams['fmax'], '!=', self.h.fmax, '(vocoder)') + with torch.no_grad(): + c = mel.transpose(2, 1) # [B, T, bins] + mel_base = hparams.get('mel_base', 10) + if mel_base != 'e': + assert mel_base in [10, '10'], "mel_base must be 'e', '10' or 10." + # log10 to log mel + c = 2.30259 * c + f0 = kwargs.get('f0') # [B, T] + if f0 is not None: + y = self.model(c, f0).view(-1) + else: + y = self.model(c).view(-1) + return y + + def spec2wav(self, mel, **kwargs): + if self.h.sampling_rate != hparams['audio_sample_rate']: + print('Mismatch parameters: hparams[\'audio_sample_rate\']=', hparams['audio_sample_rate'], '!=', + self.h.sampling_rate, '(vocoder)') + if self.h.num_mels != hparams['audio_num_mel_bins']: + print('Mismatch parameters: hparams[\'audio_num_mel_bins\']=', hparams['audio_num_mel_bins'], '!=', + self.h.num_mels, '(vocoder)') + if self.h.n_fft != hparams['fft_size']: + print('Mismatch parameters: hparams[\'fft_size\']=', hparams['fft_size'], '!=', self.h.n_fft, '(vocoder)') + if self.h.win_size != hparams['win_size']: + print('Mismatch parameters: hparams[\'win_size\']=', hparams['win_size'], '!=', self.h.win_size, + '(vocoder)') + if self.h.hop_size != hparams['hop_size']: + print('Mismatch parameters: hparams[\'hop_size\']=', hparams['hop_size'], '!=', self.h.hop_size, + '(vocoder)') + if self.h.fmin != hparams['fmin']: + print('Mismatch parameters: hparams[\'fmin\']=', hparams['fmin'], '!=', self.h.fmin, '(vocoder)') + if self.h.fmax != hparams['fmax']: + print('Mismatch parameters: hparams[\'fmax\']=', hparams['fmax'], '!=', self.h.fmax, '(vocoder)') + with torch.no_grad(): + c = torch.FloatTensor(mel).unsqueeze(0).transpose(2, 1).to(self.device) + mel_base = hparams.get('mel_base', 10) + if mel_base != 'e': + assert mel_base in [10, '10'], "mel_base must be 'e', '10' or 10." + # log10 to log mel + c = 2.30259 * c + f0 = kwargs.get('f0') + if f0 is not None: + f0 = torch.FloatTensor(f0[None, :]).to(self.device) + y = self.model(c, f0).view(-1) + else: + y = self.model(c).view(-1) + wav_out = y.cpu().numpy() + return wav_out diff --git a/modules/vocoders/registry.py b/modules/vocoders/registry.py new file mode 100644 index 0000000..a77e37e --- /dev/null +++ b/modules/vocoders/registry.py @@ -0,0 +1,21 @@ +import importlib + + +VOCODERS = {} + + +def register_vocoder(cls): + VOCODERS[cls.__name__.lower()] = cls + VOCODERS[cls.__name__] = cls + return cls + + +def get_vocoder_cls(hparams): + if hparams['vocoder'] in VOCODERS: + return VOCODERS[hparams['vocoder']] + else: + vocoder_cls = hparams['vocoder'] + pkg = ".".join(vocoder_cls.split(".")[:-1]) + cls_name = vocoder_cls.split(".")[-1] + vocoder_cls = getattr(importlib.import_module(pkg), cls_name) + return vocoder_cls diff --git a/preprocessing/acoustic_binarizer.py b/preprocessing/acoustic_binarizer.py new file mode 100644 index 0000000..9301f14 --- /dev/null +++ b/preprocessing/acoustic_binarizer.py @@ -0,0 +1,355 @@ +""" + item: one piece of data + item_name: data id + wav_fn: wave file path + spk: dataset name + ph_seq: phoneme sequence + ph_dur: phoneme durations +""" +import csv +import os +import pathlib +import random +from copy import deepcopy + +import librosa +import numpy as np +import torch + +from basics.base_binarizer import BaseBinarizer +from basics.base_pe import BasePE +from modules.fastspeech.tts_modules import LengthRegulator +from modules.pe import initialize_pe +from utils.binarizer_utils import ( + SinusoidalSmoothingConv1d, + get_mel_torch, + get_mel2ph_torch, + get_energy_librosa, + get_breathiness, + get_voicing, + get_tension_base_harmonic, +) +from utils.decomposed_waveform import DecomposedWaveform +from utils.hparams import hparams + +os.environ["OMP_NUM_THREADS"] = "1" +ACOUSTIC_ITEM_ATTRIBUTES = [ + 'spk_id', + 'mel', + 'languages', + 'tokens', + 'mel2ph', + 'f0', + 'energy', + 'breathiness', + 'voicing', + 'tension', + 'key_shift', + 'speed', +] +WAV_CANDIDATE_EXTENSIONS = ['.wav', '.flac'] + +pitch_extractor: BasePE = None +energy_smooth: SinusoidalSmoothingConv1d = None +breathiness_smooth: SinusoidalSmoothingConv1d = None +voicing_smooth: SinusoidalSmoothingConv1d = None +tension_smooth: SinusoidalSmoothingConv1d = None + + +class AcousticBinarizer(BaseBinarizer): + def __init__(self): + super().__init__(data_attrs=ACOUSTIC_ITEM_ATTRIBUTES) + self.lr = LengthRegulator() + self.need_energy = hparams['use_energy_embed'] + self.need_breathiness = hparams['use_breathiness_embed'] + self.need_voicing = hparams['use_voicing_embed'] + self.need_tension = hparams['use_tension_embed'] + assert hparams['mel_base'] == 'e', ( + "Mel base must be set to \'e\' according to 2nd stage of the migration plan. " + "See https://github.com/openvpi/DiffSinger/releases/tag/v2.3.0 for more details." + ) + + def load_meta_data(self, raw_data_dir: pathlib.Path, ds_id, spk, lang): + meta_data_dict = {} + with open(raw_data_dir / 'transcriptions.csv', 'r', encoding='utf-8') as f: + for utterance_label in csv.DictReader(f): + item_name = utterance_label['name'] + wav_fn = None + for ext in WAV_CANDIDATE_EXTENSIONS: + candidate_fn = raw_data_dir / 'wavs' / f'{item_name}{ext}' + if candidate_fn.exists(): + wav_fn = candidate_fn + break + if wav_fn is None: + raise FileNotFoundError( + f'Waveform file not found for item \'{item_name}\'. ' + f'Candidate extensions: {WAV_CANDIDATE_EXTENSIONS}' + ) + temp_dict = { + 'wav_fn': str(wav_fn), + 'spk_id': self.spk_map[spk], + 'spk_name': spk, + 'lang_seq': [ + ( + self.lang_map[lang if '/' not in p else p.split('/', maxsplit=1)[0]] + if self.phoneme_dictionary.is_cross_lingual(p if '/' in p else f'{lang}/{p}') + else 0 + ) + for p in utterance_label['ph_seq'].split() + ], + 'ph_seq': self.phoneme_dictionary.encode(utterance_label['ph_seq'], lang=lang), + 'ph_dur': [float(x) for x in utterance_label['ph_dur'].split()], + 'ph_text': utterance_label['ph_seq'], + } + assert len(temp_dict['ph_seq']) == len(temp_dict['ph_dur']), \ + f'Lengths of ph_seq and ph_dur mismatch in \'{item_name}\'.' + assert all(ph_dur >= 0 for ph_dur in temp_dict['ph_dur']), \ + f'Negative ph_dur found in \'{item_name}\'.' + meta_data_dict[f'{ds_id}:{item_name}'] = temp_dict + + return meta_data_dict + + @torch.no_grad() + def process_item(self, item_name, meta_data, binarization_args): + waveform, _ = librosa.load(meta_data['wav_fn'], sr=hparams['audio_sample_rate'], mono=True) + mel = get_mel_torch( + waveform, hparams['audio_sample_rate'], num_mel_bins=hparams['audio_num_mel_bins'], + hop_size=hparams['hop_size'], win_size=hparams['win_size'], fft_size=hparams['fft_size'], + fmin=hparams['fmin'], fmax=hparams['fmax'], + device=self.device + ) + length = mel.shape[0] + seconds = length * hparams['hop_size'] / hparams['audio_sample_rate'] + processed_input = { + 'name': item_name, + 'wav_fn': meta_data['wav_fn'], + 'spk_id': meta_data['spk_id'], + 'spk_name': meta_data['spk_name'], + 'seconds': seconds, + 'length': length, + 'mel': mel, + 'languages': np.array(meta_data['lang_seq'], dtype=np.int64), + 'tokens': np.array(meta_data['ph_seq'], dtype=np.int64), + 'ph_dur': np.array(meta_data['ph_dur']).astype(np.float32), + 'ph_text': meta_data['ph_text'], + } + + # get ground truth dur + processed_input['mel2ph'] = get_mel2ph_torch( + self.lr, torch.from_numpy(processed_input['ph_dur']), length, self.timestep, device=self.device + ).cpu().numpy() + + # get ground truth f0 + global pitch_extractor + if pitch_extractor is None: + pitch_extractor = initialize_pe() + gt_f0, uv = pitch_extractor.get_pitch( + waveform, samplerate=hparams['audio_sample_rate'], length=length, + hop_size=hparams['hop_size'], f0_min=hparams['f0_min'], f0_max=hparams['f0_max'], + interp_uv=True + ) + if uv.all(): # All unvoiced + print(f'Skipped \'{item_name}\': empty gt f0') + return None + processed_input['f0'] = gt_f0.astype(np.float32) + + if self.need_energy: + # get ground truth energy + energy = get_energy_librosa( + waveform, length, hop_size=hparams['hop_size'], win_size=hparams['win_size'] + ).astype(np.float32) + + global energy_smooth + if energy_smooth is None: + energy_smooth = SinusoidalSmoothingConv1d( + round(hparams['energy_smooth_width'] / self.timestep) + ).eval().to(self.device) + energy = energy_smooth(torch.from_numpy(energy).to(self.device)[None])[0] + + processed_input['energy'] = energy.cpu().numpy() + + # create a DecomposedWaveform object for further feature extraction + dec_waveform = DecomposedWaveform( + waveform, samplerate=hparams['audio_sample_rate'], f0=gt_f0 * ~uv, + hop_size=hparams['hop_size'], fft_size=hparams['fft_size'], win_size=hparams['win_size'], + algorithm=hparams['hnsep'] + ) + + if self.need_breathiness: + # get ground truth breathiness + breathiness = get_breathiness( + dec_waveform, None, None, length=length + ) + + global breathiness_smooth + if breathiness_smooth is None: + breathiness_smooth = SinusoidalSmoothingConv1d( + round(hparams['breathiness_smooth_width'] / self.timestep) + ).eval().to(self.device) + breathiness = breathiness_smooth(torch.from_numpy(breathiness).to(self.device)[None])[0] + + processed_input['breathiness'] = breathiness.cpu().numpy() + + if self.need_voicing: + # get ground truth voicing + voicing = get_voicing( + dec_waveform, None, None, length=length + ) + + global voicing_smooth + if voicing_smooth is None: + voicing_smooth = SinusoidalSmoothingConv1d( + round(hparams['voicing_smooth_width'] / self.timestep) + ).eval().to(self.device) + voicing = voicing_smooth(torch.from_numpy(voicing).to(self.device)[None])[0] + + processed_input['voicing'] = voicing.cpu().numpy() + + if self.need_tension: + # get ground truth tension + tension = get_tension_base_harmonic( + dec_waveform, None, None, length=length, domain='logit' + ) + + global tension_smooth + if tension_smooth is None: + tension_smooth = SinusoidalSmoothingConv1d( + round(hparams['tension_smooth_width'] / self.timestep) + ).eval().to(self.device) + tension = tension_smooth(torch.from_numpy(tension).to(self.device)[None])[0] + if tension.isnan().any(): + print('Error:', item_name) + print(tension) + return None + + processed_input['tension'] = tension.cpu().numpy() + + if hparams['use_key_shift_embed']: + processed_input['key_shift'] = 0. + + if hparams['use_speed_embed']: + processed_input['speed'] = 1. + + return processed_input + + def arrange_data_augmentation(self, data_iterator): + aug_map = {} + aug_list = [] + all_item_names = [item_name for item_name, _ in data_iterator] + total_scale = 0 + aug_pe = initialize_pe() + if self.augmentation_args['random_pitch_shifting']['enabled']: + from augmentation.spec_stretch import SpectrogramStretchAugmentation + aug_args = self.augmentation_args['random_pitch_shifting'] + key_shift_min, key_shift_max = aug_args['range'] + assert hparams['use_key_shift_embed'], \ + 'Random pitch shifting augmentation requires use_key_shift_embed == True.' + assert key_shift_min < 0 < key_shift_max, \ + 'Random pitch shifting augmentation must have a range where min < 0 < max.' + + aug_ins = SpectrogramStretchAugmentation(self.raw_data_dirs, aug_args, pe=aug_pe) + scale = aug_args['scale'] + aug_item_names = random.choices(all_item_names, k=int(scale * len(all_item_names))) + + for aug_item_name in aug_item_names: + rand = random.uniform(-1, 1) + if rand < 0: + key_shift = key_shift_min * abs(rand) + else: + key_shift = key_shift_max * rand + aug_task = { + 'name': aug_item_name, + 'func': aug_ins.process_item, + 'kwargs': {'key_shift': key_shift} + } + if aug_item_name in aug_map: + aug_map[aug_item_name].append(aug_task) + else: + aug_map[aug_item_name] = [aug_task] + aug_list.append(aug_task) + + total_scale += scale + + if self.augmentation_args['fixed_pitch_shifting']['enabled']: + from augmentation.spec_stretch import SpectrogramStretchAugmentation + aug_args = self.augmentation_args['fixed_pitch_shifting'] + targets = aug_args['targets'] + scale = aug_args['scale'] + spk_id_size = max(self.spk_ids) + 1 + min_num_spk = (1 + len(targets)) * spk_id_size + assert not self.augmentation_args['random_pitch_shifting']['enabled'], \ + 'Fixed pitch shifting augmentation is not compatible with random pitch shifting.' + assert len(targets) == len(set(targets)), \ + 'Fixed pitch shifting augmentation requires having no duplicate targets.' + assert hparams['use_spk_id'], 'Fixed pitch shifting augmentation requires use_spk_id == True.' + assert hparams['num_spk'] >= min_num_spk, \ + f'Fixed pitch shifting augmentation requires num_spk >= (1 + len(targets)) * (max(spk_ids) + 1).' + assert scale < 1, 'Fixed pitch shifting augmentation requires scale < 1.' + + aug_ins = SpectrogramStretchAugmentation(self.raw_data_dirs, aug_args, pe=aug_pe) + for i, target in enumerate(targets): + aug_item_names = random.choices(all_item_names, k=int(scale * len(all_item_names))) + for aug_item_name in aug_item_names: + replace_spk_id = self.spk_ids[int(aug_item_name.split(':', maxsplit=1)[0])] + (i + 1) * spk_id_size + aug_task = { + 'name': aug_item_name, + 'func': aug_ins.process_item, + 'kwargs': {'key_shift': target, 'replace_spk_id': replace_spk_id} + } + if aug_item_name in aug_map: + aug_map[aug_item_name].append(aug_task) + else: + aug_map[aug_item_name] = [aug_task] + aug_list.append(aug_task) + + total_scale += scale * len(targets) + + if self.augmentation_args['random_time_stretching']['enabled']: + from augmentation.spec_stretch import SpectrogramStretchAugmentation + aug_args = self.augmentation_args['random_time_stretching'] + speed_min, speed_max = aug_args['range'] + assert hparams['use_speed_embed'], \ + 'Random time stretching augmentation requires use_speed_embed == True.' + assert 0 < speed_min < 1 < speed_max, \ + 'Random time stretching augmentation must have a range where 0 < min < 1 < max.' + + aug_ins = SpectrogramStretchAugmentation(self.raw_data_dirs, aug_args, pe=aug_pe) + scale = aug_args['scale'] + k_from_raw = int(scale / (1 + total_scale) * len(all_item_names)) + k_from_aug = int(total_scale * scale / (1 + total_scale) * len(all_item_names)) + k_mutate = int(total_scale * scale / (1 + scale) * len(all_item_names)) + aug_types = [0] * k_from_raw + [1] * k_from_aug + [2] * k_mutate + aug_items = random.choices(all_item_names, k=k_from_raw) + random.choices(aug_list, k=k_from_aug + k_mutate) + + for aug_type, aug_item in zip(aug_types, aug_items): + # Uniform distribution in log domain + speed = speed_min * (speed_max / speed_min) ** random.random() + if aug_type == 0: + aug_task = { + 'name': aug_item, + 'func': aug_ins.process_item, + 'kwargs': {'speed': speed} + } + if aug_item in aug_map: + aug_map[aug_item].append(aug_task) + else: + aug_map[aug_item] = [aug_task] + aug_list.append(aug_task) + elif aug_type == 1: + aug_task = { + 'name': aug_item, + 'func': aug_item['func'], + 'kwargs': deepcopy(aug_item['kwargs']) + } + aug_task['kwargs']['speed'] = speed + if aug_item['name'] in aug_map: + aug_map[aug_item['name']].append(aug_task) + else: + aug_map[aug_item['name']] = [aug_task] + aug_list.append(aug_task) + elif aug_type == 2: + aug_item['kwargs']['speed'] = speed + + total_scale += scale + + return aug_map diff --git a/preprocessing/variance_binarizer.py b/preprocessing/variance_binarizer.py new file mode 100644 index 0000000..3d2990f --- /dev/null +++ b/preprocessing/variance_binarizer.py @@ -0,0 +1,529 @@ +import csv +import json +import os +import pathlib + +import librosa +import numpy as np +import torch +import torch.nn.functional as F +from scipy import interpolate + +from basics.base_binarizer import BaseBinarizer, BinarizationError +from basics.base_pe import BasePE +from modules.fastspeech.tts_modules import LengthRegulator +from modules.pe import initialize_pe +from utils.binarizer_utils import ( + SinusoidalSmoothingConv1d, + get_mel2ph_torch, + get_energy_librosa, + get_breathiness, + get_voicing, + get_tension_base_harmonic, +) +from utils.decomposed_waveform import DecomposedWaveform +from utils.hparams import hparams +from utils.infer_utils import resample_align_curve +from utils.pitch_utils import interp_f0 +from utils.plot import distribution_to_figure + +os.environ["OMP_NUM_THREADS"] = "1" +VARIANCE_ITEM_ATTRIBUTES = [ + 'spk_id', # index number of dataset/speaker, int64 + 'languages', # index numbers of phoneme languages, int64[T_ph,] + 'tokens', # index numbers of phonemes, int64[T_ph,] + 'ph_dur', # durations of phonemes, in number of frames, int64[T_ph,] + 'midi', # phoneme-level mean MIDI pitch, int64[T_ph,] + 'ph2word', # similar to mel2ph format, representing number of phones within each note, int64[T_ph,] + 'mel2ph', # mel2ph format representing number of frames within each phone, int64[T_s,] + 'note_midi', # note-level MIDI pitch, float32[T_n,] + 'note_rest', # flags for rest notes, bool[T_n,] + 'note_dur', # durations of notes, in number of frames, int64[T_n,] + 'note_glide', # flags for glides, 0 = none, 1 = up, 2 = down, int64[T_n,] + 'mel2note', # mel2ph format representing number of frames within each note, int64[T_s,] + 'base_pitch', # interpolated and smoothed frame-level MIDI pitch, float32[T_s,] + 'pitch', # actual pitch in semitones, float32[T_s,] + 'uv', # unvoiced masks (only for objective evaluation metrics), bool[T_s,] + 'energy', # frame-level RMS (dB), float32[T_s,] + 'breathiness', # frame-level RMS of aperiodic parts (dB), float32[T_s,] + 'voicing', # frame-level RMS of harmonic parts (dB), float32[T_s,] + 'tension', # frame-level tension (logit), float32[T_s,] +] +WAV_CANDIDATE_EXTENSIONS = ['.wav', '.flac'] +DS_INDEX_SEP = '#' + +# These operators are used as global variables due to a PyTorch shared memory bug on Windows platforms. +# See https://github.com/pytorch/pytorch/issues/100358 +pitch_extractor: BasePE = None +midi_smooth: SinusoidalSmoothingConv1d = None +energy_smooth: SinusoidalSmoothingConv1d = None +breathiness_smooth: SinusoidalSmoothingConv1d = None +voicing_smooth: SinusoidalSmoothingConv1d = None +tension_smooth: SinusoidalSmoothingConv1d = None + + +class VarianceBinarizer(BaseBinarizer): + def __init__(self): + super().__init__(data_attrs=VARIANCE_ITEM_ATTRIBUTES) + + self.use_glide_embed = hparams['use_glide_embed'] + glide_types = hparams['glide_types'] + assert 'none' not in glide_types, 'Type name \'none\' is reserved and should not appear in glide_types.' + self.glide_map = { + 'none': 0, + **{ + typename: idx + 1 + for idx, typename in enumerate(glide_types) + } + } + + predict_energy = hparams['predict_energy'] + predict_breathiness = hparams['predict_breathiness'] + predict_voicing = hparams['predict_voicing'] + predict_tension = hparams['predict_tension'] + self.predict_variances = predict_energy or predict_breathiness or predict_voicing or predict_tension + self.lr = LengthRegulator().to(self.device) + self.prefer_ds = self.binarization_args['prefer_ds'] + self.cached_ds = {} + + def load_attr_from_ds(self, ds_id, name, attr, idx=0): + item_name = f'{ds_id}:{name}' + item_name_with_idx = f'{item_name}{DS_INDEX_SEP}{idx}' + if item_name_with_idx in self.cached_ds: + ds = self.cached_ds[item_name_with_idx][0] + elif item_name in self.cached_ds: + ds = self.cached_ds[item_name][idx] + else: + ds_path = self.raw_data_dirs[ds_id] / 'ds' / f'{name}{DS_INDEX_SEP}{idx}.ds' + if ds_path.exists(): + cache_key = item_name_with_idx + else: + ds_path = self.raw_data_dirs[ds_id] / 'ds' / f'{name}.ds' + cache_key = item_name + if not ds_path.exists(): + return None + with open(ds_path, 'r', encoding='utf8') as f: + ds = json.load(f) + if not isinstance(ds, list): + ds = [ds] + self.cached_ds[cache_key] = ds + ds = ds[idx] + return ds.get(attr) + + def load_meta_data(self, raw_data_dir: pathlib.Path, ds_id, spk, lang): + meta_data_dict = {} + + with open(raw_data_dir / 'transcriptions.csv', 'r', encoding='utf8') as f: + for utterance_label in csv.DictReader(f): + utterance_label: dict + item_name = utterance_label['name'] + item_idx = int(item_name.rsplit(DS_INDEX_SEP, maxsplit=1)[-1]) if DS_INDEX_SEP in item_name else 0 + + def require(attr, optional=False): + if self.prefer_ds: + value = self.load_attr_from_ds(ds_id, item_name, attr, item_idx) + else: + value = None + if value is None: + value = utterance_label.get(attr) + if value is None and not optional: + raise ValueError(f'Missing required attribute {attr} of item \'{item_name}\'.') + return value + + wav_fn = None + for ext in WAV_CANDIDATE_EXTENSIONS: + candidate_fn = raw_data_dir / 'wavs' / f'{item_name}{ext}' + if candidate_fn.exists(): + wav_fn = candidate_fn + break + if wav_fn is None and not self.prefer_ds: + raise FileNotFoundError( + f'Waveform file not found for item \'{item_name}\'. ' + f'Candidate extensions: {WAV_CANDIDATE_EXTENSIONS}\n' + f'If you are using DS files instead of waveform files, please set \'prefer_ds\' to true.' + ) + + temp_dict = { + 'ds_idx': item_idx, + 'spk_id': self.spk_map[spk], + 'spk_name': spk, + 'language_id': self.lang_map[lang], + 'language_name': lang, + 'wav_fn': str(wav_fn) if wav_fn is not None else None, + 'lang_seq': [ + ( + self.lang_map[lang if '/' not in p else p.split('/', maxsplit=1)[0]] + if self.phoneme_dictionary.is_cross_lingual(p if '/' in p else f'{lang}/{p}') + else 0 + ) + for p in utterance_label['ph_seq'].split() + ], + 'ph_seq': self.phoneme_dictionary.encode(require('ph_seq'), lang=lang), + 'ph_dur': [float(x) for x in require('ph_dur').split()], + 'ph_text': require('ph_seq'), + } + + assert len(temp_dict['ph_seq']) == len(temp_dict['ph_dur']), \ + f'Lengths of ph_seq and ph_dur mismatch in \'{item_name}\'.' + assert all(ph_dur >= 0 for ph_dur in temp_dict['ph_dur']), \ + f'Negative ph_dur found in \'{item_name}\'.' + + if hparams['predict_dur']: + temp_dict['ph_num'] = [int(x) for x in require('ph_num').split()] + assert len(temp_dict['ph_seq']) == sum(temp_dict['ph_num']), \ + f'Sum of ph_num does not equal length of ph_seq in \'{item_name}\'.' + + if hparams['predict_pitch']: + temp_dict['note_seq'] = require('note_seq').split() + temp_dict['note_dur'] = [float(x) for x in require('note_dur').split()] + assert all(note_dur >= 0 for note_dur in temp_dict['note_dur']), \ + f'Negative note_dur found in \'{item_name}\'.' + assert len(temp_dict['note_seq']) == len(temp_dict['note_dur']), \ + f'Lengths of note_seq and note_dur mismatch in \'{item_name}\'.' + assert any([note != 'rest' for note in temp_dict['note_seq']]), \ + f'All notes are rest in \'{item_name}\'.' + if hparams['use_glide_embed']: + note_glide = require('note_glide', optional=True) + if note_glide is None: + note_glide = ['none' for _ in temp_dict['note_seq']] + else: + note_glide = note_glide.split() + assert len(note_glide) == len(temp_dict['note_seq']), \ + f'Lengths of note_seq and note_glide mismatch in \'{item_name}\'.' + assert all(g in self.glide_map for g in note_glide), \ + f'Invalid glide type found in \'{item_name}\'.' + temp_dict['note_glide'] = note_glide + + meta_data_dict[f'{ds_id}:{item_name}'] = temp_dict + + return meta_data_dict + + def check_coverage(self): + super().check_coverage() + if not hparams['predict_pitch']: + return + + # MIDI pitch distribution summary + midi_map = {} + for item_name in self.items: + for midi in self.items[item_name]['note_seq']: + if midi == 'rest': + continue + midi = librosa.note_to_midi(midi, round_midi=True) + if midi in midi_map: + midi_map[midi] += 1 + else: + midi_map[midi] = 1 + + print('===== MIDI Pitch Distribution Summary =====') + for i, key in enumerate(sorted(midi_map.keys())): + if i == len(midi_map) - 1: + end = '\n' + elif i % 10 == 9: + end = ',\n' + else: + end = ', ' + print(f'\'{librosa.midi_to_note(key, unicode=False)}\': {midi_map[key]}', end=end) + + # Draw graph. + midis = sorted(midi_map.keys()) + notes = [librosa.midi_to_note(m, unicode=False) for m in range(midis[0], midis[-1] + 1)] + plt = distribution_to_figure( + title='MIDI Pitch Distribution Summary', + x_label='MIDI Key', y_label='Number of occurrences', + items=notes, values=[midi_map.get(m, 0) for m in range(midis[0], midis[-1] + 1)] + ) + filename = self.binary_data_dir / 'midi_distribution.jpg' + plt.savefig(fname=filename, + bbox_inches='tight', + pad_inches=0.25) + print(f'| save summary to \'{filename}\'') + + if self.use_glide_embed: + # Glide type distribution summary + glide_count = { + g: 0 + for g in self.glide_map + } + for item_name in self.items: + for glide in self.items[item_name]['note_glide']: + if glide == 'none' or glide not in self.glide_map: + glide_count['none'] += 1 + else: + glide_count[glide] += 1 + + print('===== Glide Type Distribution Summary =====') + for i, key in enumerate(sorted(glide_count.keys(), key=lambda k: self.glide_map[k])): + if i == len(glide_count) - 1: + end = '\n' + elif i % 10 == 9: + end = ',\n' + else: + end = ', ' + print(f'\'{key}\': {glide_count[key]}', end=end) + + if any(n == 0 for _, n in glide_count.items()): + raise BinarizationError( + f'Missing glide types in dataset: ' + f'{sorted([g for g, n in glide_count.items() if n == 0], key=lambda k: self.glide_map[k])}' + ) + + @torch.no_grad() + def process_item(self, item_name, meta_data, binarization_args): + ds_id, name = item_name.split(':', maxsplit=1) + name = name.rsplit(DS_INDEX_SEP, maxsplit=1)[0] + ds_id = int(ds_id) + ds_seg_idx = meta_data['ds_idx'] + seconds = sum(meta_data['ph_dur']) + length = round(seconds / self.timestep) + T_ph = len(meta_data['ph_seq']) + processed_input = { + 'name': item_name, + 'wav_fn': meta_data['wav_fn'], + 'spk_id': meta_data['spk_id'], + 'spk_name': meta_data['spk_name'], + 'seconds': seconds, + 'length': length, + 'languages': np.array(meta_data['lang_seq'], dtype=np.int64), + 'tokens': np.array(meta_data['ph_seq'], dtype=np.int64), + 'ph_text': meta_data['ph_text'], + } + + ph_dur_sec = torch.FloatTensor(meta_data['ph_dur']).to(self.device) + ph_acc = torch.round(torch.cumsum(ph_dur_sec, dim=0) / self.timestep + 0.5).long() + ph_dur = torch.diff(ph_acc, dim=0, prepend=torch.LongTensor([0]).to(self.device)) + processed_input['ph_dur'] = ph_dur.cpu().numpy() + + mel2ph = get_mel2ph_torch( + self.lr, ph_dur_sec, length, self.timestep, device=self.device + ) + + if hparams['predict_pitch'] or self.predict_variances: + processed_input['mel2ph'] = mel2ph.cpu().numpy() + + # Below: extract actual f0, convert to pitch and calculate delta pitch + if meta_data['wav_fn'] is not None: + waveform, _ = librosa.load(meta_data['wav_fn'], sr=hparams['audio_sample_rate'], mono=True) + else: + waveform = None + + global pitch_extractor + if pitch_extractor is None: + pitch_extractor = initialize_pe() + f0 = uv = None + if self.prefer_ds: + f0_seq = self.load_attr_from_ds(ds_id, name, 'f0_seq', idx=ds_seg_idx) + if f0_seq is not None: + f0 = resample_align_curve( + np.array(f0_seq.split(), np.float32), + original_timestep=float(self.load_attr_from_ds(ds_id, name, 'f0_timestep', idx=ds_seg_idx)), + target_timestep=self.timestep, + align_length=length + ) + uv = f0 == 0 + f0, _ = interp_f0(f0, uv) + if f0 is None: + f0, uv = pitch_extractor.get_pitch( + waveform, samplerate=hparams['audio_sample_rate'], length=length, + hop_size=hparams['hop_size'], f0_min=hparams['f0_min'], f0_max=hparams['f0_max'], + interp_uv=True + ) + if uv.all(): # All unvoiced + print(f'Skipped \'{item_name}\': empty gt f0') + return None + pitch = torch.from_numpy(librosa.hz_to_midi(f0.astype(np.float32)).astype(np.float32)).to(self.device) + + if hparams['predict_dur']: + ph_num = torch.LongTensor(meta_data['ph_num']).to(self.device) + ph2word = self.lr(ph_num[None])[0] + processed_input['ph2word'] = ph2word.cpu().numpy() + mel2dur = torch.gather(F.pad(ph_dur, [1, 0], value=1), 0, mel2ph) # frame-level phone duration + ph_midi = pitch.new_zeros(T_ph + 1).scatter_add( + 0, mel2ph, pitch / mel2dur + )[1:] + processed_input['midi'] = ph_midi.round().long().clamp(min=0, max=127).cpu().numpy() + + if hparams['predict_pitch']: + # Below: get note sequence and interpolate rest notes + note_midi = np.array( + [(librosa.note_to_midi(n, round_midi=False) if n != 'rest' else -1) for n in meta_data['note_seq']], + dtype=np.float32 + ) + note_rest = note_midi < 0 + interp_func = interpolate.interp1d( + np.where(~note_rest)[0], note_midi[~note_rest], + kind='nearest', fill_value='extrapolate' + ) + note_midi[note_rest] = interp_func(np.where(note_rest)[0]) + processed_input['note_midi'] = note_midi + processed_input['note_rest'] = note_rest + note_midi = torch.from_numpy(note_midi).to(self.device) + + note_dur_sec = torch.FloatTensor(meta_data['note_dur']).to(self.device) + note_acc = torch.round(torch.cumsum(note_dur_sec, dim=0) / self.timestep + 0.5).long() + note_dur = torch.diff(note_acc, dim=0, prepend=torch.LongTensor([0]).to(self.device)) + processed_input['note_dur'] = note_dur.cpu().numpy() + + mel2note = get_mel2ph_torch( + self.lr, note_dur_sec, mel2ph.shape[0], self.timestep, device=self.device + ) + processed_input['mel2note'] = mel2note.cpu().numpy() + + # Below: get ornament attributes + if hparams['use_glide_embed']: + processed_input['note_glide'] = np.array([ + self.glide_map.get(x, 0) for x in meta_data['note_glide'] + ], dtype=np.int64) + + # Below: + # 1. Get the frame-level MIDI pitch, which is a step function curve + # 2. smoothen the pitch step curve as the base pitch curve + frame_midi_pitch = torch.gather(F.pad(note_midi, [1, 0], value=0), 0, mel2note) + global midi_smooth + if midi_smooth is None: + midi_smooth = SinusoidalSmoothingConv1d( + round(hparams['midi_smooth_width'] / self.timestep) + ).eval().to(self.device) + smoothed_midi_pitch = midi_smooth(frame_midi_pitch[None])[0] + processed_input['base_pitch'] = smoothed_midi_pitch.cpu().numpy() + + if hparams['predict_pitch'] or self.predict_variances: + processed_input['pitch'] = pitch.cpu().numpy() + processed_input['uv'] = uv + + # Below: extract energy + if hparams['predict_energy']: + energy = None + energy_from_wav = False + if self.prefer_ds: + energy_seq = self.load_attr_from_ds(ds_id, name, 'energy', idx=ds_seg_idx) + if energy_seq is not None: + energy = resample_align_curve( + np.array(energy_seq.split(), np.float32), + original_timestep=float(self.load_attr_from_ds( + ds_id, name, 'energy_timestep', idx=ds_seg_idx + )), + target_timestep=self.timestep, + align_length=length + ) + if energy is None: + energy = get_energy_librosa( + waveform, length, + hop_size=hparams['hop_size'], win_size=hparams['win_size'] + ).astype(np.float32) + energy_from_wav = True + + if energy_from_wav: + global energy_smooth + if energy_smooth is None: + energy_smooth = SinusoidalSmoothingConv1d( + round(hparams['energy_smooth_width'] / self.timestep) + ).eval().to(self.device) + energy = energy_smooth(torch.from_numpy(energy).to(self.device)[None])[0].cpu().numpy() + + processed_input['energy'] = energy + + # create a DecomposedWaveform object for further feature extraction + dec_waveform = DecomposedWaveform( + waveform, samplerate=hparams['audio_sample_rate'], f0=f0 * ~uv, + hop_size=hparams['hop_size'], fft_size=hparams['fft_size'], win_size=hparams['win_size'], + algorithm=hparams['hnsep'] + ) if waveform is not None else None + + # Below: extract breathiness + if hparams['predict_breathiness']: + breathiness = None + breathiness_from_wav = False + if self.prefer_ds: + breathiness_seq = self.load_attr_from_ds(ds_id, name, 'breathiness', idx=ds_seg_idx) + if breathiness_seq is not None: + breathiness = resample_align_curve( + np.array(breathiness_seq.split(), np.float32), + original_timestep=float(self.load_attr_from_ds( + ds_id, name, 'breathiness_timestep', idx=ds_seg_idx + )), + target_timestep=self.timestep, + align_length=length + ) + if breathiness is None: + breathiness = get_breathiness( + dec_waveform, None, None, length=length + ) + breathiness_from_wav = True + + if breathiness_from_wav: + global breathiness_smooth + if breathiness_smooth is None: + breathiness_smooth = SinusoidalSmoothingConv1d( + round(hparams['breathiness_smooth_width'] / self.timestep) + ).eval().to(self.device) + breathiness = breathiness_smooth(torch.from_numpy(breathiness).to(self.device)[None])[0].cpu().numpy() + + processed_input['breathiness'] = breathiness + + # Below: extract voicing + if hparams['predict_voicing']: + voicing = None + voicing_from_wav = False + if self.prefer_ds: + voicing_seq = self.load_attr_from_ds(ds_id, name, 'voicing', idx=ds_seg_idx) + if voicing_seq is not None: + voicing = resample_align_curve( + np.array(voicing_seq.split(), np.float32), + original_timestep=float(self.load_attr_from_ds( + ds_id, name, 'voicing_timestep', idx=ds_seg_idx + )), + target_timestep=self.timestep, + align_length=length + ) + if voicing is None: + voicing = get_voicing( + dec_waveform, None, None, length=length + ) + voicing_from_wav = True + + if voicing_from_wav: + global voicing_smooth + if voicing_smooth is None: + voicing_smooth = SinusoidalSmoothingConv1d( + round(hparams['voicing_smooth_width'] / self.timestep) + ).eval().to(self.device) + voicing = voicing_smooth(torch.from_numpy(voicing).to(self.device)[None])[0].cpu().numpy() + + processed_input['voicing'] = voicing + + # Below: extract tension + if hparams['predict_tension']: + tension = None + tension_from_wav = False + if self.prefer_ds: + tension_seq = self.load_attr_from_ds(ds_id, name, 'tension', idx=ds_seg_idx) + if tension_seq is not None: + tension = resample_align_curve( + np.array(tension_seq.split(), np.float32), + original_timestep=float(self.load_attr_from_ds( + ds_id, name, 'tension_timestep', idx=ds_seg_idx + )), + target_timestep=self.timestep, + align_length=length + ) + if tension is None: + tension = get_tension_base_harmonic( + dec_waveform, None, None, length=length, domain='logit' + ) + tension_from_wav = True + + if tension_from_wav: + global tension_smooth + if tension_smooth is None: + tension_smooth = SinusoidalSmoothingConv1d( + round(hparams['tension_smooth_width'] / self.timestep) + ).eval().to(self.device) + tension = tension_smooth(torch.from_numpy(tension).to(self.device)[None])[0].cpu().numpy() + + processed_input['tension'] = tension + + return processed_input + + def arrange_data_augmentation(self, data_iterator): + return {} diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..4645417 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,23 @@ +# It is recommended to install PyTorch manually. +# PyTorch >= 2.4 is recommended. +# See instructions at https://pytorch.org/get-started/locally/ + +click +einops>=0.7.0 +h5py +librosa<0.10.0 +lightning~=2.3.0 +matplotlib +MonkeyType>=23.3.0 +numpy<2.0.0 +onnx>=1.21.0 +onnxsim>=0.6.5 +praat-parselmouth==0.4.3 +pyworld==0.3.4 +PyYAML +resampy +scipy>=1.10.0 +tensorboard +tensorboardX +torchmetrics +tqdm diff --git a/samples/00_我多想说再见啊.ds b/samples/00_我多想说再见啊.ds new file mode 100644 index 0000000..509f21c --- /dev/null +++ b/samples/00_我多想说再见啊.ds @@ -0,0 +1,362 @@ +[ + { + "offset": 7.0, + "text": "AP 试 着 SP 掬 一 把 星 辰 SP 在 手 心 SP", + "ph_seq": "AP sh ir zh e SP j v y i b a x in ch en SP z ai sh ou x in SP", + "ph_dur": "0.3947 0.209 0.2554 0.1509 0.5921 0.1045 0.1045 0.3019 0.0929 0.3019 0.0929 0.2438 0.1625 0.1045 0.0929 0.4063 0.0697 0.1277 0.2206 0.1741 0.3599 0.2438 0.9985 0.0464", + "ph_num": "2 2 1 2 2 2 2 2 1 2 2 2 1 1", + "note_seq": "rest D#3 C4 rest D#4 C4 A#3 C4 C4 rest D#3 G3 G#3 rest", + "note_dur": "0.6 0.4 0.6 0.2 0.4 0.4 0.4 0.2 0.4 0.2 0.4 0.6 1.0 0.05", + "note_slur": "0 0 0 0 0 0 0 0 0 0 0 0 0 0", + "f0_seq": "160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 160.3 155.6 155.6 155.6 155.6 155.6 155.6 155.6 155.6 155.6 155.6 155.6 155.7 156.2 156.7 157.3 157.9 158.5 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h e ch u x iang s i0 m ing y ve SP", + "ph_num": "2 2 2 2 2 2 1 1", + "note_seq": "rest F4 F4 E4 E4 G#4 A4 rest", + "note_dur": "0.2541 0.2055 0.2055 0.4110 0.4110 0.4110 0.2055 0.1000", + "note_slur": "0 0 0 0 0 0 0 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + }, + { + "offset": 149.3640410958904, + "text": "SP lou SP", + "ph_seq": "SP l ou SP", + "ph_num": "2 1 1", + "note_seq": "rest E4 rest", + "note_dur": "0.2250 1.0274 0.1000", + "note_slur": "0 0 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + }, + { + "offset": 150.9667808219178, + "text": "SP ke lian lou shang SP", + "ph_seq": "SP k e l ian l ou sh ang SP", + "ph_num": "2 2 2 2 1 1", + "note_seq": "rest B4 B4 A4 A4 rest", + "note_dur": "0.2661 0.2055 0.2055 0.2055 0.3082 0.1000", + "note_slur": "0 0 0 0 0 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + }, + { + "offset": 152.28698630136984, + "text": "SP yue pai huai SP", + "ph_seq": "SP y ve p ai h uai SP", + "ph_num": "2 2 2 1 1", + "note_seq": "rest E4 D4 D4 rest", + "note_dur": "0.1788 0.2055 0.2055 0.3082 0.1000", + "note_slur": "0 0 0 0 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + }, + { + "offset": 153.99246575342465, + "text": "SP ying zhao li ren SP", + "ph_seq": "SP y ing zh ao l i r en SP", + "ph_num": "2 2 2 2 1 1", + "note_seq": "rest D4 C4 C4 D4 D4 rest", + "note_dur": "0.1171 0.2055 0.2055 0.2055 0.2055 0.4110 0.1000", + "note_slur": "0 0 0 0 1 0 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + }, + { + "offset": 155.53184931506848, + "text": "SP zhuang jing tai SP", + "ph_seq": "SP zh uang j ing t ai SP", + "ph_num": "2 2 2 1 1", + "note_seq": "rest D4 C4 A3 rest", + "note_dur": "0.2216 0.2055 0.2055 0.3082 0.1000", + "note_slur": "0 0 0 0 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + }, + { + "offset": 157.28013698630136, + "text": "SP yu hu lian zhong SP", + "ph_seq": "SP y v h u l ian zh ong SP", + "ph_num": "2 2 2 2 1 1", + "note_seq": "rest A3 C4 C4 D4 D4 rest", + "note_dur": "0.1171 0.2055 0.2055 0.2055 0.2055 0.2055 0.1000", + "note_slur": "0 0 0 0 1 0 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + }, + { + "offset": 158.39657534246575, + "text": "SP juan SP", + "ph_seq": "SP j van SP", + "ph_num": "2 1 1", + "note_seq": "rest D4 rest", + "note_dur": "0.2336 0.2055 0.1000", + "note_slur": "0 0 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + }, + { + "offset": 158.82808219178082, + "text": "SP bu SP", + "ph_seq": "SP b u SP", + "ph_num": "2 1 1", + "note_seq": "rest E4 rest", + "note_dur": "0.2130 0.2055 0.1000", + "note_slur": "0 0 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + }, + { + "offset": 159.18253424657536, + "text": "SP qu SP", + "ph_seq": "SP q v SP", + "ph_num": "2 1 1", + "note_seq": "rest F4 rest", + "note_dur": "0.2695 0.4110 0.1000", + "note_slur": "0 0 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + }, + { + "offset": 160.4736301369863, + "text": "SP da yi zhen shang fu huan lai SP", + "ph_seq": "SP d a y i zh en sh ang f u h uan l ai SP", + "ph_num": "2 2 2 2 2 2 2 1 1", + "note_seq": "rest E4 D4 E4 E4 G#4 A4 E4 E4 rest", + "note_dur": "0.2113 0.2055 0.2055 0.4110 0.4110 0.4110 0.2055 0.2055 0.8219 0.1000", + "note_slur": "0 0 0 0 0 0 0 1 0 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + }, + { + "offset": 164.0986301369863, + "text": "SP si shi xiang wang bu xiang wen SP", + "ph_seq": "SP s i0 sh ir x iang w ang b u x iang w en SP", + "ph_num": "2 2 2 2 2 2 2 1 1", + "note_seq": "rest E4 G4 A4 G4 E4 E4 D4 C4 C4 C4 D4 rest", + "note_dur": "0.2849 0.4110 0.2055 0.2055 0.2055 0.2055 0.4110 0.2055 0.2055 0.2055 0.2055 0.4110 0.1000", + "note_slur": "0 0 0 1 0 1 0 0 1 0 0 1 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + }, + { + "offset": 167.49246575342465, + "text": "SP yuan zhu yuan hua liu zhao jun SP", + "ph_seq": "SP y van zh u y van h ua l iu zh ao j vn SP", + "ph_num": "2 2 2 2 2 2 2 1 1", + "note_seq": "rest C4 A3 C4 D4 E4 G4 E4 rest", + "note_dur": "0.1788 0.4110 0.4110 0.4110 0.4110 0.4110 0.2055 0.4110 0.1000", + "note_slur": "0 0 0 0 0 0 0 0 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + }, + { + "offset": 170.70479452054792, + "text": "SP hong yan chang fei guang bu du SP", + "ph_seq": "SP h ong y En ch ang f ei g uang b u d u SP", + "ph_num": "2 2 2 2 2 2 2 1 1", + "note_seq": "rest D4 C4 C4 D4 E4 F4 E4 D4 rest", + "note_dur": "0.2541 0.2055 0.2055 0.4110 0.4110 0.4110 0.4110 0.2055 0.4110 0.1000", + "note_slur": "0 0 1 0 0 0 0 0 0 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + }, + { + "offset": 173.71849315068494, + "text": "SP yu long qian yue shui cheng wen SP", + "ph_seq": "SP y v l ong q ian y ve sh ui ch eng w en SP", + "ph_num": "2 2 2 2 2 2 2 1 1", + "note_seq": "rest E4 F4 F4 E4 E4 G#4 A4 E4 E4 rest", + "note_dur": "0.1171 0.1027 0.1027 0.2055 0.4110 0.4110 0.4110 0.2055 0.2055 1.0274 0.1000", + "note_slur": "0 0 1 0 0 0 0 0 1 0 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + }, + { + "offset": 177.30239726027398, + "text": "SP zuo ye xian tan meng luo hua SP", + "ph_seq": "SP z uo y E x ian t an m eng l uo h ua SP", + "ph_num": "2 2 2 2 2 2 2 1 1", + "note_seq": "rest G4 A4 A4 G4 G4 F4 E4 E4 F4 rest", + "note_dur": "0.2318 0.2055 0.2055 0.4110 0.4110 0.4110 0.2055 0.1027 0.3082 0.4110 0.1000", + "note_slur": "0 0 1 0 0 0 0 1 0 0 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + }, + { + "offset": 180.14486301369863, + "text": "SP ke lian chun ban bu huan jia SP", + "ph_seq": "SP k e l ian ch un b an b u h uan j ia SP", + "ph_num": "2 2 2 2 2 2 2 1 1", + "note_seq": "rest F4 F4 E4 E4 E4 D4 E4 rest", + "note_dur": "0.2661 0.2055 0.2055 0.4110 0.4110 0.4110 0.4110 0.8219 0.1000", + "note_slur": "0 0 0 0 0 0 0 0 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + }, + { + "offset": 183.46506849315068, + "text": "SP jiang shui liu chun qu yu jin SP", + "ph_seq": "SP j iang sh ui l iu ch un q v y v j in SP", + "ph_num": "2 2 2 2 2 2 2 1 1", + "note_seq": "rest B3 C4 C4 B3 B3 B3 C4 D4 rest", + "note_dur": "0.2336 0.1027 0.1027 0.2055 0.4110 0.4110 0.4110 0.2055 0.8219 0.1000", + "note_slur": "0 0 1 0 0 0 0 0 0 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + }, + { + "offset": 186.7527397260274, + "text": "SP jiang tan luo yue fu xi xia SP", + "ph_seq": "SP j iang t an l uo y ve f u x i x ia SP", + "ph_num": "2 2 2 2 2 2 2 1 1", + "note_seq": "rest E4 D4 E4 E4 G#4 A4 E4 rest", + "note_dur": "0.2336 0.2055 0.2055 0.4110 0.4110 0.4110 0.2055 1.4384 0.1000", + "note_slur": "0 0 0 0 0 0 0 0 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + }, + { + "offset": 190.4308219178082, + "text": "SP ha SP", + "ph_seq": "SP h a SP", + "ph_num": "2 1 1", + "note_seq": "rest E4 A4 G4 E4 rest", + "note_dur": "0.2541 0.4110 0.4110 0.4110 1.6438 0.1000", + "note_slur": "0 0 1 1 1 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + }, + { + "offset": 193.71849315068494, + "text": "SP ha SP", + "ph_seq": "SP h a SP", + "ph_num": "2 1 1", + "note_seq": "rest E4 C5 B4 E4 rest", + "note_dur": "0.2541 0.4110 0.4110 0.4110 0.8219 0.1000", + "note_slur": "0 0 1 1 1 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + }, + { + "offset": 196.18424657534246, + "text": "SP ha SP", + "ph_seq": "SP h a SP", + "ph_num": "2 1 1", + "note_seq": "rest G4 C4 D#4 D4 F4 E4 B3 rest", + "note_dur": "0.2541 0.4110 0.4110 0.1027 1.1301 0.1027 0.2055 0.9247 0.1000", + "note_slur": "0 0 1 1 1 1 1 1 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + }, + { + "offset": 200.03698630136984, + "text": "SP a SP", + "ph_seq": "SP a SP", + "ph_num": "1 1 1", + "note_seq": "rest C4 D#4 D4 E4 A4 G#4 B4 rest", + "note_dur": "0.1000 0.4110 0.1027 0.3082 0.8219 0.4110 0.4110 0.9247 0.1000", + "note_slur": "0 0 1 1 1 1 1 1 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + }, + { + "offset": 203.58150684931508, + "text": "SP ha SP", + "ph_seq": "SP h a SP", + "ph_num": "2 1 1", + "note_seq": "rest E4 A4 G4 E4 rest", + "note_dur": "0.2541 0.4110 0.4110 0.4110 1.6438 0.1000", + "note_slur": "0 0 1 1 1 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + }, + { + "offset": 206.86917808219175, + "text": "SP ha SP", + "ph_seq": "SP h a SP", + "ph_num": "2 1 1", + "note_seq": "rest E4 C5 B4 E4 G4 C4 D#4 D4 rest", + "note_dur": "0.2541 0.4110 0.4110 0.4110 1.2329 0.4110 0.4110 0.1027 1.1301 0.1000", + "note_slur": "0 0 1 1 1 1 1 1 1 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + }, + { + "offset": 211.74931506849313, + "text": "SP a SP", + "ph_seq": "SP a SP", + "ph_num": "1 1 1", + "note_seq": "rest B3 rest", + "note_dur": "0.1000 1.0274 0.1000", + "note_slur": "0 0 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + }, + { + "offset": 213.1876712328767, + "text": "SP a SP", + "ph_seq": "SP a SP", + "ph_num": "1 1 1", + "note_seq": "rest C4 D#4 D4 F4 E4 A4 G#4 B4 rest", + "note_dur": "0.1000 0.4110 0.1027 0.5137 0.1027 0.5137 0.4110 0.4110 1.8493 0.1000", + "note_slur": "0 0 1 1 1 1 1 1 1 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + }, + { + "offset": 219.1585616438356, + "text": "SP xie yue chen chen cang hai wu SP", + "ph_seq": "SP x ie y ve ch en ch en c ang h ai w u SP", + "ph_num": "2 2 2 2 2 2 2 1 1", + "note_seq": "rest A3 E4 E4 G4 A4 A4 B4 A4 A4 G4 A4 G4 E4 rest", + "note_dur": "0.2935 0.4110 0.4110 0.4110 0.4110 0.8219 0.2055 0.2055 0.4110 1.2329 0.1027 0.1027 0.2055 1.0274 0.1000", + "note_slur": "0 0 0 0 0 0 0 1 1 0 1 1 1 1 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + }, + { + "offset": 225.79383561643837, + "text": "SP jie shi xiao xiang wu xian lu SP", + "ph_seq": "SP j ie sh ir x iao x iang w u x ian l u SP", + "ph_num": "2 2 2 2 2 2 2 1 1", + "note_seq": "rest D4 E4 G4 E4 D4 E4 A3 D4 E4 D4 E4 D4 B3 rest", + "note_dur": "0.2336 0.4110 0.4110 0.4110 0.4110 0.2055 0.6164 0.4110 0.2055 1.4384 0.1027 0.1027 0.2055 1.0274 0.1000", + "note_slur": "0 0 0 0 0 0 1 0 0 1 1 1 1 1 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + }, + { + "offset": 232.38972602739727, + "text": "SP bu zhi cheng yue ji ren gui SP", + "ph_seq": "SP b u zh ir ch eng y ve j i r en g ui SP", + "ph_num": "2 2 2 2 2 2 2 1 1", + "note_seq": "rest D4 A3 A3 C#4 D4 E4 F4 A4 C5 B4 A4 B4 A4 rest", + "note_dur": "0.2130 0.2055 0.2055 0.4110 0.2055 0.2055 0.4110 0.8219 0.2055 0.6164 1.2329 0.1027 0.1027 0.8219 0.1000", + "note_slur": "0 0 1 0 0 1 0 0 0 1 0 1 1 1 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + }, + { + "offset": 238.95308219178082, + "text": "SP luo yue yao qing man jiang shu SP", + "ph_seq": "SP l uo y ve y ao q ing m an j iang sh u SP", + "ph_num": "2 2 2 2 2 2 2 1 1", + "note_seq": "rest A4 B4 B4 B4 B4 B4 C5 D5 E5 rest", + "note_dur": "0.2250 0.2055 0.2055 0.4110 0.4110 1.2329 0.4110 0.4110 0.2055 6.3699 0.1000", + "note_slur": "0 0 1 0 0 0 0 0 0 1 0", + "input_type": "phoneme", + "gender_timestep": null, + "gender": null, + "velocity_timestep": null, + "velocity": null + } +] \ No newline at end of file diff --git a/samples/credits.txt b/samples/credits.txt new file mode 100644 index 0000000..d1eb0ca --- /dev/null +++ b/samples/credits.txt @@ -0,0 +1,42 @@ +## 00_我多想说再见啊 + +MIDI: 班超BanC +Pitch reference: Synthesizer V AI Mai +Tuning: 飞弦p https://space.bilibili.com/5347090; YQ之神 https://space.bilibili.com/102844209 + + +## 01_逍遥仙 + +Tuning: 赤松_Akamatsu https://space.bilibili.com/29902857; YQ之神 https://space.bilibili.com/102844209 + + +## 02_一半一半 + +Tuning: 笛鹿FlutyDeer https://space.bilibili.com/386270936 + + +## 03_撒娇八连 + +Pitch reference: 瑶摆桃桃烤地瓜 https://space.bilibili.com/23346333 + + +## 04_仙瑶 + +Tuning: 笛鹿FlutyDeer https://space.bilibili.com/386270936 + + +## 05_恋人心 + +Tuning: 笛鹿FlutyDeer https://space.bilibili.com/386270936 + + +## 06_不谓侠 + +MIDI: 帝国妖月 +Tuning: ZhiBinShyu with PitchDiffusion https://www.bilibili.com/video/BV12T411t7Qg + + +## 07_春江花月夜 + +Source: https://www.vsqx.top/project/vn8437 +MIDI: 何念生 https://www.vsqx.top/space/2960 diff --git a/scripts/binarize.py b/scripts/binarize.py new file mode 100644 index 0000000..74abd2b --- /dev/null +++ b/scripts/binarize.py @@ -0,0 +1,25 @@ +import importlib +import os +import sys +from pathlib import Path + +root_dir = Path(__file__).parent.parent.resolve() +os.environ['PYTHONPATH'] = str(root_dir) +sys.path.insert(0, str(root_dir)) + +from utils.hparams import set_hparams, hparams + +set_hparams() + + +def binarize(): + binarizer_cls = hparams["binarizer_cls"] + pkg = ".".join(binarizer_cls.split(".")[:-1]) + cls_name = binarizer_cls.split(".")[-1] + binarizer_cls = getattr(importlib.import_module(pkg), cls_name) + print("| Binarizer: ", binarizer_cls) + binarizer_cls().process() + + +if __name__ == '__main__': + binarize() diff --git a/scripts/drop_spk.py b/scripts/drop_spk.py new file mode 100644 index 0000000..6a19d09 --- /dev/null +++ b/scripts/drop_spk.py @@ -0,0 +1,72 @@ +import torch +import argparse +import pathlib +import re + + +def modify_spk_embed(spk_embed): + num_spk, hidden_size = spk_embed.shape + all_ids = set(range(num_spk)) + if args.drop is not None: + drop_ids = set([int(i) for i in args.drop.split(',') if i != '']).intersection(all_ids) + else: + drop_ids = all_ids - set([int(i) for i in args.retain.split(',') if i != '']) + + fill_list = None + if args.fill == 'zeros': + fill_list = [0. for _ in drop_ids] + elif args.fill == 'random': + fill_list = [torch.randn(1, hidden_size, dtype=torch.float32, device='cpu') for _ in drop_ids] + elif args.fill == 'mean': + mean = torch.mean(spk_embed, dim=0, keepdim=True) + fill_list = [mean for _ in drop_ids] + elif args.fill == 'cyclic': + retain_ids = sorted(all_ids - drop_ids) + num_retain = len(retain_ids) + fill_list = [spk_embed[retain_ids[i % num_retain], :] for i, _ in enumerate(drop_ids)] + + for spk_id, fill in zip(sorted(drop_ids), fill_list): + spk_embed[spk_id, :] = fill + + +parser = argparse.ArgumentParser(description='Drop or edit spk_embed in a checkpoint.') +parser.add_argument('input', type=str, help='Path to the input file') +parser.add_argument('output', type=str, help='Path to the output file') +drop_retain_group = parser.add_mutually_exclusive_group() +drop_retain_group.add_argument('--drop', type=str, required=False, metavar='ID,ID,...', + help='Drop specific speaker IDs.') +drop_retain_group.add_argument('--retain', type=str, required=False, metavar='ID,ID,...', + help='Retain specific speaker IDs and drop all the others.') +parser.add_argument('--fill', type=str, required=False, default='zeros', metavar='METHOD', + choices=['zeros', 'random', 'mean', 'cyclic'], + help='Specify a filling method for the dropped embedding. ' + 'Available methods: zeros, random, mean, cyclic') +parser.add_argument('--overwrite', required=False, default=False, + action='store_true', help='Overwrite if the output file exists.') +args = parser.parse_args() +assert args.drop is not None or args.retain is not None, 'Either --drop or --retain should be specified.' +if args.drop and not re.fullmatch(r'(\d+)?(,\d+)*,?', args.drop): + print(f'Invalid format for --drop: \'{args.drop}\'') + exit(-1) +if args.retain and not re.fullmatch(r'(\d+)?(,\d+)*,?', args.retain): + print(f'Invalid format for --retain: \'{args.retain}\'') + exit(-1) + +import torch +input_ckpt = pathlib.Path(args.input).resolve() +output_ckpt = pathlib.Path(args.output).resolve() +assert input_ckpt.exists(), 'The input file does not exist.' +assert args.overwrite or not output_ckpt.exists(), \ + 'The output file already exists or is the same as the input file.\n' \ + 'This is not recommended because spk_embed dropping scripts may not be stable, ' \ + 'and you may be at risk of losing your model.\n' \ + 'If you are sure to OVERWRITE the existing file, please re-run this script with the \'--overwrite\' argument.' + +ckpt_loaded = torch.load(input_ckpt, map_location='cpu') +state_dict = ckpt_loaded['state_dict'] +if 'model.fs2.spk_embed.weight' in state_dict: + modify_spk_embed(state_dict['model.fs2.spk_embed.weight']) +if 'model.spk_embed.weight' in state_dict: + modify_spk_embed(state_dict['model.spk_embed.weight']) + +torch.save(ckpt_loaded, output_ckpt) diff --git a/scripts/export.py b/scripts/export.py new file mode 100644 index 0000000..537cdad --- /dev/null +++ b/scripts/export.py @@ -0,0 +1,294 @@ +import os +import pathlib +import re +import sys +from typing import List + +import click +import torch + +root_dir = pathlib.Path(__file__).resolve().parent.parent +os.environ['PYTHONPATH'] = str(root_dir) +sys.path.insert(0, str(root_dir)) + +from utils.hparams import set_hparams, hparams + + +def find_exp(exp): + if not (root_dir / 'checkpoints' / exp).exists(): + for subdir in (root_dir / 'checkpoints').iterdir(): + if not subdir.is_dir(): + continue + if subdir.name.startswith(exp): + print(f'| match ckpt by prefix: {subdir.name}') + exp = subdir.name + break + else: + raise click.BadParameter( + f'There are no matching exp starting with \'{exp}\' in \'checkpoints\' folder. ' + 'Please specify \'--exp\' as the folder name or prefix.' + ) + else: + print(f'| found ckpt by name: {exp}') + return exp + + +def parse_spk_settings(export_spk, freeze_spk): + if export_spk is None: + export_spk = [] + else: + export_spk = list(export_spk) + from utils.infer_utils import parse_commandline_spk_mix + spk_name_pattern = r'[0-9A-Za-z_-]+' + export_spk_mix = [] + for spk in export_spk: + assert '=' in spk or '|' not in spk, \ + 'You must specify an alias with \'NAME=\' for each speaker mix.' + if '=' in spk: + alias, mix = spk.split('=', maxsplit=1) + assert re.fullmatch(spk_name_pattern, alias) is not None, f'Invalid alias \'{alias}\' for speaker mix.' + export_spk_mix.append((alias, parse_commandline_spk_mix(mix))) + else: + export_spk_mix.append((spk, {spk: 1.0})) + freeze_spk_mix = None + if freeze_spk is not None: + assert '=' in freeze_spk or '|' not in freeze_spk, \ + 'You must specify an alias with \'NAME=\' for each speaker mix.' + if '=' in freeze_spk: + alias, mix = freeze_spk.split('=', maxsplit=1) + assert re.fullmatch(spk_name_pattern, alias) is not None, f'Invalid alias \'{alias}\' for speaker mix.' + freeze_spk_mix = (alias, parse_commandline_spk_mix(mix)) + else: + freeze_spk_mix = (freeze_spk, {freeze_spk: 1.0}) + return export_spk_mix, freeze_spk_mix + + +@click.group() +def main(): + pass + + +@main.command(help='Export DiffSinger acoustic model to ONNX format.') +@click.option( + '--exp', type=click.STRING, + required=True, metavar='EXP', callback=lambda ctx, param, value: find_exp(value), + help='Choose an experiment to export.' +) +@click.option( + '--ckpt', type=click.IntRange(min=0), + required=False, metavar='STEPS', + help='Checkpoint training steps.' +) +@click.option( + '--out', type=click.Path( + dir_okay=True, file_okay=False, + path_type=pathlib.Path, resolve_path=True + ), + required=False, + help='Output directory for the artifacts.' +) +@click.option( + '--freeze_gender', type=click.FloatRange(min=-1, max=1), + help='(for random pitch shifting) Freeze gender value into the model.' +) +@click.option( + '--freeze_velocity', is_flag=True, + help='(for random time stretching) Freeze default velocity value into the model.' +) +@click.option( + '--export_spk', type=click.STRING, + required=False, multiple=True, + help='(for multi-speaker models) Export one or more speaker or speaker mixture keys.' +) +@click.option( + '--freeze_spk', type=click.STRING, + required=False, + help='(for multi-speaker models) Freeze one speaker or speaker mixture into the model.' +) +def acoustic( + exp: str, + ckpt: int = None, + out: pathlib.Path = None, + freeze_gender: float = 0., + freeze_velocity: bool = False, + export_spk: List[str] = None, + freeze_spk: str = None +): + # Validate arguments + if export_spk and freeze_spk: + print('--export_spk is exclusive to --freeze_spk.') + exit(-1) + if out is None: + out = root_dir / 'artifacts' / exp + export_spk_mix, freeze_spk_mix = parse_spk_settings(export_spk, freeze_spk) + + # Load configurations + sys.argv = [ + sys.argv[0], + '--exp_name', + exp, + '--infer' + ] + set_hparams() + + # Export artifacts + from deployment.exporters import DiffSingerAcousticExporter + print(f'| Exporter: {DiffSingerAcousticExporter}') + exporter = DiffSingerAcousticExporter( + device=torch.device('cuda' if torch.cuda.is_available() else 'cpu'), + cache_dir=root_dir / 'deployment' / 'cache', + ckpt_steps=ckpt, + freeze_gender=freeze_gender, + freeze_velocity=freeze_velocity, + export_spk=export_spk_mix, + freeze_spk=freeze_spk_mix + ) + try: + exporter.export(out) + except KeyboardInterrupt: + exit(-1) + + +@main.command(help='Export DiffSinger variance model to ONNX format.') +@click.option( + '--exp', type=click.STRING, + required=True, metavar='EXP', callback=lambda ctx, param, value: find_exp(value), + help='Choose an experiment to export.' +) +@click.option( + '--ckpt', type=click.IntRange(min=0), + required=False, metavar='STEPS', + help='Checkpoint training steps.' +) +@click.option( + '--out', type=click.Path( + dir_okay=True, file_okay=False, + path_type=pathlib.Path, resolve_path=True + ), + required=False, + help='Output directory for the artifacts.' +) +@click.option( + '--freeze_glide', is_flag=True, + help='Freeze default glide embedding into the model.' +) +@click.option( + '--freeze_expr', is_flag=True, + help='Freeze default pitch expressiveness factor into the model.' +) +@click.option( + '--export_spk', type=click.STRING, + required=False, multiple=True, + help='(for multi-speaker models) Export one or more speaker or speaker mixture keys.' +) +@click.option( + '--freeze_spk', type=click.STRING, + required=False, + help='(for multi-speaker models) Freeze one speaker or speaker mixture into the model.' +) +def variance( + exp: str, + ckpt: int = None, + out: str = None, + freeze_glide: bool = False, + freeze_expr: bool = False, + export_spk: List[str] = None, + freeze_spk: str = None +): + # Validate arguments + if export_spk and freeze_spk: + print('--export_spk is exclusive to --freeze_spk.') + exit(-1) + if out is None: + out = root_dir / 'artifacts' / exp + export_spk_mix, freeze_spk_mix = parse_spk_settings(export_spk, freeze_spk) + + # Load configurations + sys.argv = [ + sys.argv[0], + '--exp_name', + exp, + '--infer' + ] + set_hparams() + from deployment.exporters import DiffSingerVarianceExporter + print(f'| Exporter: {DiffSingerVarianceExporter}') + exporter = DiffSingerVarianceExporter( + device=torch.device('cuda' if torch.cuda.is_available() else 'cpu'), + cache_dir=root_dir / 'deployment' / 'cache', + ckpt_steps=ckpt, + freeze_glide=freeze_glide, + freeze_expr=freeze_expr, + export_spk=export_spk_mix, + freeze_spk=freeze_spk_mix + ) + try: + exporter.export(out) + except KeyboardInterrupt: + exit(-1) + + +@main.command(help='Export NSF-HiFiGAN vocoder model to ONNX format.') +@click.option( + '--config', type=click.Path( + exists=True, file_okay=True, dir_okay=False, readable=True, + path_type=pathlib.Path, resolve_path=True + ), + required=True, + help='Specify a configuration file for the vocoder.' +) +@click.option( + '--ckpt', type=click.Path( + exists=True, file_okay=True, dir_okay=False, readable=True, + path_type=pathlib.Path, resolve_path=True + ), + required=False, + help='Specify a model path of the vocoder checkpoint.' +) +@click.option( + '--out', type=click.Path( + dir_okay=True, file_okay=False, + path_type=pathlib.Path, resolve_path=True + ), + required=False, + help='Output directory for the artifacts.' +) +@click.option( + '--name', type=click.STRING, + required=False, default='nsf_hifigan', show_default=False, + help='Specify filename (without suffix) of the target model file.' +) +def nsf_hifigan( + config: pathlib.Path, + ckpt: pathlib.Path = None, + out: pathlib.Path = None, + name: str = None +): + # Check arguments + if out is None: + out = root_dir / 'artifacts' / 'nsf_hifigan' + + # Load configurations + set_hparams(config.as_posix()) + if ckpt is None: + model_path = pathlib.Path(hparams['vocoder_ckpt']).resolve() + else: + model_path = ckpt + + # Export artifacts + from deployment.exporters import NSFHiFiGANExporter + print(f'| Exporter: {NSFHiFiGANExporter}') + exporter = NSFHiFiGANExporter( + device=torch.device('cuda' if torch.cuda.is_available() else 'cpu'), + cache_dir=root_dir / 'deployment' / 'cache', + model_path=model_path, + model_name=name + ) + try: + exporter.export(out) + except KeyboardInterrupt: + exit(-1) + + +if __name__ == '__main__': + main() diff --git a/scripts/infer.py b/scripts/infer.py new file mode 100644 index 0000000..ae08f5d --- /dev/null +++ b/scripts/infer.py @@ -0,0 +1,381 @@ +import json +import os +import pathlib +import sys +from collections import OrderedDict +from pathlib import Path + +import click +from typing import Tuple + +root_dir = Path(__file__).resolve().parent.parent +os.environ['PYTHONPATH'] = str(root_dir) +sys.path.insert(0, str(root_dir)) + + +def find_exp(exp): + if not (root_dir / 'checkpoints' / exp).exists(): + for subdir in (root_dir / 'checkpoints').iterdir(): + if not subdir.is_dir(): + continue + if subdir.name.startswith(exp): + print(f'| match ckpt by prefix: {subdir.name}') + exp = subdir.name + break + else: + raise click.BadParameter( + f'There are no matching exp starting with \'{exp}\' in \'checkpoints\' folder. ' + 'Please specify \'--exp\' as the folder name or prefix.' + ) + else: + print(f'| found ckpt by name: {exp}') + return exp + + +@click.group() +def main(): + pass + + +@main.command(help='Run DiffSinger acoustic model inference') +@click.argument( + 'proj', type=click.Path( + exists=True, file_okay=True, dir_okay=False, readable=True, + path_type=pathlib.Path, resolve_path=True + ), + metavar='DS_FILE' +) +@click.option( + '--exp', type=str, + required=True, metavar='EXP', + callback=lambda ctx, param, value: find_exp(value), + help='Selection of model' +) +@click.option( + '--ckpt', type=click.IntRange(min=0), + required=False, metavar='STEPS', + help='Selection of checkpoint training steps' +) +@click.option( + '--spk', type=click.STRING, + required=False, + help='Speaker name or mixture of speakers' +) +@click.option( + '--lang', type=click.STRING, + required=False, + help='Default language name' +) +@click.option( + '--out', type=click.Path( + file_okay=False, dir_okay=True, path_type=pathlib.Path + ), + required=False, + help='Path of the output folder' +) +@click.option( + '--title', type=click.STRING, + required=False, + help='Title of output file' +) +@click.option( + '--num', type=click.IntRange(min=1), + required=False, default=1, + help='Number of runs' +) +@click.option( + '--key', type=click.INT, + required=False, default=0, + help='Key transition of pitch' +) +@click.option( + '--gender', type=click.FloatRange(min=-1, max=1), + required=False, + help='Formant shifting (gender control)' +) +@click.option( + '--seed', type=click.INT, + required=False, default=-1, + help='Random seed of the inference' +) +@click.option( + '--depth', type=click.FloatRange(min=0, max=1), + required=False, + help='Shallow diffusion depth' +) +@click.option( + '--steps', type=click.IntRange(min=1), + required=False, + help='Diffusion sampling steps' +) +@click.option( + '--mel', is_flag=True, + help='Save intermediate mel format instead of waveform' +) +def acoustic( + proj: pathlib.Path, + exp: str, + ckpt: int, + spk: str, + lang: str, + out: pathlib.Path, + title: str, + num: int, + key: int, + gender: float, + seed: int, + depth: float, + steps: int, + mel: bool +): + name = proj.stem if not title else title + if out is None: + out = proj.parent + + with open(proj, 'r', encoding='utf-8') as f: + params = json.load(f) + + if not isinstance(params, list): + params = [params] + + if len(params) == 0: + print('The input file is empty.') + exit() + + from utils.infer_utils import trans_key, parse_commandline_spk_mix + + if key != 0: + params = trans_key(params, key) + key_suffix = '%+dkey' % key + if not title: + name += key_suffix + print(f'| key transition: {key:+d}') + + sys.argv = [ + sys.argv[0], + '--exp_name', + exp, + '--infer' + ] + from utils.hparams import set_hparams, hparams + set_hparams() + + # Check for vocoder path + assert mel or (root_dir / hparams['vocoder_ckpt']).exists(), \ + f'Vocoder ckpt \'{hparams["vocoder_ckpt"]}\' not found. ' \ + f'Please put it to the checkpoints directory to run inference.' + + # For compatibility: + # migrate timesteps, K_step, K_step_infer, diff_speedup to time_scale_factor, T_start, T_start_infer, sampling_steps + if 'diff_speedup' not in hparams and 'pndm_speedup' in hparams: + hparams['diff_speedup'] = hparams['pndm_speedup'] + if 'T_start' not in hparams: + hparams['T_start'] = 1 - hparams['K_step'] / hparams['timesteps'] + if 'T_start_infer' not in hparams: + hparams['T_start_infer'] = 1 - hparams['K_step_infer'] / hparams['timesteps'] + if 'sampling_steps' not in hparams: + if hparams['use_shallow_diffusion']: + hparams['sampling_steps'] = hparams['K_step_infer'] // hparams['diff_speedup'] + else: + hparams['sampling_steps'] = hparams['timesteps'] // hparams['diff_speedup'] + if 'time_scale_factor' not in hparams: + hparams['time_scale_factor'] = hparams['timesteps'] + + if depth is not None: + assert depth <= 1 - hparams['T_start'], ( + f"Depth should not be larger than 1 - T_start ({1 - hparams['T_start']})" + ) + hparams['K_step_infer'] = round(hparams['timesteps'] * depth) + hparams['T_start_infer'] = 1 - depth + if steps is not None: + if hparams['use_shallow_diffusion']: + step_size = (1 - hparams['T_start_infer']) / steps + if 'K_step_infer' in hparams: + hparams['diff_speedup'] = round(step_size * hparams['K_step_infer']) + else: + if 'timesteps' in hparams: + hparams['diff_speedup'] = round(hparams['timesteps'] / steps) + hparams['sampling_steps'] = steps + + spk_mix = parse_commandline_spk_mix(spk) if hparams['use_spk_id'] and spk is not None else None + for param in params: + if gender is not None and hparams['use_key_shift_embed']: + param['gender'] = gender + if spk_mix is not None: + param['spk_mix'] = spk_mix + if lang is not None: + param['lang'] = lang + + from inference.ds_acoustic import DiffSingerAcousticInfer + infer_ins = DiffSingerAcousticInfer(load_vocoder=not mel, ckpt_steps=ckpt) + print(f'| Model: {type(infer_ins.model)}') + + try: + infer_ins.run_inference( + params, out_dir=out, title=name, num_runs=num, + spk_mix=spk_mix, seed=seed, save_mel=mel + ) + except KeyboardInterrupt: + exit(-1) + + +@main.command(help='Run DiffSinger variance model inference') +@click.argument( + 'proj', type=click.Path( + exists=True, file_okay=True, dir_okay=False, readable=True, + path_type=pathlib.Path, resolve_path=True + ), + metavar='DS_FILE' +) +@click.option( + '--exp', type=str, + required=True, metavar='EXP', + callback=lambda ctx, param, value: find_exp(value), + help='Selection of model' +) +@click.option( + '--ckpt', type=click.IntRange(min=0), + required=False, metavar='STEPS', + help='Selection of checkpoint training steps' +) +@click.option( + '--predict', type=click.STRING, + multiple=True, metavar='TAGS', + help='Parameters to predict' +) +@click.option( + '--spk', type=click.STRING, + required=False, + help='Speaker name or mixture of speakers' +) +@click.option( + '--lang', type=click.STRING, + required=False, + help='Default language name' +) +@click.option( + '--out', type=click.Path( + file_okay=False, dir_okay=True, path_type=pathlib.Path + ), + required=False, + help='Path of the output folder' +) +@click.option( + '--title', type=click.STRING, + required=False, + help='Title of output file' +) +@click.option( + '--num', type=click.IntRange(min=1), + required=False, default=1, + help='Number of runs' +) +@click.option( + '--key', type=click.INT, + required=False, default=0, + help='Key transition of pitch' +) +@click.option( + '--expr', type=click.FloatRange(min=0, max=1), + required=False, help='Static expressiveness control' +) +@click.option( + '--seed', type=click.INT, + required=False, default=-1, + help='Random seed of the inference' +) +@click.option( + '--steps', type=click.IntRange(min=1), + required=False, + help='Diffusion sampling steps' +) +def variance( + proj: pathlib.Path, + exp: str, + ckpt: int, + spk: str, + lang: str, + predict: Tuple[str], + out: pathlib.Path, + title: str, + num: int, + key: int, + expr: float, + seed: int, + steps: int +): + name = proj.stem if not title else title + if out is None: + out = proj.parent + if (not out or out.resolve() == proj.parent.resolve()) and not title: + name += '_variance' + + with open(proj, 'r', encoding='utf-8') as f: + params = json.load(f) + + if not isinstance(params, list): + params = [params] + params = [OrderedDict(p) for p in params] + + if len(params) == 0: + print('The input file is empty.') + exit() + + from utils.infer_utils import trans_key, parse_commandline_spk_mix + + if key != 0: + params = trans_key(params, key) + key_suffix = '%+dkey' % key + if not title: + name += key_suffix + print(f'| key transition: {key:+d}') + + sys.argv = [ + sys.argv[0], + '--exp_name', + exp, + '--infer' + ] + from utils.hparams import set_hparams, hparams + set_hparams() + + # For compatibility: + # migrate timesteps, K_step, K_step_infer, diff_speedup to time_scale_factor, T_start, T_start_infer, sampling_steps + if 'diff_speedup' not in hparams and 'pndm_speedup' in hparams: + hparams['diff_speedup'] = hparams['pndm_speedup'] + if 'sampling_steps' not in hparams: + hparams['sampling_steps'] = hparams['timesteps'] // hparams['diff_speedup'] + if 'time_scale_factor' not in hparams: + hparams['time_scale_factor'] = hparams['timesteps'] + + if steps is not None: + if 'timesteps' in hparams: + hparams['diff_speedup'] = round(hparams['timesteps'] / steps) + hparams['sampling_steps'] = steps + + spk_mix = parse_commandline_spk_mix(spk) if hparams['use_spk_id'] and spk is not None else None + for param in params: + if expr is not None: + param['expr'] = expr + if spk_mix is not None: + param['ph_spk_mix_backup'] = param.get('ph_spk_mix') + param['spk_mix_backup'] = param.get('spk_mix') + param['ph_spk_mix'] = param['spk_mix'] = spk_mix + if lang is not None: + param['lang'] = lang + + from inference.ds_variance import DiffSingerVarianceInfer + infer_ins = DiffSingerVarianceInfer(ckpt_steps=ckpt, predictions=set(predict)) + print(f'| Model: {type(infer_ins.model)}') + + try: + infer_ins.run_inference( + params, out_dir=out, title=name, + num_runs=num, seed=seed + ) + except KeyboardInterrupt: + exit(-1) + + +if __name__ == '__main__': + main() diff --git a/scripts/train.py b/scripts/train.py new file mode 100644 index 0000000..d4a2236 --- /dev/null +++ b/scripts/train.py @@ -0,0 +1,31 @@ +import importlib +import os + +import sys +from pathlib import Path + +root_dir = Path(__file__).parent.parent.resolve() +os.environ['PYTHONPATH'] = str(root_dir) +sys.path.insert(0, str(root_dir)) + +os.environ['TORCH_CUDNN_V8_API_ENABLED'] = '1' # Prevent unacceptable slowdowns when using 16 precision + +from utils.hparams import set_hparams, hparams + +set_hparams() +if not hparams['nccl_p2p']: + print("Disabling NCCL P2P") + os.environ['NCCL_P2P_DISABLE'] = '1' + + +def run_task(): + assert hparams['task_cls'] != '' + pkg = ".".join(hparams["task_cls"].split(".")[:-1]) + cls_name = hparams["task_cls"].split(".")[-1] + task_cls = getattr(importlib.import_module(pkg), cls_name) + + task_cls.start() + + +if __name__ == '__main__': + run_task() diff --git a/scripts/vocode.py b/scripts/vocode.py new file mode 100644 index 0000000..5586186 --- /dev/null +++ b/scripts/vocode.py @@ -0,0 +1,90 @@ +# coding=utf8 +import argparse +import os +import pathlib +import sys + +root_dir = pathlib.Path(__file__).parent.parent.resolve() +os.environ['PYTHONPATH'] = str(root_dir) +sys.path.insert(0, str(root_dir)) + +import numpy as np +import torch +import tqdm + +from inference.ds_acoustic import DiffSingerAcousticInfer +from utils.infer_utils import cross_fade, save_wav +from utils.hparams import set_hparams, hparams + +parser = argparse.ArgumentParser(description='Run DiffSinger vocoder') +parser.add_argument('mel', type=str, help='Path to the input file') +parser.add_argument('--exp', type=str, required=False, help='Read vocoder class and path from chosen experiment') +parser.add_argument('--config', type=str, required=False, help='Read vocoder class and path from config file') +parser.add_argument('--class', type=str, required=False, help='Specify vocoder class') +parser.add_argument('--ckpt', type=str, required=False, help='Specify vocoder checkpoint path') +parser.add_argument('--out', type=str, required=False, help='Path of the output folder') +parser.add_argument('--title', type=str, required=False, help='Title of output file') +args = parser.parse_args() + +mel = pathlib.Path(args.mel) +name = mel.stem if not args.title else args.title +config = None +if args.exp: + config = root_dir / 'checkpoints' / args.exp / 'config.yaml' +elif args.config: + config = pathlib.Path(args.config) +else: + assert False, 'Either argument \'--exp\' or \'--config\' should be specified.' + +sys.argv = [ + sys.argv[0], + '--config', + str(config) +] +set_hparams(print_hparams=False) + +cls = getattr(args, 'class') +if cls: + hparams['vocoder'] = cls +if args.ckpt: + hparams['vocoder_ckpt'] = args.ckpt + + +out = args.out +if args.out: + out = pathlib.Path(args.out) +else: + out = mel.parent + +mel_seq = torch.load(mel) +assert isinstance(mel_seq, list), 'Not a valid mel sequence.' +assert len(mel_seq) > 0, 'Mel sequence is empty.' + +sample_rate = hparams['audio_sample_rate'] +infer_ins = DiffSingerAcousticInfer(load_model=False) + + +def run_vocoder(path: pathlib.Path): + result = np.zeros(0) + current_length = 0 + + for seg_mel in tqdm.tqdm(mel_seq, desc='mel segment', total=len(mel_seq)): + seg_audio = infer_ins.run_vocoder(seg_mel['mel'].to(infer_ins.device), f0=seg_mel['f0'].to(infer_ins.device)) + seg_audio = seg_audio.squeeze(0).cpu().numpy() + silent_length = round(seg_mel['offset'] * sample_rate) - current_length + if silent_length >= 0: + result = np.append(result, np.zeros(silent_length)) + result = np.append(result, seg_audio) + else: + result = cross_fade(result, seg_audio, current_length + silent_length) + current_length = current_length + silent_length + seg_audio.shape[0] + + print(f'| save audio: {path}') + save_wav(result, path, sample_rate) + + +os.makedirs(out, exist_ok=True) +try: + run_vocoder(out / (name + '.wav')) +except KeyboardInterrupt: + exit(-1) diff --git a/training/acoustic_task.py b/training/acoustic_task.py new file mode 100644 index 0000000..ca6a71c --- /dev/null +++ b/training/acoustic_task.py @@ -0,0 +1,242 @@ +import matplotlib +import torch +import torch.distributions +import torch.optim +import torch.utils.data + +import utils +import utils.infer_utils +from basics.base_dataset import BaseDataset +from basics.base_task import BaseTask +from basics.base_vocoder import BaseVocoder +from modules.aux_decoder import build_aux_loss +from modules.losses import DiffusionLoss, RectifiedFlowLoss +from modules.toplevel import DiffSingerAcoustic, ShallowDiffusionOutput +from modules.vocoders.registry import get_vocoder_cls +from utils.hparams import hparams +from utils.plot import spec_to_figure + +matplotlib.use('Agg') + + +class AcousticDataset(BaseDataset): + def __init__(self, prefix, preload=False): + super(AcousticDataset, self).__init__(prefix, hparams['dataset_size_key'], preload) + self.required_variances = {} # key: variance name, value: padding value + if hparams['use_energy_embed']: + self.required_variances['energy'] = 0.0 + if hparams['use_breathiness_embed']: + self.required_variances['breathiness'] = 0.0 + if hparams['use_voicing_embed']: + self.required_variances['voicing'] = 0.0 + if hparams['use_tension_embed']: + self.required_variances['tension'] = 0.0 + + self.need_key_shift = hparams['use_key_shift_embed'] + self.need_speed = hparams['use_speed_embed'] + self.need_spk_id = hparams['use_spk_id'] + self.need_lang_id = hparams['use_lang_id'] + + def collater(self, samples): + batch = super().collater(samples) + if batch['size'] == 0: + return batch + + tokens = utils.collate_nd([s['tokens'] for s in samples], 0) + f0 = utils.collate_nd([s['f0'] for s in samples], 0.0) + mel2ph = utils.collate_nd([s['mel2ph'] for s in samples], 0) + mel = utils.collate_nd([s['mel'] for s in samples], 0.0) + batch.update({ + 'tokens': tokens, + 'mel2ph': mel2ph, + 'mel': mel, + 'f0': f0, + }) + for v_name, v_pad in self.required_variances.items(): + batch[v_name] = utils.collate_nd([s[v_name] for s in samples], v_pad) + if self.need_key_shift: + batch['key_shift'] = torch.FloatTensor([s['key_shift'] for s in samples])[:, None] + if self.need_speed: + batch['speed'] = torch.FloatTensor([s['speed'] for s in samples])[:, None] + if self.need_spk_id: + spk_ids = torch.LongTensor([s['spk_id'] for s in samples]) + batch['spk_ids'] = spk_ids + if self.need_lang_id: + languages = utils.collate_nd([s['languages'] for s in samples], 0) + batch['languages'] = languages + return batch + + +class AcousticTask(BaseTask): + def __init__(self): + super().__init__() + self.dataset_cls = AcousticDataset + self.diffusion_type = hparams['diffusion_type'] + assert self.diffusion_type in ['ddpm', 'reflow'], f"Unknown diffusion type: {self.diffusion_type}" + self.use_shallow_diffusion = hparams['use_shallow_diffusion'] + if self.use_shallow_diffusion: + self.shallow_args = hparams['shallow_diffusion_args'] + self.train_aux_decoder = self.shallow_args['train_aux_decoder'] + self.train_diffusion = self.shallow_args['train_diffusion'] + + self.use_vocoder = hparams['infer'] or hparams['val_with_vocoder'] + if self.use_vocoder: + self.vocoder: BaseVocoder = get_vocoder_cls(hparams)() + self.logged_gt_wav = set() + self.required_variances = [] + if hparams['use_energy_embed']: + self.required_variances.append('energy') + if hparams['use_breathiness_embed']: + self.required_variances.append('breathiness') + if hparams['use_voicing_embed']: + self.required_variances.append('voicing') + if hparams['use_tension_embed']: + self.required_variances.append('tension') + super()._finish_init() + + def _build_model(self): + return DiffSingerAcoustic( + vocab_size=len(self.phoneme_dictionary), + out_dims=hparams['audio_num_mel_bins'] + ) + + # noinspection PyAttributeOutsideInit + def build_losses_and_metrics(self): + if self.use_shallow_diffusion: + self.aux_mel_loss = build_aux_loss(self.shallow_args['aux_decoder_arch']) + self.lambda_aux_mel_loss = hparams['lambda_aux_mel_loss'] + self.register_validation_loss('aux_mel_loss') + if self.diffusion_type == 'ddpm': + self.mel_loss = DiffusionLoss(loss_type=hparams['main_loss_type']) + elif self.diffusion_type == 'reflow': + self.mel_loss = RectifiedFlowLoss( + loss_type=hparams['main_loss_type'], log_norm=hparams['main_loss_log_norm'] + ) + else: + raise ValueError(f"Unknown diffusion type: {self.diffusion_type}") + self.register_validation_loss('mel_loss') + + def run_model(self, sample, infer=False): + txt_tokens = sample['tokens'] # [B, T_ph] + target = sample['mel'] # [B, T_s, M] + mel2ph = sample['mel2ph'] # [B, T_s] + f0 = sample['f0'] + variances = { + v_name: sample[v_name] + for v_name in self.required_variances + } + key_shift = sample.get('key_shift') + speed = sample.get('speed') + + if hparams['use_spk_id']: + spk_embed_id = sample['spk_ids'] + else: + spk_embed_id = None + if hparams['use_lang_id']: + languages = sample['languages'] + else: + languages = None + output: ShallowDiffusionOutput = self.model( + txt_tokens, mel2ph=mel2ph, f0=f0, **variances, + key_shift=key_shift, speed=speed, + spk_embed_id=spk_embed_id, languages=languages, + gt_mel=target, infer=infer + ) + + if infer: + return output + else: + losses = {} + + if output.aux_out is not None: + aux_out = output.aux_out + norm_gt = self.model.aux_decoder.norm_spec(target) + aux_mel_loss = self.lambda_aux_mel_loss * self.aux_mel_loss(aux_out, norm_gt) + losses['aux_mel_loss'] = aux_mel_loss + + non_padding = (mel2ph > 0).unsqueeze(-1).float() + if output.diff_out is not None: + if self.diffusion_type == 'ddpm': + x_recon, x_noise = output.diff_out + mel_loss = self.mel_loss(x_recon, x_noise, non_padding=non_padding) + elif self.diffusion_type == 'reflow': + v_pred, v_gt, t = output.diff_out + mel_loss = self.mel_loss(v_pred, v_gt, t=t, non_padding=non_padding) + else: + raise ValueError(f"Unknown diffusion type: {self.diffusion_type}") + losses['mel_loss'] = mel_loss + + return losses + + def on_train_start(self): + if self.use_vocoder and self.vocoder.get_device() != self.device: + self.vocoder.to_device(self.device) + + def _on_validation_start(self): + if self.use_vocoder and self.vocoder.get_device() != self.device: + self.vocoder.to_device(self.device) + + def _validation_step(self, sample, batch_idx): + losses = self.run_model(sample, infer=False) + if sample['size'] > 0 and min(sample['indices']) < hparams['num_valid_plots']: + mel_out: ShallowDiffusionOutput = self.run_model(sample, infer=True) + for i in range(len(sample['indices'])): + data_idx = sample['indices'][i].item() + if data_idx < hparams['num_valid_plots']: + if self.use_vocoder: + self.plot_wav( + data_idx, sample['mel'][i], + mel_out.aux_out[i] if mel_out.aux_out is not None else None, + mel_out.diff_out[i], + sample['f0'][i] + ) + if mel_out.aux_out is not None: + self.plot_mel(data_idx, sample['mel'][i], mel_out.aux_out[i], 'auxmel') + if mel_out.diff_out is not None: + self.plot_mel(data_idx, sample['mel'][i], mel_out.diff_out[i], 'diffmel') + return losses, sample['size'] + + ############ + # validation plots + ############ + def plot_wav(self, data_idx, gt_mel, aux_mel, diff_mel, f0): + f0_len = self.valid_dataset.metadata['f0'][data_idx] + mel_len = self.valid_dataset.metadata['mel'][data_idx] + gt_mel = gt_mel[:mel_len].unsqueeze(0) + if aux_mel is not None: + aux_mel = aux_mel[:mel_len].unsqueeze(0) + if diff_mel is not None: + diff_mel = diff_mel[:mel_len].unsqueeze(0) + f0 = f0[:f0_len].unsqueeze(0) + if data_idx not in self.logged_gt_wav: + gt_wav = self.vocoder.spec2wav_torch(gt_mel, f0=f0) + self.logger.all_rank_experiment.add_audio( + f'gt_{data_idx}', gt_wav, + sample_rate=hparams['audio_sample_rate'], + global_step=self.global_step + ) + self.logged_gt_wav.add(data_idx) + if aux_mel is not None: + aux_wav = self.vocoder.spec2wav_torch(aux_mel, f0=f0) + self.logger.all_rank_experiment.add_audio( + f'aux_{data_idx}', aux_wav, + sample_rate=hparams['audio_sample_rate'], + global_step=self.global_step + ) + if diff_mel is not None: + diff_wav = self.vocoder.spec2wav_torch(diff_mel, f0=f0) + self.logger.all_rank_experiment.add_audio( + f'diff_{data_idx}', diff_wav, + sample_rate=hparams['audio_sample_rate'], + global_step=self.global_step + ) + + def plot_mel(self, data_idx, gt_spec, out_spec, name_prefix='mel'): + vmin = hparams['mel_vmin'] + vmax = hparams['mel_vmax'] + mel_len = self.valid_dataset.metadata['mel'][data_idx] + spec_cat = torch.cat([(out_spec - gt_spec).abs() + vmin, gt_spec, out_spec], -1) + title_text = f"{self.valid_dataset.metadata['spk_names'][data_idx]} - {self.valid_dataset.metadata['names'][data_idx]}" + self.logger.all_rank_experiment.add_figure(f'{name_prefix}_{data_idx}', spec_to_figure( + spec_cat[:mel_len], vmin, vmax, title_text + ), global_step=self.global_step) diff --git a/training/variance_task.py b/training/variance_task.py new file mode 100644 index 0000000..646d954 --- /dev/null +++ b/training/variance_task.py @@ -0,0 +1,337 @@ +import matplotlib +import torch +import torch.distributions +import torch.optim +import torch.utils.data + +import utils +import utils.infer_utils +from basics.base_dataset import BaseDataset +from basics.base_task import BaseTask +from modules.losses import DurationLoss, DiffusionLoss, RectifiedFlowLoss +from modules.metrics import ( + RawCurveAccuracy, RawCurveR2Score, RhythmCorrectness, PhonemeDurationAccuracy +) +from modules.toplevel import DiffSingerVariance +from utils.hparams import hparams +from utils.plot import dur_to_figure, pitch_note_to_figure, curve_to_figure + +matplotlib.use('Agg') + + +class VarianceDataset(BaseDataset): + def __init__(self, prefix, preload=False): + super(VarianceDataset, self).__init__(prefix, hparams['dataset_size_key'], preload) + need_energy = hparams['predict_energy'] + need_breathiness = hparams['predict_breathiness'] + need_voicing = hparams['predict_voicing'] + need_tension = hparams['predict_tension'] + self.predict_variances = need_energy or need_breathiness or need_voicing or need_tension + + def collater(self, samples): + batch = super().collater(samples) + if batch['size'] == 0: + return batch + + tokens = utils.collate_nd([s['tokens'] for s in samples], 0) + ph_dur = utils.collate_nd([s['ph_dur'] for s in samples], 0) + batch.update({ + 'tokens': tokens, + 'ph_dur': ph_dur + }) + + if hparams['use_spk_id']: + batch['spk_ids'] = torch.LongTensor([s['spk_id'] for s in samples]) + if hparams['use_lang_id']: + batch['languages'] = utils.collate_nd([s['languages'] for s in samples], 0) + if hparams['predict_dur']: + batch['ph2word'] = utils.collate_nd([s['ph2word'] for s in samples], 0) + batch['midi'] = utils.collate_nd([s['midi'] for s in samples], 0) + if hparams['predict_pitch']: + batch['note_midi'] = utils.collate_nd([s['note_midi'] for s in samples], -1) + batch['note_rest'] = utils.collate_nd([s['note_rest'] for s in samples], True) + batch['note_dur'] = utils.collate_nd([s['note_dur'] for s in samples], 0) + if hparams['use_glide_embed']: + batch['note_glide'] = utils.collate_nd([s['note_glide'] for s in samples], 0) + batch['mel2note'] = utils.collate_nd([s['mel2note'] for s in samples], 0) + batch['base_pitch'] = utils.collate_nd([s['base_pitch'] for s in samples], 0) + if hparams['predict_pitch'] or self.predict_variances: + batch['mel2ph'] = utils.collate_nd([s['mel2ph'] for s in samples], 0) + batch['pitch'] = utils.collate_nd([s['pitch'] for s in samples], 0) + batch['uv'] = utils.collate_nd([s['uv'] for s in samples], True) + if hparams['predict_energy']: + batch['energy'] = utils.collate_nd([s['energy'] for s in samples], 0) + if hparams['predict_breathiness']: + batch['breathiness'] = utils.collate_nd([s['breathiness'] for s in samples], 0) + if hparams['predict_voicing']: + batch['voicing'] = utils.collate_nd([s['voicing'] for s in samples], 0) + if hparams['predict_tension']: + batch['tension'] = utils.collate_nd([s['tension'] for s in samples], 0) + + return batch + + +def random_retake_masks(b, t, device): + # 1/4 segments are True in average + B_masks = torch.randint(low=0, high=4, size=(b, 1), dtype=torch.long, device=device) == 0 + # 1/3 frames are True in average + T_masks = utils.random_continuous_masks(b, t, dim=1, device=device) + # 1/4 segments and 1/2 frames are True in average (1/4 + 3/4 * 1/3 = 1/2) + return B_masks | T_masks + + +class VarianceTask(BaseTask): + def __init__(self): + super().__init__() + self.dataset_cls = VarianceDataset + + self.diffusion_type = hparams['diffusion_type'] + + self.use_spk_id = hparams['use_spk_id'] + self.use_lang_id = hparams['use_lang_id'] + + self.predict_dur = hparams['predict_dur'] + if self.predict_dur: + self.lambda_dur_loss = hparams['lambda_dur_loss'] + + self.predict_pitch = hparams['predict_pitch'] + if self.predict_pitch: + self.lambda_pitch_loss = hparams['lambda_pitch_loss'] + + predict_energy = hparams['predict_energy'] + predict_breathiness = hparams['predict_breathiness'] + predict_voicing = hparams['predict_voicing'] + predict_tension = hparams['predict_tension'] + self.variance_prediction_list = [] + if predict_energy: + self.variance_prediction_list.append('energy') + if predict_breathiness: + self.variance_prediction_list.append('breathiness') + if predict_voicing: + self.variance_prediction_list.append('voicing') + if predict_tension: + self.variance_prediction_list.append('tension') + self.predict_variances = len(self.variance_prediction_list) > 0 + self.lambda_var_loss = hparams['lambda_var_loss'] + super()._finish_init() + + def _build_model(self): + return DiffSingerVariance( + vocab_size=len(self.phoneme_dictionary), + ) + + # noinspection PyAttributeOutsideInit + def build_losses_and_metrics(self): + if self.predict_dur: + dur_hparams = hparams['dur_prediction_args'] + self.dur_loss = DurationLoss( + offset=dur_hparams['log_offset'], + loss_type=dur_hparams['loss_type'], + lambda_pdur=dur_hparams['lambda_pdur_loss'], + lambda_wdur=dur_hparams['lambda_wdur_loss'], + lambda_sdur=dur_hparams['lambda_sdur_loss'] + ) + self.register_validation_loss('dur_loss') + self.register_validation_metric('rhythm_corr', RhythmCorrectness(tolerance=0.05)) + self.register_validation_metric('ph_dur_acc', PhonemeDurationAccuracy(tolerance=0.2)) + if self.predict_pitch: + if self.diffusion_type == 'ddpm': + self.pitch_loss = DiffusionLoss(loss_type=hparams['main_loss_type']) + elif self.diffusion_type == 'reflow': + self.pitch_loss = RectifiedFlowLoss( + loss_type=hparams['main_loss_type'], log_norm=hparams['main_loss_log_norm'] + ) + else: + raise ValueError(f'Unknown diffusion type: {self.diffusion_type}') + self.register_validation_loss('pitch_loss') + self.register_validation_metric('pitch_acc', RawCurveAccuracy(tolerance=0.5)) + self.register_validation_metric('pitch_r2', RawCurveR2Score()) + if self.predict_variances: + if self.diffusion_type == 'ddpm': + self.var_loss = DiffusionLoss(loss_type=hparams['main_loss_type']) + elif self.diffusion_type == 'reflow': + self.var_loss = RectifiedFlowLoss( + loss_type=hparams['main_loss_type'], log_norm=hparams['main_loss_log_norm'] + ) + else: + raise ValueError(f'Unknown diffusion type: {self.diffusion_type}') + self.register_validation_loss('var_loss') + for name in self.variance_prediction_list: + self.register_validation_metric(f'{name}_r2', RawCurveR2Score()) + + def run_model(self, sample, infer=False): + spk_ids = sample['spk_ids'] if self.use_spk_id else None # [B,] + languages = sample['languages'] if self.use_lang_id else None # [B,] + txt_tokens = sample['tokens'] # [B, T_ph] + ph_dur = sample['ph_dur'] # [B, T_ph] + ph2word = sample.get('ph2word') # [B, T_ph] + midi = sample.get('midi') # [B, T_ph] + mel2ph = sample.get('mel2ph') # [B, T_s] + + note_midi = sample.get('note_midi') # [B, T_n] + note_rest = sample.get('note_rest') # [B, T_n] + note_dur = sample.get('note_dur') # [B, T_n] + note_glide = sample.get('note_glide') # [B, T_n] + mel2note = sample.get('mel2note') # [B, T_s] + + base_pitch = sample.get('base_pitch') # [B, T_s] + pitch = sample.get('pitch') # [B, T_s] + energy = sample.get('energy') # [B, T_s] + breathiness = sample.get('breathiness') # [B, T_s] + voicing = sample.get('voicing') # [B, T_s] + tension = sample.get('tension') # [B, T_s] + + pitch_retake = variance_retake = None + if (self.predict_pitch or self.predict_variances) and not infer: + # randomly select continuous retaking regions + b = sample['size'] + t = mel2ph.shape[1] + device = mel2ph.device + if self.predict_pitch: + pitch_retake = random_retake_masks(b, t, device) + if self.predict_variances: + variance_retake = { + v_name: random_retake_masks(b, t, device) + for v_name in self.variance_prediction_list + } + + output = self.model( + txt_tokens, languages=languages, + midi=midi, ph2word=ph2word, + ph_dur=ph_dur, mel2ph=mel2ph, + note_midi=note_midi, note_rest=note_rest, + note_dur=note_dur, note_glide=note_glide, mel2note=mel2note, + base_pitch=base_pitch, pitch=pitch, + energy=energy, breathiness=breathiness, voicing=voicing, tension=tension, + pitch_retake=pitch_retake, variance_retake=variance_retake, + spk_id=spk_ids, infer=infer + ) + + dur_pred, pitch_pred, variances_pred = output + if infer: + if dur_pred is not None: + dur_pred = dur_pred.round().long() + return dur_pred, pitch_pred, variances_pred # Tensor, Tensor, Dict[str, Tensor] + else: + losses = {} + if dur_pred is not None: + losses['dur_loss'] = self.lambda_dur_loss * self.dur_loss(dur_pred, ph_dur, ph2word=ph2word) + non_padding = (mel2ph > 0).unsqueeze(-1) if mel2ph is not None else None + if pitch_pred is not None: + if self.diffusion_type == 'ddpm': + pitch_x_recon, pitch_noise = pitch_pred + pitch_loss = self.pitch_loss( + pitch_x_recon, pitch_noise, non_padding=non_padding + ) + elif self.diffusion_type == 'reflow': + pitch_v_pred, pitch_v_gt, t = pitch_pred + pitch_loss = self.pitch_loss( + pitch_v_pred, pitch_v_gt, t=t, non_padding=non_padding + ) + else: + raise ValueError(f"Unknown diffusion type: {self.diffusion_type}") + losses['pitch_loss'] = self.lambda_pitch_loss * pitch_loss + if variances_pred is not None: + if self.diffusion_type == 'ddpm': + var_x_recon, var_noise = variances_pred + var_loss = self.var_loss( + var_x_recon, var_noise, non_padding=non_padding + ) + elif self.diffusion_type == 'reflow': + var_v_pred, var_v_gt, t = variances_pred + var_loss = self.var_loss( + var_v_pred, var_v_gt, t=t, non_padding=non_padding + ) + else: + raise ValueError(f"Unknown diffusion type: {self.diffusion_type}") + losses['var_loss'] = self.lambda_var_loss * var_loss + + return losses + + def _validation_step(self, sample, batch_idx): + losses = self.run_model(sample, infer=False) + if min(sample['indices']) < hparams['num_valid_plots']: + def sample_get(key, idx, abs_idx): + return sample[key][idx][:self.valid_dataset.metadata[key][abs_idx]].unsqueeze(0) + + dur_preds, pitch_preds, variances_preds = self.run_model(sample, infer=True) + for i in range(len(sample['indices'])): + data_idx = sample['indices'][i] + if data_idx < hparams['num_valid_plots']: + if dur_preds is not None: + dur_len = self.valid_dataset.metadata['ph_dur'][data_idx] + tokens = sample_get('tokens', i, data_idx) + gt_dur = sample_get('ph_dur', i, data_idx) + pred_dur = dur_preds[i][:dur_len].unsqueeze(0) + ph2word = sample_get('ph2word', i, data_idx) + mask = tokens != 0 + self.valid_metrics['rhythm_corr'].update( + pdur_pred=pred_dur, pdur_target=gt_dur, ph2word=ph2word, mask=mask + ) + self.valid_metrics['ph_dur_acc'].update( + pdur_pred=pred_dur, pdur_target=gt_dur, ph2word=ph2word, mask=mask + ) + self.plot_dur( + data_idx, gt_dur, pred_dur, + txt=self.valid_dataset.metadata['ph_texts'][data_idx].split() + ) + if pitch_preds is not None: + pitch_len = self.valid_dataset.metadata['pitch'][data_idx] + pred_pitch = sample_get('base_pitch', i, data_idx) + pitch_preds[i][:pitch_len].unsqueeze(0) + gt_pitch = sample_get('pitch', i, data_idx) + mask = (sample_get('mel2ph', i, data_idx) > 0) & ~sample_get('uv', i, data_idx) + self.valid_metrics['pitch_acc'].update(pred=pred_pitch, target=gt_pitch, mask=mask) + self.valid_metrics['pitch_r2'].update(pred=pred_pitch, target=gt_pitch, mask=mask) + self.plot_pitch( + data_idx, + gt_pitch=gt_pitch, + pred_pitch=pred_pitch, + note_midi=sample_get('note_midi', i, data_idx), + note_dur=sample_get('note_dur', i, data_idx), + note_rest=sample_get('note_rest', i, data_idx) + ) + for name in self.variance_prediction_list: + variance_len = self.valid_dataset.metadata[name][data_idx] + gt_variances = sample[name][i][:variance_len].unsqueeze(0) + pred_variances = variances_preds[name][i][:variance_len].unsqueeze(0) + mask = (sample_get('mel2ph', i, data_idx) > 0) & ~sample_get('uv', i, data_idx) + self.valid_metrics[f'{name}_r2'].update(pred=pred_variances, target=gt_variances, mask=mask) + self.plot_curve( + data_idx, + gt_curve=gt_variances, + pred_curve=pred_variances, + curve_name=name + ) + return losses, sample['size'] + + ############ + # validation plots + ############ + def plot_dur(self, data_idx, gt_dur, pred_dur, txt=None): + gt_dur = gt_dur[0].cpu().numpy() + pred_dur = pred_dur[0].cpu().numpy() + title_text = f"{self.valid_dataset.metadata['spk_names'][data_idx]} - {self.valid_dataset.metadata['names'][data_idx]}" + self.logger.all_rank_experiment.add_figure(f'dur_{data_idx}', dur_to_figure( + gt_dur, pred_dur, txt, title_text + ), self.global_step) + + def plot_pitch(self, data_idx, gt_pitch, pred_pitch, note_midi, note_dur, note_rest): + gt_pitch = gt_pitch[0].cpu().numpy() + pred_pitch = pred_pitch[0].cpu().numpy() + note_midi = note_midi[0].cpu().numpy() + note_dur = note_dur[0].cpu().numpy() + note_rest = note_rest[0].cpu().numpy() + title_text = f"{self.valid_dataset.metadata['spk_names'][data_idx]} - {self.valid_dataset.metadata['names'][data_idx]}" + self.logger.all_rank_experiment.add_figure(f'pitch_{data_idx}', pitch_note_to_figure( + gt_pitch, pred_pitch, note_midi, note_dur, note_rest, title_text + ), self.global_step) + + def plot_curve(self, data_idx, gt_curve, pred_curve, base_curve=None, grid=None, curve_name='curve'): + gt_curve = gt_curve[0].cpu().numpy() + pred_curve = pred_curve[0].cpu().numpy() + if base_curve is not None: + base_curve = base_curve[0].cpu().numpy() + title_text = f"{self.valid_dataset.metadata['spk_names'][data_idx]} - {self.valid_dataset.metadata['names'][data_idx]}" + self.logger.all_rank_experiment.add_figure(f'{curve_name}_{data_idx}', curve_to_figure( + gt_curve, pred_curve, base_curve, grid=grid, title=title_text + ), self.global_step) diff --git a/utils/__init__.py b/utils/__init__.py new file mode 100644 index 0000000..97ee682 --- /dev/null +++ b/utils/__init__.py @@ -0,0 +1,360 @@ +from __future__ import annotations + +import pathlib +import re +import time +import types +from collections import OrderedDict +from fnmatch import fnmatch + +import numpy as np +import torch +import torch.nn.functional as F + +from basics.base_module import CategorizedModule +from utils.hparams import hparams +from utils.training_utils import get_latest_checkpoint_path + + +def tensors_to_scalars(metrics): + new_metrics = {} + for k, v in metrics.items(): + if isinstance(v, torch.Tensor): + v = v.item() + if type(v) is dict: + v = tensors_to_scalars(v) + new_metrics[k] = v + return new_metrics + + +def collate_nd(values, pad_value=0, max_len=None): + """ + Pad a list of Nd tensors on their first dimension and stack them into a (N+1)d tensor. + """ + size = ((max(v.size(0) for v in values) if max_len is None else max_len), *values[0].shape[1:]) + res = torch.full((len(values), *size), fill_value=pad_value, dtype=values[0].dtype, device=values[0].device) + + for i, v in enumerate(values): + res[i, :len(v), ...] = v + return res + + +def random_continuous_masks(*shape: int, dim: int, device: str | torch.device = 'cpu'): + start, end = torch.sort( + torch.randint( + low=0, high=shape[dim] + 1, size=(*shape[:dim], 2, *((1,) * (len(shape) - dim - 1))), device=device + ).expand(*((-1,) * (dim + 1)), *shape[dim + 1:]), dim=dim + )[0].split(1, dim=dim) + idx = torch.arange( + 0, shape[dim], dtype=torch.long, device=device + ).reshape(*((1,) * dim), shape[dim], *((1,) * (len(shape) - dim - 1))) + masks = (idx >= start) & (idx < end) + return masks + + +def _is_batch_full(batch, num_frames, max_batch_frames, max_batch_size): + if len(batch) == 0: + return 0 + if len(batch) == max_batch_size: + return 1 + if num_frames > max_batch_frames: + return 1 + return 0 + + +def batch_by_size( + indices, num_frames_fn, max_batch_frames=80000, max_batch_size=48, + required_batch_size_multiple=1 +): + """ + Yield mini-batches of indices bucketed by size. Batches may contain + sequences of different lengths. + + Args: + indices (List[int]): ordered list of dataset indices + num_frames_fn (callable): function that returns the number of frames at + a given index + max_batch_frames (int, optional): max number of frames in each batch + (default: 80000). + max_batch_size (int, optional): max number of sentences in each + batch (default: 48). + required_batch_size_multiple: require the batch size to be multiple + of a given number + """ + bsz_mult = required_batch_size_multiple + + if isinstance(indices, types.GeneratorType): + indices = np.fromiter(indices, dtype=np.int64, count=-1) + + sample_len = 0 + sample_lens = [] + batch = [] + batches = [] + for i in range(len(indices)): + idx = indices[i] + num_frames = num_frames_fn(idx) + sample_lens.append(num_frames) + sample_len = max(sample_len, num_frames) + assert sample_len <= max_batch_frames, ( + "sentence at index {} of size {} exceeds max_batch_samples " + "limit of {}!".format(idx, sample_len, max_batch_frames) + ) + num_frames = (len(batch) + 1) * sample_len + + if _is_batch_full(batch, num_frames, max_batch_frames, max_batch_size): + mod_len = max( + bsz_mult * (len(batch) // bsz_mult), + len(batch) % bsz_mult, + ) + batches.append(batch[:mod_len]) + batch = batch[mod_len:] + sample_lens = sample_lens[mod_len:] + sample_len = max(sample_lens) if len(sample_lens) > 0 else 0 + batch.append(idx) + if len(batch) > 0: + batches.append(batch) + return batches + + +def make_positions(tensor, padding_idx): + """Replace non-padding symbols with their position numbers. + + Position numbers begin at padding_idx+1. Padding symbols are ignored. + """ + # The series of casts and type-conversions here are carefully + # balanced to both work with ONNX export and XLA. In particular XLA + # prefers ints, cumsum defaults to output longs, and ONNX doesn't know + # how to handle the dtype kwarg in cumsum. + mask = tensor.ne(padding_idx).int() + return (torch.cumsum(mask, dim=1).type_as(mask) * mask).long() + padding_idx + + +def softmax(x, dim): + return F.softmax(x, dim=dim, dtype=torch.float32) + + +def unpack_dict_to_list(samples): + samples_ = [] + bsz = samples.get('outputs').size(0) + for i in range(bsz): + res = {} + for k, v in samples.items(): + try: + res[k] = v[i] + except (IndexError, TypeError): + pass + samples_.append(res) + return samples_ + + +def filter_kwargs(dict_to_filter, kwarg_obj): + import inspect + + sig = inspect.signature(kwarg_obj) + if any(param.kind == param.VAR_KEYWORD for param in sig.parameters.values()): + # the signature contains definitions like **kwargs, so there is no need to filter + return dict_to_filter.copy() + filter_keys = [ + param.name + for param in sig.parameters.values() + if param.kind == param.POSITIONAL_OR_KEYWORD or param.kind == param.KEYWORD_ONLY + ] + filtered_dict = {filter_key: dict_to_filter[filter_key] for filter_key in filter_keys if + filter_key in dict_to_filter} + return filtered_dict + + +def load_ckpt( + cur_model, ckpt_base_dir, ckpt_steps=None, + prefix_in_ckpt='model', exclude_key_patterns=None, key_in_ckpt='state_dict', + strict=True, device='cpu' +): + if exclude_key_patterns is None: + # Pop all RoPE buffers from some old checkpoints, + # Because these buffers are all computed during initialization now. + # TODO: this is a legacy handling and should be removed in the future. + exclude_key_patterns = ['*.rotary_embed.*'] + if not isinstance(ckpt_base_dir, pathlib.Path): + ckpt_base_dir = pathlib.Path(ckpt_base_dir) + if ckpt_base_dir.is_file(): + checkpoint_path = [ckpt_base_dir] + elif ckpt_steps is not None: + checkpoint_path = [ckpt_base_dir / f'model_ckpt_steps_{int(ckpt_steps)}.ckpt'] + else: + base_dir = ckpt_base_dir + checkpoint_path = sorted( + [ + ckpt_file + for ckpt_file in base_dir.iterdir() + if ckpt_file.is_file() and re.fullmatch(r'model_ckpt_steps_\d+\.ckpt', ckpt_file.name) + ], + key=lambda x: int(re.search(r'\d+', x.name).group(0)) + ) + assert len(checkpoint_path) > 0, f'| ckpt not found in {ckpt_base_dir}.' + checkpoint_path = checkpoint_path[-1] + ckpt_loaded = torch.load(checkpoint_path, map_location=device) + if isinstance(cur_model, CategorizedModule): + cur_model.check_category(ckpt_loaded.get('category')) + if key_in_ckpt is None: + state_dict = ckpt_loaded + else: + state_dict = ckpt_loaded[key_in_ckpt] + if prefix_in_ckpt is not None: + old_state_dict = state_dict + state_dict = OrderedDict() + for k, v in old_state_dict.items(): + if not k.startswith(f'{prefix_in_ckpt}.'): + continue + k = k[len(prefix_in_ckpt) + 1:] + excluded = False + for pat in exclude_key_patterns: + if fnmatch(k, pat): + excluded = True + break + if excluded: + continue + state_dict[k] = v + + # Manual self-attention (MultiheadSelfAttentionWithRoPE) uses 'in_proj.weight', + # while older checkpoints saved from torch.nn.MultiheadAttention use 'in_proj_weight'. + # The two tensors have identical shape and semantics (Q/K/V stacked along dim 0), + # so a key rename is sufficient to load legacy ckpts. + renamed = OrderedDict() + for k, v in state_dict.items(): + if k.endswith('.self_attn.in_proj_weight'): + k = k[:-len('in_proj_weight')] + 'in_proj.weight' + renamed[k] = v + state_dict = renamed + + if not strict: + cur_model_state_dict = cur_model.state_dict() + unmatched_keys = [] + for key, param in state_dict.items(): + if key in cur_model_state_dict: + new_param = cur_model_state_dict[key] + if new_param.shape != param.shape: + unmatched_keys.append(key) + print('| Unmatched keys: ', key, new_param.shape, param.shape) + for key in unmatched_keys: + del state_dict[key] + cur_model.load_state_dict(state_dict, strict=strict) + shown_model_name = 'state dict' + if prefix_in_ckpt is not None: + shown_model_name = f'\'{prefix_in_ckpt}\'' + elif key_in_ckpt is not None: + shown_model_name = f'\'{key_in_ckpt}\'' + print(f'| load {shown_model_name} from \'{checkpoint_path}\'.') + + +def remove_padding(x, padding_idx=0): + if x is None: + return None + assert len(x.shape) in [1, 2] + if len(x.shape) == 2: # [T, H] + return x[np.abs(x).sum(-1) != padding_idx] + elif len(x.shape) == 1: # [T] + return x[x != padding_idx] + + +class Timer: + timer_map = {} + + def __init__(self, name, print_time=False): + if name not in Timer.timer_map: + Timer.timer_map[name] = 0 + self.name = name + self.print_time = print_time + + def __enter__(self): + self.t = time.time() + + def __exit__(self, exc_type, exc_val, exc_tb): + Timer.timer_map[self.name] += time.time() - self.t + if self.print_time: + print(self.name, Timer.timer_map[self.name]) + + +def print_arch(model, model_name='model'): + print(f"| {model_name} Arch: ", model) + # num_params(model, model_name=model_name) + + +def num_params(model, print_out=True, model_name="model"): + parameters = filter(lambda p: p.requires_grad, model.parameters()) + parameters = sum([np.prod(p.size()) for p in parameters]) / 1_000_000 + if print_out: + print(f'| {model_name} Trainable Parameters: %.3fM' % parameters) + return parameters + + +def build_object_from_class_name(cls_str, parent_cls, *args, **kwargs): + import importlib + + pkg = ".".join(cls_str.split(".")[:-1]) + cls_name = cls_str.split(".")[-1] + cls_type = getattr(importlib.import_module(pkg), cls_name) + if parent_cls is not None: + assert issubclass(cls_type, parent_cls), f'| {cls_type} is not subclass of {parent_cls}.' + + return cls_type(*args, **filter_kwargs(kwargs, cls_type)) + + +def build_lr_scheduler_from_config(optimizer, scheduler_args): + try: + # PyTorch 2.0+ + from torch.optim.lr_scheduler import LRScheduler as LRScheduler + except ImportError: + # PyTorch 1.X + from torch.optim.lr_scheduler import _LRScheduler as LRScheduler + + def helper(params): + if isinstance(params, list): + return [helper(s) for s in params] + elif isinstance(params, dict): + resolved = {k: helper(v) for k, v in params.items()} + if 'cls' in resolved: + if ( + resolved["cls"] == "torch.optim.lr_scheduler.ChainedScheduler" + and scheduler_args["scheduler_cls"] == "torch.optim.lr_scheduler.SequentialLR" + ): + raise ValueError("ChainedScheduler cannot be part of a SequentialLR.") + resolved['optimizer'] = optimizer + obj = build_object_from_class_name( + resolved['cls'], + LRScheduler, + **resolved + ) + return obj + return resolved + else: + return params + + resolved = helper(scheduler_args) + resolved['optimizer'] = optimizer + return build_object_from_class_name( + scheduler_args['scheduler_cls'], + LRScheduler, + **resolved + ) + + +def simulate_lr_scheduler(optimizer_args, scheduler_args, step_count, num_param_groups=1): + optimizer_cls = optimizer_args['optimizer_cls'] + optimizer = build_object_from_class_name( + 'torch.optim.AdamW' if optimizer_cls == 'modules.optimizer.muon.Muon_AdamW' else optimizer_cls, + torch.optim.Optimizer, + [{'params': torch.nn.Parameter(), 'initial_lr': optimizer_args['lr']} for _ in range(num_param_groups)], + **optimizer_args + ) + scheduler = build_lr_scheduler_from_config(optimizer, scheduler_args) + scheduler.optimizer._step_count = 1 + for _ in range(step_count): + scheduler.step() + return scheduler.state_dict() + + +def remove_suffix(string: str, suffix: str): + # Just for Python 3.8 compatibility, since `str.removesuffix()` API of is available since Python 3.9 + if string.endswith(suffix): + string = string[:-len(suffix)] + return string diff --git a/utils/binarizer_utils.py b/utils/binarizer_utils.py new file mode 100644 index 0000000..940d14a --- /dev/null +++ b/utils/binarizer_utils.py @@ -0,0 +1,229 @@ +from typing import Union + +import librosa +import numpy as np +import parselmouth +import torch + +from modules.nsf_hifigan.nvSTFT import STFT +from utils.decomposed_waveform import DecomposedWaveform +from utils.pitch_utils import interp_f0 + + +def get_mel_torch( + waveform, samplerate, + *, + num_mel_bins=128, hop_size=512, win_size=2048, fft_size=2048, + fmin=40, fmax=16000, + keyshift=0, speed=1, device=None +): + if device is None: + device = 'cuda' if torch.cuda.is_available() else 'cpu' + stft = STFT(samplerate, num_mel_bins, fft_size, win_size, hop_size, fmin, fmax, device=device) + with torch.no_grad(): + wav_torch = torch.from_numpy(waveform).to(device) + mel_torch = stft.get_mel(wav_torch.unsqueeze(0), keyshift=keyshift, speed=speed).squeeze(0).T + return mel_torch.cpu().numpy() + + +@torch.no_grad() +def get_mel2ph_torch(lr, durs, length, timestep, device='cpu'): + ph_acc = torch.round(torch.cumsum(durs.to(device), dim=0) / timestep + 0.5).long() + ph_dur = torch.diff(ph_acc, dim=0, prepend=torch.LongTensor([0]).to(device)) + mel2ph = lr(ph_dur[None])[0] + num_frames = mel2ph.shape[0] + if num_frames < length: + mel2ph = torch.cat((mel2ph, torch.full((length - num_frames,), fill_value=mel2ph[-1], device=device)), dim=0) + elif num_frames > length: + mel2ph = mel2ph[:length] + return mel2ph + + +def get_pitch_parselmouth( + waveform, samplerate, length, + *, hop_size, f0_min=65, f0_max=1100, + speed=1, interp_uv=False +): + """ + + :param waveform: [T] + :param samplerate: sampling rate + :param length: Expected number of frames + :param hop_size: Frame width, in number of samples + :param f0_min: Minimum f0 in Hz + :param f0_max: Maximum f0 in Hz + :param speed: Change the speed + :param interp_uv: Interpolate unvoiced parts + :return: f0, uv + """ + hop_size = int(np.round(hop_size * speed)) + time_step = hop_size / samplerate + + l_pad = int(np.ceil(1.5 / f0_min * samplerate)) + r_pad = hop_size * ((len(waveform) - 1) // hop_size + 1) - len(waveform) + l_pad + 1 + waveform = np.pad(waveform, (l_pad, r_pad)) + + # noinspection PyArgumentList + s = parselmouth.Sound(waveform, sampling_frequency=samplerate).to_pitch_ac( + time_step=time_step, voicing_threshold=0.6, + pitch_floor=f0_min, pitch_ceiling=f0_max + ) + assert np.abs(s.t1 - 1.5 / f0_min) < 0.001 + f0 = s.selected_array['frequency'].astype(np.float32) + if len(f0) < length: + f0 = np.pad(f0, (0, length - len(f0))) + f0 = f0[: length] + uv = f0 == 0 + if interp_uv: + f0, uv = interp_f0(f0, uv) + return f0, uv + + +def get_energy_librosa(waveform, length, *, hop_size, win_size, domain='db'): + """ + Definition of energy: RMS of the waveform, in dB representation + :param waveform: [T] + :param length: Expected number of frames + :param hop_size: Frame width, in number of samples + :param win_size: Window size, in number of samples + :param domain: db or amplitude + :return: energy + """ + energy = librosa.feature.rms(y=waveform, frame_length=win_size, hop_length=hop_size)[0] + if len(energy) < length: + energy = np.pad(energy, (0, length - len(energy))) + energy = energy[: length] + if domain == 'db': + energy = librosa.amplitude_to_db(energy) + elif domain == 'amplitude': + pass + else: + raise ValueError(f'Invalid domain: {domain}') + return energy + + +def get_breathiness( + waveform: Union[np.ndarray, DecomposedWaveform], + samplerate, f0, length, + *, hop_size=None, fft_size=None, win_size=None +): + """ + Definition of breathiness: RMS of the aperiodic part, in dB representation + :param waveform: All other analysis parameters will not take effect if a DeconstructedWaveform is given + :param samplerate: sampling rate + :param f0: reference f0 + :param length: Expected number of frames + :param hop_size: Frame width, in number of samples + :param fft_size: Number of fft bins + :param win_size: Window size, in number of samples + :return: breathiness + """ + if not isinstance(waveform, DecomposedWaveform): + waveform = DecomposedWaveform( + waveform=waveform, samplerate=samplerate, f0=f0, + hop_size=hop_size, fft_size=fft_size, win_size=win_size + ) + waveform_ap = waveform.aperiodic() + breathiness = get_energy_librosa( + waveform_ap, length=length, + hop_size=waveform.hop_size, win_size=waveform.win_size + ) + return breathiness + + +def get_voicing( + waveform: Union[np.ndarray, DecomposedWaveform], + samplerate, f0, length, + *, hop_size=None, fft_size=None, win_size=None +): + """ + Definition of voicing: RMS of the harmonic part, in dB representation + :param waveform: All other analysis parameters will not take effect if a DeconstructedWaveform is given + :param samplerate: sampling rate + :param f0: reference f0 + :param length: Expected number of frames + :param hop_size: Frame width, in number of samples + :param fft_size: Number of fft bins + :param win_size: Window size, in number of samples + :return: voicing + """ + if not isinstance(waveform, DecomposedWaveform): + waveform = DecomposedWaveform( + waveform=waveform, samplerate=samplerate, f0=f0, + hop_size=hop_size, fft_size=fft_size, win_size=win_size + ) + waveform_sp = waveform.harmonic() + voicing = get_energy_librosa( + waveform_sp, length=length, + hop_size=waveform.hop_size, win_size=waveform.win_size + ) + return voicing + + +def get_tension_base_harmonic( + waveform: Union[np.ndarray, DecomposedWaveform], + samplerate, f0, length, + *, hop_size=None, fft_size=None, win_size=None, + domain='logit' +): + """ + Definition of tension: radio of the real harmonic part (harmonic part except the base harmonic) + to the full harmonic part. + :param waveform: All other analysis parameters will not take effect if a DeconstructedWaveform is given + :param samplerate: sampling rate + :param f0: reference f0 + :param length: Expected number of frames + :param hop_size: Frame width, in number of samples + :param fft_size: Number of fft bins + :param win_size: Window size, in number of samples + :param domain: The domain of the final ratio representation. + Can be 'ratio' (the raw ratio), 'db' (log decibel) or 'logit' (the reverse function of sigmoid) + :return: tension + """ + if not isinstance(waveform, DecomposedWaveform): + waveform = DecomposedWaveform( + waveform=waveform, samplerate=samplerate, f0=f0, + hop_size=hop_size, fft_size=fft_size, win_size=win_size + ) + waveform_h = waveform.harmonic() + waveform_base_h = waveform.harmonic(0) + energy_base_h = get_energy_librosa( + waveform_base_h, length, + hop_size=waveform.hop_size, win_size=waveform.win_size, + domain='amplitude' + ) + energy_h = get_energy_librosa( + waveform_h, length, + hop_size=waveform.hop_size, win_size=waveform.win_size, + domain='amplitude' + ) + tension = np.sqrt(np.clip(energy_h ** 2 - energy_base_h ** 2, a_min=0, a_max=None)) / (energy_h + 1e-5) + if domain == 'ratio': + tension = np.clip(tension, a_min=0, a_max=1) + elif domain == 'db': + tension = np.clip(tension, a_min=1e-5, a_max=1) + tension = librosa.amplitude_to_db(tension) + elif domain == 'logit': + tension = np.clip(tension, a_min=1e-4, a_max=1 - 1e-4) + tension = np.log(tension / (1 - tension)) + return tension + + +class SinusoidalSmoothingConv1d(torch.nn.Conv1d): + def __init__(self, kernel_size): + super().__init__( + in_channels=1, + out_channels=1, + kernel_size=max(kernel_size, 1), + bias=False, + padding='same', + padding_mode='replicate' + ) + if kernel_size > 1: + smooth_kernel = torch.sin(torch.from_numpy( + np.linspace(0, 1, kernel_size).astype(np.float32) * np.pi + )) + smooth_kernel /= smooth_kernel.sum() + else: + smooth_kernel = torch.tensor([1.0], dtype=torch.float32) + self.weight.data = smooth_kernel[None, None] diff --git a/utils/decomposed_waveform.py b/utils/decomposed_waveform.py new file mode 100644 index 0000000..cfccb0e --- /dev/null +++ b/utils/decomposed_waveform.py @@ -0,0 +1,282 @@ +from typing import Dict + +import numpy as np +import pyworld as pw +import torch +from torch.nn import functional as F + +from modules.hnsep.vr import load_sep_model +from utils import hparams +from utils.pitch_utils import interp_f0 + + +class DecomposedWaveform: + def __new__( + cls, waveform, samplerate, f0, + *, + hop_size=None, fft_size=None, win_size=None, + algorithm='world', device=None + ): + if algorithm == 'world': + obj = object.__new__(DecomposedWaveformPyWorld) + # noinspection PyProtectedMember + obj._init( + waveform=waveform, samplerate=samplerate, f0=f0, + hop_size=hop_size, fft_size=fft_size, win_size=win_size, + device=device + ) + elif algorithm == 'vr': + obj = object.__new__(DecomposedWaveformVocalRemover) + hnsep_ckpt = hparams['hnsep_ckpt'] + # noinspection PyProtectedMember + obj._init( + waveform=waveform, samplerate=samplerate, f0=f0, hop_size=hop_size, + fft_size=fft_size, win_size=win_size, model_path=hnsep_ckpt, + device=device + ) + else: + raise ValueError(f" [x] Unknown harmonic-noise separator: {algorithm}") + return obj + + @property + def samplerate(self): + raise NotImplementedError() + + @property + def hop_size(self): + raise NotImplementedError() + + @property + def fft_size(self): + raise NotImplementedError() + + @property + def win_size(self): + raise NotImplementedError() + + def harmonic(self, k: int = None) -> np.ndarray: + raise NotImplementedError() + + def aperiodic(self) -> np.ndarray: + raise NotImplementedError() + + +class DecomposedWaveformPyWorld(DecomposedWaveform): + def _init( + self, waveform, samplerate, f0, # basic parameters + *, + hop_size=None, fft_size=None, win_size=None, base_harmonic_radius=3.5, # analysis parameters + device=None # computation parameters + ): + # the source components + self._waveform = waveform + self._samplerate = samplerate + self._f0 = f0 + # extraction parameters + self._hop_size = hop_size + self._fft_size = fft_size if fft_size is not None else win_size + self._win_size = win_size if win_size is not None else win_size + self._time_step = hop_size / samplerate + self._half_width = base_harmonic_radius + self._device = ('cuda' if torch.cuda.is_available() else 'cpu') if device is None else device + # intermediate variables + self._f0_world = None + self._sp = None + self._ap = None + # final components + self._harmonic_part: np.ndarray = None + self._aperiodic_part: np.ndarray = None + self._harmonics: Dict[int, np.ndarray] = {} + + @property + def samplerate(self): + return self._samplerate + + @property + def hop_size(self): + return self._hop_size + + @property + def fft_size(self): + return self._fft_size + + @property + def win_size(self): + return self._win_size + + def _world_extraction(self): + # Add a tiny noise to the signal to avoid NaN results of D4C in rare edge cases + # References: + # - https://github.com/JeremyCCHsu/Python-Wrapper-for-World-Vocoder/issues/50 + # - https://github.com/mmorise/World/issues/116 + x = self._waveform.astype(np.double) + np.random.randn(*self._waveform.shape) * 1e-5 + samplerate = self._samplerate + f0 = self._f0.astype(np.double) + + hop_size = self._hop_size + fft_size = self._fft_size + + wav_frames = (x.shape[0] + hop_size - 1) // hop_size + f0_frames = f0.shape[0] + if f0_frames < wav_frames: + f0 = np.pad(f0, (0, wav_frames - f0_frames), mode='constant', constant_values=(f0[0], f0[-1])) + elif f0_frames > wav_frames: + f0 = f0[:wav_frames] + + time_step = hop_size / samplerate + t = np.arange(0, wav_frames) * time_step + self._f0_world = f0 + self._sp = pw.cheaptrick(x, f0, t, samplerate, fft_size=fft_size) # extract smoothed spectrogram + self._ap = pw.d4c(x, f0, t, samplerate, fft_size=fft_size) # extract aperiodicity + + def _kth_harmonic(self, k: int) -> np.ndarray: + """ + Extract the Kth harmonic (starting from 0) from the waveform. Author: @yxlllc + :param k: a non-negative integer + :return: kth_harmonic float32[T] + """ + if k in self._harmonics: + return self._harmonics[k] + + hop_size = self._hop_size + win_size = self._win_size + samplerate = self._samplerate + half_width = self._half_width + device = self._device + + waveform = torch.from_numpy(self.harmonic()).unsqueeze(0).to(device) # [B, n_samples] + n_samples = waveform.shape[1] + f0 = self._f0 * (k + 1) + pad_size = int(n_samples // hop_size) - len(f0) + 1 + if pad_size > 0: + f0 = np.pad(f0, (0, pad_size), mode='constant', constant_values=(f0[0], f0[-1])) + + f0, _ = interp_f0(f0, uv=f0 == 0) + f0 = torch.from_numpy(f0).to(device)[None, :, None] # [B, n_frames, 1] + n_f0_frames = f0.shape[1] + + phase = torch.arange(win_size, dtype=waveform.dtype, device=device) / win_size * 2 * np.pi + nuttall_window = ( + 0.355768 + - 0.487396 * torch.cos(phase) + + 0.144232 * torch.cos(2 * phase) + - 0.012604 * torch.cos(3 * phase) + ) + spec = torch.stft( + waveform, + n_fft=win_size, + win_length=win_size, + hop_length=hop_size, + window=nuttall_window, + center=True, + return_complex=True + ).permute(0, 2, 1) # [B, n_frames, n_spec] + n_spec_frames, n_specs = spec.shape[1:] + idx = torch.arange(n_specs).unsqueeze(0).unsqueeze(0).to(f0) # [1, 1, n_spec] + center = f0 * win_size / samplerate + start = torch.clip(center - half_width, min=0) + end = torch.clip(center + half_width, max=n_specs) + idx_mask = (center >= 1) & (idx >= start) & (idx < end) # [B, n_frames, n_spec] + if n_f0_frames < n_spec_frames: + idx_mask = F.pad(idx_mask, [0, 0, 0, n_spec_frames - n_f0_frames]) + spec = spec * idx_mask[:, :n_spec_frames, :] + self._harmonics[k] = torch.istft( + spec.permute(0, 2, 1), + n_fft=win_size, + win_length=win_size, + hop_length=hop_size, + window=nuttall_window, + center=True, + length=n_samples + ).squeeze(0).cpu().numpy() + + return self._harmonics[k] + + def harmonic(self, k: int = None) -> np.ndarray: + """ + Extract the full harmonic part, or the Kth harmonic if `k` is not None, from the waveform. + :param k: an integer representing the harmonic index, starting from 0 + :return: full_harmonics float32[T] or kth_harmonic float32[T] + """ + if k is not None: + return self._kth_harmonic(k) + if self._harmonic_part is not None: + return self._harmonic_part + if self._sp is None or self._ap is None: + self._world_extraction() + # noinspection PyAttributeOutsideInit + self._harmonic_part = pw.synthesize( + self._f0_world, + np.clip(self._sp * (1 - self._ap * self._ap), a_min=1e-16, a_max=None), # clip to avoid zeros + np.zeros_like(self._ap), + self._samplerate, frame_period=self._time_step * 1000 + ).astype(np.float32) # synthesize the harmonic part using the parameters + return self._harmonic_part + + def aperiodic(self) -> np.ndarray: + """ + Extract the aperiodic part from the waveform. + :return: aperiodic_part float32[T] + """ + if self._aperiodic_part is not None: + return self._aperiodic_part + if self._sp is None or self._ap is None: + self._world_extraction() + # noinspection PyAttributeOutsideInit + self._aperiodic_part = pw.synthesize( + self._f0_world, self._sp * self._ap * self._ap, np.ones_like(self._ap), + self._samplerate, frame_period=self._time_step * 1000 + ).astype(np.float32) # synthesize the aperiodic part using the parameters + return self._aperiodic_part + + +SEP_MODEL = None + + +class DecomposedWaveformVocalRemover(DecomposedWaveformPyWorld): + def _init( + self, waveform, samplerate, f0, + hop_size=None, fft_size=None, win_size=None, base_harmonic_radius=3.5, + model_path=None, device=None + ): + super()._init( + waveform, samplerate, f0, hop_size=hop_size, fft_size=fft_size, + win_size=win_size, base_harmonic_radius=base_harmonic_radius, device=device + ) + global SEP_MODEL + if SEP_MODEL is None: + SEP_MODEL = load_sep_model(model_path, self._device) + self.sep_model = SEP_MODEL + + def _infer(self): + with torch.no_grad(): + x = torch.from_numpy(self._waveform).to(self._device).reshape(1, 1, -1) + if not self.sep_model.is_mono: + x = x.repeat(1, 2, 1) + x = self.sep_model.predict_from_audio(x) + x = torch.mean(x, dim=1) + self._harmonic_part = x.squeeze().cpu().numpy() + self._aperiodic_part = self._waveform - self._harmonic_part + + def harmonic(self, k: int = None) -> np.ndarray: + """ + Extract the full harmonic part, or the Kth harmonic if `k` is not None, from the waveform. + :param k: an integer representing the harmonic index, starting from 0 + :return: full_harmonics float32[T] or kth_harmonic float32[T] + """ + if k is not None: + return self._kth_harmonic(k) + if self._harmonic_part is not None: + return self._harmonic_part + self._infer() + return self._harmonic_part + + def aperiodic(self) -> np.ndarray: + """ + Extract the aperiodic part from the waveform. + :return: aperiodic_part float32[T] + """ + if self._aperiodic_part is not None: + return self._aperiodic_part + self._infer() + return self._aperiodic_part diff --git a/utils/hparams.py b/utils/hparams.py new file mode 100644 index 0000000..e339a54 --- /dev/null +++ b/utils/hparams.py @@ -0,0 +1,146 @@ +import argparse +import os +import yaml + +try: + from lightning.pytorch.utilities.rank_zero import rank_zero_only +except ModuleNotFoundError: + def rank_zero_only(f): + return f + +from utils.multiprocess_utils import is_main_process as mp_is_main_process +global_print_hparams = True +hparams = {} + + +class Args: + def __init__(self, **kwargs): + for k, v in kwargs.items(): + self.__setattr__(k, v) + + +def override_config(old_config: dict, new_config: dict): + for k, v in new_config.items(): + if isinstance(v, dict) and k in old_config: + override_config(old_config[k], new_config[k]) + else: + old_config[k] = v + + +def set_hparams(config='', exp_name='', hparams_str='', print_hparams=True, global_hparams=True): + """ + Load hparams from multiple sources: + 1. config chain (i.e. first load base_config, then load config); + 2. if reset == True, load from the (auto-saved) complete config file ('config.yaml') + which contains all settings and do not rely on base_config; + 3. load from argument --hparams or hparams_str, as temporary modification. + """ + if config == '': + parser = argparse.ArgumentParser(description='neural music') + parser.add_argument('--config', type=str, default='', + help='location of the data corpus') + parser.add_argument('--exp_name', type=str, default='', help='exp_name') + parser.add_argument('--hparams', type=str, default='', + help='location of the data corpus') + parser.add_argument('--infer', action='store_true', help='infer') + parser.add_argument('--reset', action='store_true', help='reset hparams') + args, unknown = parser.parse_known_args() + + tmp_args_hparams = args.hparams.split(',') if args.hparams.strip() != '' else [] + tmp_args_hparams.extend(hparams_str.split(',') if hparams_str.strip() != '' else []) + args.hparams = ','.join(tmp_args_hparams) + else: + args = Args(config=config, exp_name=exp_name, hparams=hparams_str, + infer=False, reset=False) + + args_work_dir = '' + if args.exp_name != '': + args.work_dir = args.exp_name + args_work_dir = os.path.join('checkpoints', args.work_dir) + + config_chains = [] + loaded_config = set() + + def load_config(config_fn): # deep first + with open(config_fn, encoding='utf-8') as f: + hparams_ = yaml.safe_load(f) + loaded_config.add(config_fn) + if 'base_config' in hparams_: + ret_hparams = {} + if not isinstance(hparams_['base_config'], list): + hparams_['base_config'] = [hparams_['base_config']] + for c in hparams_['base_config']: + if c not in loaded_config: + if c.startswith('.'): + c = f'{os.path.dirname(config_fn)}/{c}' + c = os.path.normpath(c) + override_config(ret_hparams, load_config(c)) + override_config(ret_hparams, hparams_) + else: + ret_hparams = hparams_ + config_chains.append(config_fn) + return ret_hparams + + global hparams + assert args.config != '' or args_work_dir != '', 'Either config or exp name should be specified.' + saved_hparams = {} + ckpt_config_path = os.path.join(args_work_dir, 'config.yaml') + if args_work_dir != '' and os.path.exists(ckpt_config_path): + with open(ckpt_config_path, encoding='utf-8') as f: + saved_hparams.update(yaml.safe_load(f)) + + hparams_ = {} + if args.config != '': + hparams_.update(load_config(args.config)) + + if not args.reset: + hparams_.update(saved_hparams) + hparams_['work_dir'] = args_work_dir + + if args.hparams != "": + for new_hparam in args.hparams.split(","): + if new_hparam.strip() == "": + continue + k, v = new_hparam.split("=") + if k not in hparams_: + hparams_[k] = eval(v) + if v in ['True', 'False'] or type(hparams_[k]) == bool: + hparams_[k] = eval(v) + else: + hparams_[k] = type(hparams_[k])(v) + + @rank_zero_only + def dump_hparams(): + if args_work_dir != '' and (not os.path.exists(ckpt_config_path) or args.reset) and not args.infer: + os.makedirs(hparams_['work_dir'], exist_ok=True) + if mp_is_main_process: + # Only the main process will save the config file + with open(ckpt_config_path, 'w', encoding='utf-8') as f: + hparams_non_recursive = hparams_.copy() + hparams_non_recursive['base_config'] = [] + yaml.safe_dump(hparams_non_recursive, f, allow_unicode=True, encoding='utf-8') + dump_hparams() + + hparams_['infer'] = args.infer + if global_hparams: + hparams.clear() + hparams.update(hparams_) + + if hparams.get('exp_name') is None: + hparams['exp_name'] = args.exp_name + if hparams_.get('exp_name') is None: + hparams_['exp_name'] = args.exp_name + + @rank_zero_only + def print_out_hparams(): + global global_print_hparams + if mp_is_main_process and print_hparams and global_print_hparams and global_hparams: + print('| Hparams chains: ', config_chains) + print('| Hparams: ') + for i, (k, v) in enumerate(sorted(hparams_.items())): + print(f"\033[0;33m{k}\033[0m: {v}, ", end="\n" if i % 5 == 4 else "") + print("") + global_print_hparams = False + print_out_hparams() + + return hparams_ diff --git a/utils/indexed_datasets.py b/utils/indexed_datasets.py new file mode 100644 index 0000000..fea8c81 --- /dev/null +++ b/utils/indexed_datasets.py @@ -0,0 +1,96 @@ +import pathlib +import multiprocessing +from collections import deque + +import h5py +import torch +import numpy as np + + +class IndexedDataset: + def __init__(self, path, prefix, num_cache=0): + super().__init__() + self.path = pathlib.Path(path) / f'{prefix}.data' + if not self.path.exists(): + raise FileNotFoundError(f'IndexedDataset not found: {self.path}') + self.dset = None + self.cache = deque(maxlen=num_cache) + self.num_cache = num_cache + + def check_index(self, i): + if i < 0 or i >= len(self.dset): + raise IndexError('index out of range') + + def __del__(self): + if self.dset: + self.dset.close() + + def __getitem__(self, i): + if self.dset is None: + self.dset = h5py.File(self.path, 'r') + self.check_index(i) + if self.num_cache > 0: + for c in self.cache: + if c[0] == i: + return c[1] + item = {k: v[()].item() if v.shape == () else torch.from_numpy(v[()]) for k, v in self.dset[str(i)].items()} + if self.num_cache > 0: + self.cache.appendleft((i, item)) + return item + + def __len__(self): + if self.dset is None: + self.dset = h5py.File(self.path, 'r') + return len(self.dset) + + +class IndexedDatasetBuilder: + def __init__(self, path, prefix, allowed_attr=None, auto_increment=True): + self.path = pathlib.Path(path) / f'{prefix}.data' + self.prefix = prefix + self.dset = h5py.File(self.path, 'w') + self.counter = 0 + self.auto_increment = auto_increment + if allowed_attr is not None: + self.allowed_attr = set(allowed_attr) + else: + self.allowed_attr = None + + def add_item(self, item, item_no=None): + if self.auto_increment and item_no is not None or not self.auto_increment and item_no is None: + raise ValueError('auto_increment and provided item_no are mutually exclusive') + if self.allowed_attr is not None: + item = { + k: item[k] + for k in self.allowed_attr + if k in item + } + if self.auto_increment: + item_no = self.counter + self.counter += 1 + for k, v in item.items(): + if v is None: + continue + self.dset.create_dataset(f'{item_no}/{k}', data=v) + return item_no + + def finalize(self): + self.dset.close() + + +if __name__ == "__main__": + import random + from tqdm import tqdm + + ds_path = './checkpoints/indexed_ds_example' + size = 100 + items = [{"a": np.random.normal(size=[10000, 10]), + "b": np.random.normal(size=[10000, 10])} for i in range(size)] + builder = IndexedDatasetBuilder(ds_path, 'example') + for i in tqdm(range(size)): + builder.add_item(items[i]) + builder.finalize() + ds = IndexedDataset(ds_path, 'example') + for i in tqdm(range(10000)): + idx = random.randint(0, size - 1) + assert (ds[idx]['a'] == items[idx]['a']).all() diff --git a/utils/infer_utils.py b/utils/infer_utils.py new file mode 100644 index 0000000..7dc32c2 --- /dev/null +++ b/utils/infer_utils.py @@ -0,0 +1,104 @@ +import re + +import librosa +import numpy as np +from scipy.io import wavfile + + +def trans_f0_seq(feature_pit, transform): + feature_pit = feature_pit * 2 ** (transform / 12) + return round(feature_pit, 1) + + +def trans_key(raw_data, key): + warning_tag = False + for i in raw_data: + note_seq_list = i["note_seq"].split(" ") + new_note_seq_list = [] + for note_seq in note_seq_list: + if note_seq != "rest": + new_note_seq = librosa.midi_to_note(librosa.note_to_midi(note_seq) + key, unicode=False) + # new_note_seq = move_key(note_seq, key) + new_note_seq_list.append(new_note_seq) + else: + new_note_seq_list.append(note_seq) + i["note_seq"] = " ".join(new_note_seq_list) + if i.get("f0_seq"): + f0_seq_list = i["f0_seq"].split(" ") + f0_seq_list = [float(x) for x in f0_seq_list] + new_f0_seq_list = [] + for f0_seq in f0_seq_list: + new_f0_seq = trans_f0_seq(f0_seq, key) + new_f0_seq_list.append(str(new_f0_seq)) + i["f0_seq"] = " ".join(new_f0_seq_list) + else: + warning_tag = True + if warning_tag: + print("Warning: parts of f0_seq do not exist, please freeze the pitch line in the editor.\r\n") + return raw_data + + +def resample_align_curve(points: np.ndarray, original_timestep: float, target_timestep: float, align_length: int): + t_max = (len(points) - 1) * original_timestep + curve_interp = np.interp( + np.arange(0, t_max, target_timestep), + original_timestep * np.arange(len(points)), + points + ).astype(points.dtype) + delta_l = align_length - len(curve_interp) + if delta_l < 0: + curve_interp = curve_interp[:align_length] + elif delta_l > 0: + curve_interp = np.concatenate((curve_interp, np.full(delta_l, fill_value=curve_interp[-1])), axis=0) + return curve_interp + + +def parse_commandline_spk_mix(mix: str) -> dict: + """ + Parse speaker mix info from commandline + :param mix: Input like "opencpop" or "opencpop|qixuan" or "opencpop:0.5|qixuan:0.5" + :return: A dict whose keys are speaker names and values are proportions + """ + name_pattern = r'[0-9A-Za-z_-]+' + proportion_pattern = r'\d+(\.\d+)?' + single_pattern = rf'{name_pattern}(:{proportion_pattern})?' + assert re.fullmatch(rf'{single_pattern}(\|{single_pattern})*', mix) is not None, f'Invalid mix pattern: {mix}' + without_proportion = set() + proportion_map = {} + for component in mix.split('|'): + # If already exists + name_and_proportion = component.split(':') + assert name_and_proportion[0] not in without_proportion and name_and_proportion[0] not in proportion_map, \ + f'Duplicate speaker name: {name_and_proportion[0]}' + if ':' in component: + proportion_map[name_and_proportion[0]] = float(name_and_proportion[1]) + else: + without_proportion.add(name_and_proportion[0]) + sum_given_proportions = sum(proportion_map.values()) + assert sum_given_proportions < 1 or len(without_proportion) == 0, \ + 'Proportion of all speakers should be specified if the sum of all given proportions are larger than 1.' + for name in without_proportion: + proportion_map[name] = (1 - sum_given_proportions) / len(without_proportion) + sum_all_proportions = sum(proportion_map.values()) + assert sum_all_proportions > 0, 'Sum of all proportions should be positive.' + for name in proportion_map: + proportion_map[name] /= sum_all_proportions + return proportion_map + + +def cross_fade(a: np.ndarray, b: np.ndarray, idx: int): + result = np.zeros(idx + b.shape[0]) + fade_len = a.shape[0] - idx + np.copyto(dst=result[:idx], src=a[:idx]) + k = np.linspace(0, 1.0, num=fade_len, endpoint=True) + result[idx: a.shape[0]] = (1 - k) * a[idx:] + k * b[: fade_len] + np.copyto(dst=result[a.shape[0]:], src=b[fade_len:]) + return result + + +def save_wav(wav, path, sr, norm=False): + if norm: + wav = wav / np.abs(wav).max() + wav *= 32767 + # proposed by @dsmiller + wavfile.write(path, sr, wav.astype(np.int16)) diff --git a/utils/multiprocess_utils.py b/utils/multiprocess_utils.py new file mode 100644 index 0000000..236d94a --- /dev/null +++ b/utils/multiprocess_utils.py @@ -0,0 +1,52 @@ +import platform +import re +import traceback + +from torch.multiprocessing import Manager, Process, current_process, get_context + +is_main_process = not bool(re.match(r'((.*Process)|(SyncManager)|(.*PoolWorker))-\d+', current_process().name)) + + +def main_process_print(self, *args, sep=' ', end='\n', file=None): + if is_main_process: + print(self, *args, sep=sep, end=end, file=file) + + +def chunked_worker_run(map_func, args, results_queue=None): + for a in args: + # noinspection PyBroadException + try: + res = map_func(*a) + results_queue.put(res) + except KeyboardInterrupt: + break + except Exception: + traceback.print_exc() + results_queue.put(None) + + +def chunked_multiprocess_run(map_func, args, num_workers, q_max_size=1000): + num_jobs = len(args) + if num_jobs < num_workers: + num_workers = num_jobs + + queues = [Manager().Queue(maxsize=q_max_size // num_workers) for _ in range(num_workers)] + if platform.system().lower() != 'windows': + process_creation_func = get_context('spawn').Process + else: + process_creation_func = Process + + workers = [] + for i in range(num_workers): + worker = process_creation_func( + target=chunked_worker_run, args=(map_func, args[i::num_workers], queues[i]), daemon=True + ) + workers.append(worker) + worker.start() + + for i in range(num_jobs): + yield queues[i % num_workers].get() + + for worker in workers: + worker.join() + worker.close() diff --git a/utils/onnx_helper.py b/utils/onnx_helper.py new file mode 100644 index 0000000..5fddcfe --- /dev/null +++ b/utils/onnx_helper.py @@ -0,0 +1,421 @@ +import inspect +import re +from typing import Dict, Tuple, Union, Literal + +import onnx +import torch +from google.protobuf.internal.containers import RepeatedCompositeFieldContainer +from onnx import GraphProto, ModelProto, NodeProto, ValueInfoProto + +__verbose__: bool = True +""" +Whether log information of successful operations +""" + + +# Whether the running torch.onnx.export() exposes the `dynamo` keyword. +# Its presence is fragmented across PyTorch versions: introduced as a +# separate `torch.onnx.dynamo_export` API in 2.1, added as a kwarg on +# `torch.onnx.export` in 2.4, removed in some intermediate releases, and +# reinstated (with a True default) in 2.9. We probe the signature once at +# import time and only pass `dynamo=False` when the kwarg actually exists. +# All ONNX graph surgery in this module is written against the TorchScript +# exporter, so we want to stay on it whenever the choice is offered. +TORCHSCRIPT_EXPORT_KWARGS: Dict[str, object] = ( + {'dynamo': False} + if 'dynamo' in inspect.signature(torch.onnx.export).parameters + else {} +) + + +def _verbose(self, *args, sep=' ', end='\n', file=None): + if __verbose__: + print(self, *args, sep=sep, end=end, file=file) + + +def model_override_io_shapes( + model: ModelProto, + input_shapes: Dict[str, Tuple[Union[str, int]]] = None, + output_shapes: Dict[str, Tuple[Union[str, int]]] = None, +): + """ + Override the shapes of inputs/outputs of the model graph (in-place operation). + :param model: model to perform the operation on + :param input_shapes: a dict with keys as input/output names and values as shape tuples + :param output_shapes: the same as input_shapes + """ + def _override_shapes( + shape_list_old: RepeatedCompositeFieldContainer[ValueInfoProto], + shape_dict_new: Dict[str, Tuple[Union[str, int]]]): + for value_info in shape_list_old: + if value_info.name in shape_dict_new: + name = value_info.name + dims = value_info.type.tensor_type.shape.dim + assert len(shape_dict_new[name]) == len(dims), \ + f'Number of given and existing dimensions mismatch: {name}' + for i, dim in enumerate(shape_dict_new[name]): + if isinstance(dim, int): + dims[i].dim_param = '' + dims[i].dim_value = dim + else: + dims[i].dim_value = 0 + dims[i].dim_param = dim + _verbose(f'| override shape of \'{name}\' with {shape_dict_new[name]}') + + if input_shapes is not None: + _override_shapes(model.graph.input, input_shapes) + if output_shapes is not None: + _override_shapes(model.graph.output, output_shapes) + + +def model_reorder_io_list( + model: ModelProto, + input_or_output: Literal['input', 'output'], + target_name: str, + insert_after_name: str, +): + """ + Reorder the input of the model graph by moving the target input after the specified input (in-place operation). + If the given names are not found, the operation will be ignored. + :param model: model to perform the operation on + :param input_or_output: 'input' or 'output' to specify the list to reorder + :param target_name: the name of the input to be reordered + :param insert_after_name: the name of the input to be inserted after (None for the first) + """ + def _reorder_input(input_list: RepeatedCompositeFieldContainer[ValueInfoProto]): + nonlocal input_or_output + target_idx = -1 + insert_after_idx = -1 + for i, value_info in enumerate(input_list): + if value_info.name == target_name: + target_idx = i + if value_info.name == insert_after_name: + insert_after_idx = i + if target_idx != -1 and insert_after_idx != -1: + target = input_list.pop(target_idx) + input_list.insert(insert_after_idx + 1, target) + _verbose(f'| reorder {input_or_output}: \'{target_name}\' after \'{insert_after_name}\'') + + if input_or_output == 'input': + _reorder_input(model.graph.input) + elif input_or_output == 'output': + _reorder_input(model.graph.output) + else: + raise ValueError('Argument \'input_or_output\' should be either \'input\' or \'output\'.') + + +def model_add_prefixes( + model: ModelProto, + initializer_prefix=None, + value_info_prefix=None, + node_prefix=None, + dim_prefix=None, + ignored_pattern=None, +): + """ + Adds prefixes to names inside the given ONNX model graph, including sub-graphs (in-place operation). + This method is a complete version of the official onnx.compose.add_prefix API, which does not consider sub-graphs. + """ + initializers = set() + value_infos = set() + + def _record_initializers_and_value_infos_recursive(subgraph): + # Record names in current graph + for initializer in subgraph.initializer: + if ignored_pattern is not None and re.match(ignored_pattern, initializer.name): + continue + initializers.add(initializer.name) + for value_info in subgraph.value_info: + if ignored_pattern is not None and re.match(ignored_pattern, value_info.name): + continue + value_infos.add(value_info.name) + for node in subgraph.node: + # For 'If' and 'Loop' nodes, do recording recursively + if node.op_type == 'If': + for attr in node.attribute: + branch = onnx.helper.get_attribute_value(attr) + _record_initializers_and_value_infos_recursive(branch) + elif node.op_type == 'Loop': + for attr in node.attribute: + if attr.name == 'body': + body = onnx.helper.get_attribute_value(attr) + _record_initializers_and_value_infos_recursive(body) + + def _add_prefixes_recursive(subgraph): + # Add prefixes in current graph + if initializer_prefix is not None: + for initializer in subgraph.initializer: + if ignored_pattern is not None and re.match(ignored_pattern, initializer.name): + continue + new_name = initializer_prefix + initializer.name + _verbose('| add prefix:', initializer.name, '->', new_name) + initializer.name = new_name + + for value_info in subgraph.value_info: + if dim_prefix is not None: + for dim in value_info.type.tensor_type.shape.dim: + if dim.dim_param is None or dim.dim_param == '' or \ + ignored_pattern is not None and re.match(ignored_pattern, dim.dim_param): + continue + new_dim_param = dim_prefix + dim.dim_param + _verbose('| add prefix:', dim.dim_param, '->', new_dim_param) + dim.dim_param = new_dim_param + + if value_info_prefix is None or \ + ignored_pattern is not None and re.match(ignored_pattern, value_info.name): + continue + new_name = value_info_prefix + value_info.name + _verbose('| add prefix:', value_info.name, '->', new_name) + value_info.name = new_name + + if node_prefix is not None: + for node in subgraph.node: + if ignored_pattern is not None and re.match(ignored_pattern, node.name): + continue + new_name = node_prefix + node.name + _verbose('| add prefix:', node.name, '->', new_name) + node.name = new_name + + for node in subgraph.node: + # For 'If' and 'Loop' nodes, add prefixes recursively + if node.op_type == 'If': + for attr in node.attribute: + branch = onnx.helper.get_attribute_value(attr) + _add_prefixes_recursive(branch) + elif node.op_type == 'Loop': + for attr in node.attribute: + if attr.name == 'body': + body = onnx.helper.get_attribute_value(attr) + _add_prefixes_recursive(body) + + # For each node, rename its inputs and outputs + for io_list in [node.input, node.output]: + for i, io_value in enumerate(io_list): + if io_value in initializers and initializer_prefix is not None: + new_value = initializer_prefix + io_value + _verbose('| add prefix:', io_value, '->', new_value) + io_list[i] = new_value + if io_value in value_infos and value_info_prefix is not None: + new_value = value_info_prefix + io_value + _verbose('| add prefix:', io_value, '->', new_value) + io_list[i] = new_value + + _record_initializers_and_value_infos_recursive(model.graph) + _add_prefixes_recursive(model.graph) + + +def graph_fold_back_to_squeeze(graph: GraphProto): + """ + Fold the substructures of 'Shape', 'Gather', 'Equal', 'If' to one single 'Squeeze' node. + This can unify the different behaviors between aten::squeeze and onnx:Squeeze. + """ + def _graph_fold_back_to_squeeze_recursive(subgraph: GraphProto): + # Do folding in sub-graphs recursively. + for node in subgraph.node: + if node.op_type == 'If': + for attr in node.attribute: + branch = onnx.helper.get_attribute_value(attr) + _graph_fold_back_to_squeeze_recursive(branch) + elif node.op_type == 'Loop': + for attr in node.attribute: + if attr.name == 'body': + body = onnx.helper.get_attribute_value(attr) + _graph_fold_back_to_squeeze_recursive(body) + + # Do folding in current graph. + i_shape = 0 + while i_shape < len(subgraph.node): + if subgraph.node[i_shape].op_type == 'Shape': + shape_node = subgraph.node[i_shape] + shape_out = shape_node.output[0] + i_gather = i_shape + 1 + while i_gather < len(subgraph.node): + if subgraph.node[i_gather].op_type == 'Gather' and subgraph.node[i_gather].input[0] == shape_out: + gather_node = subgraph.node[i_gather] + gather_out = gather_node.output[0] + i_equal = i_gather + 1 + while i_equal < len(subgraph.node): + if subgraph.node[i_equal].op_type == 'Equal' and ( + subgraph.node[i_equal].input[0] == gather_out + or subgraph.node[i_equal].input[1] == gather_out): + equal_node = subgraph.node[i_equal] + equal_out = equal_node.output[0] + i_if = i_equal + 1 + while i_if < len(subgraph.node): + if subgraph.node[i_if].op_type == 'If' \ + and subgraph.node[i_if].input[0] == equal_out: + # Found the substructure to be folded. + if_node = subgraph.node[i_if] + # Create 'Squeeze' node. + squeeze_node = onnx.helper.make_node( + op_type='Squeeze', + inputs=[ + *list(shape_node.input), + # For ONNX opset >= 13, axes should be an input instead of an attribute. + gather_node.input[1] # Use 'indices' input of 'Gather' + ], + outputs=if_node.output, + name=shape_node.name.replace('Shape', 'Squeeze') + ) + # Replace 'Shape', 'Gather', 'Equal', 'If' with 'Squeeze'. + subgraph.node.insert(i_shape, squeeze_node) + subgraph.node.remove(shape_node) + subgraph.node.remove(gather_node) + subgraph.node.remove(equal_node) + subgraph.node.remove(if_node) + _verbose( + f'| fold nodes: [\'{shape_node.name}\', \'{gather_node.name}\', ' + f'\'{equal_node.name}\', \'{if_node.name}\'] -> \'{squeeze_node.name}\'') + break + i_if += 1 + else: + break + i_equal += 1 + else: + break + i_gather += 1 + else: + break + i_shape += 1 + + _graph_fold_back_to_squeeze_recursive(graph) + + +def graph_extract_conditioner_projections( + graph: GraphProto, + op_type: str, + weight_pattern: str, + alias_prefix: str +): + """ + Extract conditioner projection nodes out of the backbone wrapped by diffusion. + These nodes only need to be calculated once before entering the main denoising loop, + and can be reused inside the loop. This optimizes the performance of ONNX inference. + + :param graph: graph to perform the operation on + :param op_type: the ONNX operator type of the conditioner projections (usually 'Conv' or 'Gemm') + :param weight_pattern: a regular expression as pattern of the conditioner projection weight keys + :param alias_prefix: add prefixes to the outputs of extracted projection nodes + """ + node_dict: Dict[str, Tuple[str, NodeProto]] = {} # key: pattern match, value: (alias, node) + + def _extract_conv_nodes_recursive(subgraph: GraphProto): + to_be_removed = [] + for sub_node in subgraph.node: + if sub_node.op_type == 'If': + for attr in sub_node.attribute: + branch = onnx.helper.get_attribute_value(attr) + _extract_conv_nodes_recursive(branch) + elif sub_node.op_type == 'Loop': + for attr in sub_node.attribute: + if attr.name == 'body': + body = onnx.helper.get_attribute_value(attr) + _extract_conv_nodes_recursive(body) + elif sub_node.op_type == op_type and re.match(weight_pattern, sub_node.input[1]): + # Found node to extract + cached = node_dict.get(sub_node.input[1]) + if cached is None: + out_alias = f'{alias_prefix}.{len(node_dict)}' + node_dict[sub_node.input[1]] = (out_alias, sub_node) + else: + out_alias = cached[0] + out = sub_node.output[0] + # Search for nodes downstream the extracted node and match them to the renamed output. + for dep_node in subgraph.node: + for dep_idx, dep_input in enumerate(dep_node.input): + if dep_input == out: + dep_node.input.remove(out) + dep_node.input.insert(dep_idx, out_alias) + # Add the node to the remove list. + to_be_removed.append(sub_node) + [subgraph.node.remove(_n) for _n in to_be_removed] + + toplevel_entry_node_idx = toplevel_entry_node = None + # Find the **last** If node in toplevel graph + for i, n in enumerate(graph.node): + if n.op_type == 'If': + toplevel_entry_node_idx = i + toplevel_entry_node = n + # If not found, find the **last** Loop node in toplevel graph + if toplevel_entry_node is None: + for i, n in enumerate(graph.node): + if n.op_type == 'Loop': + toplevel_entry_node_idx = i + toplevel_entry_node = n + if toplevel_entry_node is not None: + for a in toplevel_entry_node.attribute: + # Apply to all sub-graphs + v = onnx.helper.get_attribute_value(a) + if isinstance(v, GraphProto): + _extract_conv_nodes_recursive(v) + + # Insert the extracted nodes before the first 'If' node which carries the main denoising loop. + for key in reversed(node_dict): + alias, node = node_dict[key] + # Rename output of the node. + out_name = node.output[0] + node.output.remove(node.output[0]) + node.output.insert(0, alias) + # Insert node into the main graph. + graph.node.insert(toplevel_entry_node_idx, node) + # Rename value info of the output. + for v in graph.value_info: + if v.name == out_name: + v.name = alias + break + _verbose(f'| extract conditioner projection: \'{node.name}\'') + + +def graph_remove_unused_values(graph: GraphProto): + used_values = set() + + def _record_usage_recursive(subgraph: GraphProto): + for node in subgraph.node: + # For 'If' and 'Loop' nodes, do recording recursively + if node.op_type == 'If': + for attr in node.attribute: + branch = onnx.helper.get_attribute_value(attr) + _record_usage_recursive(branch) + elif node.op_type == 'Loop': + for attr in node.attribute: + if attr.name == 'body': + body = onnx.helper.get_attribute_value(attr) + _record_usage_recursive(body) + # For each node, record its inputs and outputs + for io_list in [node.input, node.output]: + for io_value in io_list: + used_values.add(io_value) + + def _clean_unused_recursively(subgraph): + # Do cleaning in sub-graphs recursively. + for node in subgraph.node: + if node.op_type == 'If': + for attr in node.attribute: + branch = onnx.helper.get_attribute_value(attr) + _clean_unused_recursively(branch) + elif node.op_type == 'Loop': + for attr in node.attribute: + if attr.name == 'body': + body = onnx.helper.get_attribute_value(attr) + _clean_unused_recursively(body) + + # Do cleaning in current graph. + i = 0 + while i < len(subgraph.initializer): + name = subgraph.initializer[i].name + if name not in used_values: + subgraph.initializer.pop(i) + _verbose(f'| remove unused initializer: {name}') + else: + i += 1 + i = 0 + while i < len(subgraph.value_info): + name = subgraph.value_info[i].name + if name not in used_values: + subgraph.value_info.pop(i) + _verbose(f'| remove unused value info: {name}') + else: + i += 1 + + _record_usage_recursive(graph) + _clean_unused_recursively(graph) diff --git a/utils/phoneme_utils.py b/utils/phoneme_utils.py new file mode 100644 index 0000000..8b2a5ad --- /dev/null +++ b/utils/phoneme_utils.py @@ -0,0 +1,210 @@ +import json +import pathlib +from typing import Dict, List, Union + +from utils.hparams import hparams + +PAD_INDEX = 0 + + +class PhonemeDictionary: + def __init__( + self, + dictionaries: Dict[str, pathlib.Path], + extra_phonemes: List[str] = None, + merged_groups: List[List[str]] = None + ): + # Step 1: Collect all phonemes + all_phonemes = {'AP', 'SP'} + if extra_phonemes: + for ph in extra_phonemes: + if '/' in ph: + lang, name = ph.split('/', maxsplit=1) + if lang not in dictionaries: + raise ValueError( + f"Invalid phoneme tag '{ph}' in extra phonemes: " + f"unrecognized language name '{lang}'." + ) + if name in all_phonemes: + raise ValueError( + f"Invalid phoneme tag '{ph}' in extra phonemes: " + f"short name conflicts with existing tag." + ) + all_phonemes.add(ph) + self._multi_langs = len(dictionaries) > 1 + for lang, dict_path in dictionaries.items(): + with open(dict_path, 'r', encoding='utf8') as dict_file: + for line in dict_file: + _, phonemes = line.strip().split('\t') + phonemes = phonemes.split() + for phoneme in phonemes: + if '/' in phoneme: + raise ValueError( + f"Invalid phoneme tag '{phoneme}' in dictionary '{dict_path}': " + f"should not contain the reserved character '/'." + ) + if phoneme in all_phonemes: + continue + if self._multi_langs: + all_phonemes.add(f'{lang}/{phoneme}') + else: + all_phonemes.add(phoneme) + # Step 2: Parse merged phoneme groups + if merged_groups is None: + merged_groups = [] + else: + _merged_groups = [] + for group in merged_groups: + _group = [] + for phoneme in group: + if '/' in phoneme: + lang, name = phoneme.split('/', maxsplit=1) + if lang not in dictionaries: + raise ValueError( + f"Invalid phoneme tag '{phoneme}' in merged group: " + f"unrecognized language name '{lang}'." + ) + if self._multi_langs: + element = phoneme + else: + element = name + else: + element = phoneme + if element not in all_phonemes: + raise ValueError( + f"Invalid phoneme tag '{phoneme}' in merged group: " + f"not found in phoneme set." + ) + _group.append(element) + _merged_groups.append(_group) + merged_groups = [set(phones) for phones in _merged_groups if len(phones) > 1] + # Step 3: Build phoneme index + merged_phonemes_inverted_index = {} + for idx, group in enumerate(merged_groups): + other_idx = None + for phoneme in group: + if phoneme in merged_phonemes_inverted_index: + other_idx = merged_phonemes_inverted_index[phoneme] + break + target_idx = idx if other_idx is None else other_idx + for phoneme in group: + merged_phonemes_inverted_index[phoneme] = target_idx + if other_idx is not None: + merged_groups[other_idx] |= group + group.clear() + phone_to_id = {} + id_to_phone = [] + cross_lingual_phonemes = set() + idx = 1 + for phoneme in sorted(all_phonemes): + if phoneme in merged_phonemes_inverted_index: + has_assigned = True + for alias in merged_groups[merged_phonemes_inverted_index[phoneme]]: + if alias not in phone_to_id: + has_assigned = False + phone_to_id[alias] = idx + if not has_assigned: + merged_group = sorted(merged_groups[merged_phonemes_inverted_index[phoneme]]) + merged_from_langs = { + (alias.split('/', maxsplit=1)[0] if '/' in alias else None) + for alias in merged_group + } + id_to_phone.append(tuple(merged_group)) + idx += 1 + if len(merged_from_langs) > 1: + cross_lingual_phonemes.update(ph for ph in merged_group if '/' in ph) + else: + phone_to_id[phoneme] = idx + id_to_phone.append(phoneme) + idx += 1 + self._phone_to_id: Dict[str, int] = phone_to_id + self._id_to_phone: List[Union[str, tuple]] = id_to_phone + self._cross_lingual_phonemes = frozenset(cross_lingual_phonemes) + + @property + def vocab_size(self): + return len(self._id_to_phone) + 1 + + def __len__(self): + return self.vocab_size + + @property + def cross_lingual_phonemes(self): + return self._cross_lingual_phonemes + + def is_cross_lingual(self, phone): + return phone in self._cross_lingual_phonemes + + def encode_one(self, phone, lang=None): + if '/' in phone: + lang, phone = phone.split('/', maxsplit=1) + if lang is None or not self._multi_langs or phone in self._phone_to_id: + return self._phone_to_id[phone] + if '/' not in phone: + phone = f'{lang}/{phone}' + return self._phone_to_id[phone] + + def encode(self, sentence, lang=None): + phones = sentence.strip().split() if isinstance(sentence, str) else sentence + return [self.encode_one(phone, lang=lang) for phone in phones] + + def decode_one(self, idx, lang=None, scalar=True): + if idx <= 0: + return None + phone = self._id_to_phone[idx - 1] + if not scalar or isinstance(phone, str): + return phone + if lang is None or not self._multi_langs: + return phone[0] + for alias in phone: + if alias.startswith(f'{lang}/'): + return alias + return phone[0] + + def decode(self, ids, lang=None, scalar=True): + ids = list(ids) + return ' '.join([ + self.decode_one(i, lang=lang, scalar=scalar) + for i in ids + if i >= 1 + ]) + + def dump(self, filename): + with open(filename, 'w', encoding='utf8') as fp: + json.dump(self._phone_to_id, fp, ensure_ascii=False, indent=2) + + +_dictionary = None + + +def load_phoneme_dictionary() -> PhonemeDictionary: + if _dictionary is not None: + return _dictionary + config_dicts = hparams.get('dictionaries') + if config_dicts is not None: + dicts = {} + for lang, config_dict_path in config_dicts.items(): + dict_path = pathlib.Path(hparams['work_dir']) / f'dictionary-{lang}.txt' + if not dict_path.exists(): + dict_path = pathlib.Path(config_dict_path) + if not dict_path.exists(): + raise FileNotFoundError( + f"Could not locate dictionary for language '{lang}'." + ) + dicts[lang] = dict_path + else: + dict_path = pathlib.Path(hparams['work_dir']) / 'dictionary.txt' + if not dict_path.exists(): + dict_path = pathlib.Path(hparams['dictionary']) + if not dict_path.exists(): + raise FileNotFoundError( + f"Could not locate dictionary file." + ) + dicts = { + 'default': dict_path + } + return PhonemeDictionary( + dictionaries=dicts, + extra_phonemes=hparams.get('extra_phonemes'), + merged_groups=hparams.get('merged_phoneme_groups') + ) diff --git a/utils/pitch_utils.py b/utils/pitch_utils.py new file mode 100644 index 0000000..57ae943 --- /dev/null +++ b/utils/pitch_utils.py @@ -0,0 +1,27 @@ +import numpy as np + + +def norm_f0(f0, uv=None): + if uv is None: + uv = f0 == 0 + f0 = np.log2(f0 + uv) # avoid arithmetic error + f0[uv] = -np.inf + return f0 + + +def interp_f0(f0, uv=None): + if uv is None: + uv = f0 == 0 + f0 = norm_f0(f0, uv) + if uv.any() and not uv.all(): + f0[uv] = np.interp(np.where(uv)[0], np.where(~uv)[0], f0[~uv]) + return denorm_f0(f0, uv=None), uv + + +def denorm_f0(f0, uv, pitch_padding=None): + f0 = 2 ** f0 + if uv is not None: + f0[uv > 0] = 0 + if pitch_padding is not None: + f0[pitch_padding] = 0 + return f0 diff --git a/utils/plot.py b/utils/plot.py new file mode 100644 index 0000000..aebe56a --- /dev/null +++ b/utils/plot.py @@ -0,0 +1,122 @@ +import matplotlib.pyplot as plt +import numpy as np +import torch +from matplotlib.ticker import MultipleLocator + + +def spec_to_figure(spec, vmin=None, vmax=None, title=None): + if isinstance(spec, torch.Tensor): + spec = spec.cpu().numpy() + fig = plt.figure(figsize=(12, 9)) + plt.pcolormesh(spec.T, vmin=vmin, vmax=vmax) + if title is not None: + plt.title(title, fontsize=15) + plt.tight_layout() + return fig + + +def dur_to_figure(dur_gt, dur_pred, txt, title=None): + if isinstance(dur_gt, torch.Tensor): + dur_gt = dur_gt.cpu().numpy() + if isinstance(dur_pred, torch.Tensor): + dur_pred = dur_pred.cpu().numpy() + dur_gt = dur_gt.astype(np.int64) + dur_pred = dur_pred.astype(np.int64) + dur_gt = np.cumsum(dur_gt) + dur_pred = np.cumsum(dur_pred) + width = max(12, min(48, len(txt) // 2)) + fig = plt.figure(figsize=(width, 8)) + plt.vlines(dur_pred, 12, 22, colors='r', label='pred') + plt.vlines(dur_gt, 0, 10, colors='b', label='gt') + for i in range(len(txt)): + shift = (i % 8) + 1 + plt.text((dur_pred[i-1] + dur_pred[i]) / 2 if i > 0 else dur_pred[i] / 2, 12 + shift, txt[i], + size=16, horizontalalignment='center') + plt.text((dur_gt[i-1] + dur_gt[i]) / 2 if i > 0 else dur_gt[i] / 2, shift, txt[i], + size=16, horizontalalignment='center') + plt.plot([dur_pred[i], dur_gt[i]], [12, 10], color='black', linewidth=2, linestyle=':') + plt.yticks([]) + plt.xlim(0, max(dur_pred[-1], dur_gt[-1])) + plt.legend() + if title is not None: + plt.title(title, fontsize=15) + plt.tight_layout() + return fig + + +def pitch_note_to_figure(pitch_gt, pitch_pred=None, note_midi=None, note_dur=None, note_rest=None, title=None): + if isinstance(pitch_gt, torch.Tensor): + pitch_gt = pitch_gt.cpu().numpy() + if isinstance(pitch_pred, torch.Tensor): + pitch_pred = pitch_pred.cpu().numpy() + if isinstance(note_midi, torch.Tensor): + note_midi = note_midi.cpu().numpy() + if isinstance(note_dur, torch.Tensor): + note_dur = note_dur.cpu().numpy() + if isinstance(note_rest, torch.Tensor): + note_rest = note_rest.cpu().numpy() + fig = plt.figure() + if note_midi is not None and note_dur is not None: + note_dur_acc = np.cumsum(note_dur) + if note_rest is None: + note_rest = np.zeros_like(note_midi, dtype=np.bool_) + for i in range(len(note_midi)): + # if note_rest[i]: + # continue + plt.gca().add_patch( + plt.Rectangle( + xy=(note_dur_acc[i-1] if i > 0 else 0, note_midi[i] - 0.5), + width=note_dur[i], height=1, + edgecolor='grey', fill=False, + linewidth=1.5, linestyle='--' if note_rest[i] else '-' + ) + ) + plt.plot(pitch_gt, color='b', label='gt') + if pitch_pred is not None: + plt.plot(pitch_pred, color='r', label='pred') + plt.gca().yaxis.set_major_locator(MultipleLocator(1)) + plt.grid(axis='y') + plt.legend() + if title is not None: + plt.title(title, fontsize=15) + plt.tight_layout() + return fig + + +def curve_to_figure(curve_gt, curve_pred=None, curve_base=None, grid=None, title=None): + if isinstance(curve_gt, torch.Tensor): + curve_gt = curve_gt.cpu().numpy() + if isinstance(curve_pred, torch.Tensor): + curve_pred = curve_pred.cpu().numpy() + if isinstance(curve_base, torch.Tensor): + curve_base = curve_base.cpu().numpy() + fig = plt.figure() + if curve_base is not None: + plt.plot(curve_base, color='g', label='base') + plt.plot(curve_gt, color='b', label='gt') + if curve_pred is not None: + plt.plot(curve_pred, color='r', label='pred') + if grid is not None: + plt.gca().yaxis.set_major_locator(MultipleLocator(grid)) + plt.grid(axis='y') + plt.legend() + if title is not None: + plt.title(title, fontsize=15) + plt.tight_layout() + return fig + + +def distribution_to_figure(title, x_label, y_label, items: list, values: list, zoom=0.8, rotate=False): + fig = plt.figure(figsize=(int(len(items) * zoom), 10)) + plt.bar(x=items, height=values) + plt.tick_params(labelsize=15) + plt.xlim(-1, len(items)) + for a, b in zip(items, values): + plt.text(a, b, b, ha='center', va='bottom', fontsize=15) + plt.grid() + plt.title(title, fontsize=30) + plt.xlabel(x_label, fontsize=20) + plt.ylabel(y_label, fontsize=20) + if rotate: + fig.autofmt_xdate(rotation=45) + return fig diff --git a/utils/training_utils.py b/utils/training_utils.py new file mode 100644 index 0000000..e906f77 --- /dev/null +++ b/utils/training_utils.py @@ -0,0 +1,447 @@ +import math +import re +from copy import deepcopy +from pathlib import Path +from typing import Dict + +import lightning.pytorch as pl +import numpy as np +import torch +from lightning.fabric.loggers.tensorboard import _TENSORBOARD_AVAILABLE +from lightning.pytorch.callbacks import ModelCheckpoint, TQDMProgressBar +from lightning.pytorch.loggers import TensorBoardLogger +from lightning.pytorch.utilities.rank_zero import rank_zero_info, rank_zero_only +from torch.optim.lr_scheduler import LambdaLR +from torch.utils.data.distributed import Sampler + +import utils +from utils.hparams import hparams + + +# ==========LR schedulers========== + +class RSQRTSchedule(object): + def __init__(self, optimizer): + super().__init__() + self.optimizer = optimizer + self.constant_lr = hparams['lr'] + self.warmup_updates = hparams['warmup_updates'] + self.hidden_size = hparams['hidden_size'] + self.lr = hparams['lr'] + for param_group in optimizer.param_groups: + param_group['lr'] = self.lr + self.step(0) + + def step(self, num_updates): + constant_lr = self.constant_lr + warmup = min(num_updates / self.warmup_updates, 1.0) + rsqrt_decay = max(self.warmup_updates, num_updates) ** -0.5 + rsqrt_hidden = self.hidden_size ** -0.5 + self.lr = max(constant_lr * warmup * rsqrt_decay * rsqrt_hidden, 1e-7) + for param_group in self.optimizer.param_groups: + param_group['lr'] = self.lr + return self.lr + + def get_lr(self): + return self.optimizer.param_groups[0]['lr'] + + +class WarmupCosineSchedule(LambdaLR): + """ Linear warmup and then cosine decay. + Linearly increases learning rate from 0 to 1 over `warmup_steps` training steps. + Decreases learning rate from 1. to 0. over remaining `t_total - warmup_steps` steps following a cosine curve. + If `cycles` (default=0.5) is different from default, learning rate follows cosine function after warmup. + `eta_min` (default=0.0) corresponds to the minimum learning rate reached by the scheduler. + """ + + def __init__(self, optimizer, warmup_steps, t_total, warmup_min=0.0, eta_min=0.0, cycles=.5, last_epoch=-1): + self.warmup_steps = warmup_steps + self.t_total = t_total + self.eta_min = eta_min + self.cycles = cycles + self.warmup_min = warmup_min + super(WarmupCosineSchedule, self).__init__(optimizer, self.lr_lambda, last_epoch=last_epoch) + + def lr_lambda(self, step): + if step < self.warmup_steps: + progress = step / max(1.0, self.warmup_steps) + return self.warmup_min + progress * (1.0 - self.warmup_min) + # progress after warmup + progress = (step - self.warmup_steps) / max(1, self.t_total - self.warmup_steps) + return max(self.eta_min, 0.5 * (1. + math.cos(math.pi * self.cycles * 2.0 * progress))) + + +# ==========Torch samplers========== + +class DsBatchSampler(Sampler): + def __init__(self, dataset, max_batch_frames, max_batch_size, sub_indices=None, + num_replicas=None, rank=None, + required_batch_count_multiple=1, batch_by_size=True, sort_by_similar_size=True, + size_reversed=False, shuffle_sample=False, shuffle_batch=False, + disallow_empty_batch=True, pad_batch_assignment=True, seed=0, drop_last=False) -> None: + if rank >= num_replicas or rank < 0: + raise ValueError( + f"Invalid rank {rank}, rank should be in the interval [0, {num_replicas - 1}]") + self.dataset = dataset + self.max_batch_frames = max_batch_frames + self.max_batch_size = max_batch_size + self.sub_indices = sub_indices + self.num_replicas = num_replicas + self.rank = rank + self.required_batch_count_multiple = required_batch_count_multiple + self.batch_by_size = batch_by_size + self.sort_by_similar_size = sort_by_similar_size + self.size_reversed = size_reversed + self.shuffle_sample = shuffle_sample + self.shuffle_batch = shuffle_batch + self.disallow_empty_batch = disallow_empty_batch + self.pad_batch_assignment = pad_batch_assignment + self.seed = seed + self.drop_last = drop_last + self.epoch = 0 + self.batches = None + self.formed = None + + def __form_batches(self): + if self.formed == self.epoch + self.seed: + return + rng = np.random.default_rng() + # Create indices + if self.shuffle_sample: + if self.sub_indices is not None: + rng.shuffle(self.sub_indices) + indices = np.array(self.sub_indices) + else: + indices = rng.permutation(len(self.dataset)) + + if self.sort_by_similar_size: + grid = int(hparams['sampler_frame_count_grid']) + assert grid > 0 + sizes = (np.round(np.array(self.dataset.sizes)[indices] / grid) * grid).clip(grid, None) + sizes *= (-1 if self.size_reversed else 1) + indices = indices[np.argsort(sizes, kind='mergesort')] + + indices = indices.tolist() + else: + indices = self.sub_indices if self.sub_indices is not None else list(range(len(self.dataset))) + + # Batching + if self.batch_by_size: + batches = utils.batch_by_size( + indices, self.dataset.num_frames, + max_batch_frames=self.max_batch_frames, + max_batch_size=self.max_batch_size + ) + else: + batches = [indices[i:i + self.max_batch_size] for i in range(0, len(indices), self.max_batch_size)] + if len(batches) < self.num_replicas and self.disallow_empty_batch: + raise RuntimeError("There is not enough batch to assign to each node.") + + # Either drop_last or separate the leftovers. + floored_total_batch_count = (len(batches) // self.num_replicas) * self.num_replicas + if self.drop_last and len(batches) > floored_total_batch_count: + batches = batches[:floored_total_batch_count] + leftovers = [] + if len(batches) == 0: + raise RuntimeError("There is no batch left after dropping the last batch.") + elif self.shuffle_batch: + leftovers = (rng.permutation(len(batches) - floored_total_batch_count) + floored_total_batch_count).tolist() + else: + leftovers = list(range(floored_total_batch_count, len(batches))) + + # Initial batch assignment to current rank. + batch_assignment = np.arange(floored_total_batch_count).reshape(-1, self.num_replicas).transpose() + if self.shuffle_batch: + batch_assignment = rng.permuted(batch_assignment, axis=0)[self.rank].tolist() + else: + batch_assignment = batch_assignment[self.rank].tolist() + + # Assign leftovers or pad the batch assignment. + floored_batch_count = len(batch_assignment) + if self.rank < len(leftovers): + batch_assignment.append(leftovers[self.rank]) + floored_batch_count += 1 + elif len(leftovers) > 0 and self.pad_batch_assignment: + if not batch_assignment: + raise RuntimeError("Cannot pad empty batch assignment.") + batch_assignment.append(batch_assignment[self.epoch % floored_batch_count]) + # Ensure the batch count is multiple of required_batch_count_multiple. + if self.required_batch_count_multiple > 1 and len(batch_assignment) % self.required_batch_count_multiple != 0: + ceiled_batch_count = math.ceil( + len(batch_assignment) / self.required_batch_count_multiple + ) * self.required_batch_count_multiple + for i in range(ceiled_batch_count - len(batch_assignment)): + batch_assignment.append( + batch_assignment[(i + self.epoch * self.required_batch_count_multiple) % floored_batch_count]) + + if batch_assignment: + self.batches = [deepcopy(batches[i]) for i in batch_assignment] + else: + self.batches = [[]] + self.formed = self.epoch + self.seed + + del indices + del batches + del batch_assignment + + def __iter__(self): + self.__form_batches() + return iter(self.batches) + + def __len__(self): + self.__form_batches() + if self.batches is None: + raise RuntimeError("Batches are not initialized. Call __form_batches first.") + return len(self.batches) + + def set_epoch(self, epoch): + self.epoch = epoch + self.__form_batches() + + + +# ==========PL related========== + +class DsModelCheckpoint(ModelCheckpoint): + def __init__( + self, + *args, + permanent_ckpt_start, + permanent_ckpt_interval, + **kwargs + ): + super().__init__(*args, **kwargs) + self.permanent_ckpt_start = permanent_ckpt_start or 0 + self.permanent_ckpt_interval = permanent_ckpt_interval or 0 + self.enable_permanent_ckpt = self.permanent_ckpt_start > 0 and self.permanent_ckpt_interval > 9 + + self._verbose = self.verbose + self.verbose = False + + def state_dict(self): + ret = super().state_dict() + ret.pop('dirpath') + return ret + + def load_state_dict(self, state_dict) -> None: + super().load_state_dict(state_dict) + + def on_validation_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None: + if trainer.lightning_module.skip_immediate_ckpt_save: + trainer.lightning_module.skip_immediate_ckpt_save = False + return + self.last_val_step = trainer.global_step + super().on_validation_end(trainer, pl_module) + + def _update_best_and_save( + self, current: torch.Tensor, trainer: "pl.Trainer", monitor_candidates: Dict[str, torch.Tensor] + ) -> None: + k = len(self.best_k_models) + 1 if self.save_top_k == -1 else self.save_top_k + + del_filepath = None + _op = max if self.mode == "min" else min + while len(self.best_k_models) > k and k > 0: + self.kth_best_model_path = _op(self.best_k_models, key=self.best_k_models.get) # type: ignore[arg-type] + self.kth_value = self.best_k_models[self.kth_best_model_path] + + del_filepath = self.kth_best_model_path + self.best_k_models.pop(del_filepath) + filepath = self._get_metric_interpolated_filepath_name(monitor_candidates, trainer, del_filepath) + if del_filepath is not None and filepath != del_filepath: + self._remove_checkpoint(trainer, del_filepath) + + if len(self.best_k_models) == k and k > 0: + self.kth_best_model_path = _op(self.best_k_models, key=self.best_k_models.get) # type: ignore[arg-type] + self.kth_value = self.best_k_models[self.kth_best_model_path] + + super()._update_best_and_save(current, trainer, monitor_candidates) + + def _save_checkpoint(self, trainer: "pl.Trainer", filepath: str) -> None: + filepath = (Path(self.dirpath) / Path(filepath).name).resolve() + super()._save_checkpoint(trainer, str(filepath)) + if self._verbose: + relative_path = filepath + # Avoid using `is_relative_to` because Python 3.8 does not support this + if Path('.').resolve() in filepath.parents: + relative_path = filepath.relative_to(Path('.').resolve()) + rank_zero_info(f'Checkpoint {relative_path} saved.') + + def _remove_checkpoint(self, trainer: "pl.Trainer", filepath: str): + filepath = (Path(self.dirpath) / Path(filepath).name).resolve() + relative_path = filepath + # Avoid using `is_relative_to` because Python 3.8 does not support this + if Path('.').resolve() in filepath.parents: + relative_path = filepath.relative_to(Path('.').resolve()) + search = re.search(r'steps_\d+', relative_path.stem) + if search: + step = int(search.group(0)[6:]) + if self.enable_permanent_ckpt and \ + step >= self.permanent_ckpt_start and \ + (step - self.permanent_ckpt_start) % self.permanent_ckpt_interval == 0: + rank_zero_info(f'Checkpoint {relative_path} is now permanent.') + return + super()._remove_checkpoint(trainer, filepath) + if self._verbose: + rank_zero_info(f'Removed checkpoint {relative_path}.') + + +def get_latest_checkpoint_path(work_dir): + if not isinstance(work_dir, Path): + work_dir = Path(work_dir) + if not work_dir.exists(): + return None + + last_step = -1 + last_ckpt_name = None + + for ckpt in work_dir.glob('model_ckpt_steps_*.ckpt'): + search = re.search(r'steps_\d+', ckpt.name) + if search: + step = int(search.group(0)[6:]) + if step > last_step: + last_step = step + last_ckpt_name = str(ckpt) + + return last_ckpt_name if last_ckpt_name is not None else None + + +class DsTQDMProgressBar(TQDMProgressBar): + def __init__(self, refresh_rate: int = 1, process_position: int = 0, show_steps: bool = True): + super().__init__(refresh_rate, process_position) + self.show_steps = show_steps + + def get_metrics(self, trainer, model): + items = super().get_metrics(trainer, model) + if 'batch_size' in items: + items['batch_size'] = int(items['batch_size']) + if self.show_steps: + items['steps'] = str(trainer.global_step) + for k, v in items.items(): + if isinstance(v, float): + if np.isnan(v): + items[k] = 'nan' + elif 0.001 <= v < 10: + items[k] = np.format_float_positional(v, unique=True, precision=5, trim='-') + elif 0.00001 <= v < 0.001: + if len(np.format_float_positional(v, unique=True, precision=8, trim='-')) > 8: + items[k] = np.format_float_scientific(v, precision=3, unique=True, min_digits=2, trim='-') + else: + items[k] = np.format_float_positional(v, unique=True, precision=5, trim='-') + elif v < 0.00001: + items[k] = np.format_float_scientific(v, precision=3, unique=True, min_digits=2, trim='-') + items.pop("v_num", None) + return items + + +class DsTensorBoardLogger(TensorBoardLogger): + @property + def all_rank_experiment(self): + if rank_zero_only.rank == 0: + return self.experiment + if hasattr(self, "_all_rank_experiment") and self._all_rank_experiment is not None: + return self._all_rank_experiment + + assert rank_zero_only.rank != 0 + if self.root_dir: + self._fs.makedirs(self.root_dir, exist_ok=True) + + if _TENSORBOARD_AVAILABLE: + from torch.utils.tensorboard import SummaryWriter + else: + from tensorboardX import SummaryWriter # type: ignore[no-redef] + + self._all_rank_experiment = SummaryWriter(log_dir=self.log_dir, **self._kwargs) + return self._all_rank_experiment + + def finalize(self, status: str) -> None: + if rank_zero_only.rank == 0: + super().finalize(status) + elif hasattr(self, "_all_rank_experiment") and self._all_rank_experiment is not None: + self.all_rank_experiment.flush() + self.all_rank_experiment.close() + + def __getstate__(self): + state = super().__getstate__() + if "_all_rank_experiment" in state: + del state["_all_rank_experiment"] + return state + +def get_strategy( + devices="auto", + num_nodes=1, + accelerator="auto", + strategy={"name": "auto"}, + precision=None, +): + from lightning.fabric.utilities.device_parser import _determine_root_gpu_device + from lightning.pytorch.accelerators import AcceleratorRegistry + from lightning.pytorch.accelerators.cuda import CUDAAccelerator + from lightning.pytorch.accelerators.mps import MPSAccelerator + from lightning.pytorch.strategies import Strategy, SingleDeviceStrategy, StrategyRegistry + from lightning.pytorch.trainer.connectors import accelerator_connector + from lightning.pytorch.utilities.rank_zero import rank_zero_warn + class _DsAcceleratorConnector(accelerator_connector._AcceleratorConnector): + def __init__(self) -> None: + accelerator_connector._register_external_accelerators_and_strategies() + self._registered_strategies = StrategyRegistry.available_strategies() + self._accelerator_types = AcceleratorRegistry.available_accelerators() + self._parallel_devices = [] + self._check_config_and_set_final_flags( + strategy=strategy["name"], + accelerator=accelerator, + precision=precision, + plugins=[], + sync_batchnorm=False, + ) + if self._accelerator_flag == "auto": + self._accelerator_flag = self._choose_auto_accelerator() + elif self._accelerator_flag == "gpu": + self._accelerator_flag = self._choose_gpu_accelerator_backend() + self._check_device_config_and_set_final_flags(devices=devices, num_nodes=num_nodes) + self._set_parallel_devices_and_init_accelerator() + if self._strategy_flag == "auto": + self._strategy_flag = self._choose_strategy() + self._check_strategy_and_fallback() + self._init_strategy() + for k in ["colossalai", "bagua", "hpu", "hpu_parallel", "hpu_single", "ipu", "ipu_strategy"]: + if k in StrategyRegistry: + StrategyRegistry.remove(k) + + def _init_strategy(self) -> None: + assert isinstance(self._strategy_flag, (str, Strategy)) + if isinstance(self._strategy_flag, str): + if self._strategy_flag not in StrategyRegistry: + available_names = ", ".join(sorted(StrategyRegistry.available_strategies())) or "none" + raise KeyError(f"Invalid strategy name {strategy['name']}. Available names: {available_names}") + data = StrategyRegistry[self._strategy_flag] + params = {} + # Replicate additional logic for _choose_strategy when dealing with single device strategies + if issubclass(data["strategy"], SingleDeviceStrategy): + if self._accelerator_flag == "hpu": + params = {"device": torch.device("hpu")} + elif self._accelerator_flag == "tpu": + params = {"device": self._parallel_devices[0]} + elif data["strategy"] is SingleDeviceStrategy: + if isinstance(self._accelerator_flag, (CUDAAccelerator, MPSAccelerator)) or ( + isinstance(self._accelerator_flag, str) and self._accelerator_flag in ("cuda", "gpu", "mps") + ): + params = {"device": _determine_root_gpu_device(self._parallel_devices)} + else: + params = {"device": "cpu"} + else: + raise NotImplementedError + params.update(data["init_params"]) + params.update({k: v for k, v in strategy.items() if k != "name"}) + self.strategy = data["strategy"](**utils.filter_kwargs(params, data["strategy"])) + elif isinstance(self._strategy_flag, SingleDeviceStrategy): + params = {"device": self._strategy_flag.root_device} + params.update({k: v for k, v in strategy.items() if k != "name"}) + self.strategy = self._strategy_flag.__class__(**utils.filter_kwargs(params, self._strategy_flag.__class__)) + else: + rank_zero_warn( + f"Inferred strategy {self._strategy_flag.__class__.__name__} cannot take custom configurations." + f"To use custom configurations, please specify the strategy name explicitly." + ) + self.strategy = self._strategy_flag + + return _DsAcceleratorConnector().strategy