chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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from pathlib import Path
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from typing import List, Optional
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from examples.speech_to_text.data_utils import S2TDataConfigWriter
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def gen_config_yaml(
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manifest_root: Path,
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yaml_filename: str = "config.yaml",
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specaugment_policy: Optional[str] = "lb",
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feature_transform: Optional[List[str]] = None,
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input_channels: Optional[int] = 1,
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input_feat_per_channel: Optional[int] = 80,
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audio_root: str = "",
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vocoder_type: Optional[str] = None,
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vocoder_checkpoint: Optional[str] = None,
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vocoder_cfg: Optional[str] = None,
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extra=None,
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):
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manifest_root = manifest_root.absolute()
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writer = S2TDataConfigWriter(manifest_root / yaml_filename)
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if input_channels is not None:
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writer.set_input_channels(input_channels)
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if input_feat_per_channel is not None:
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writer.set_input_feat_per_channel(input_feat_per_channel)
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specaugment_setters = {
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"lb": writer.set_specaugment_lb_policy,
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"ld": writer.set_specaugment_ld_policy,
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"sm": writer.set_specaugment_sm_policy,
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"ss": writer.set_specaugment_ss_policy,
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}
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specaugment_setter = specaugment_setters.get(specaugment_policy, None)
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if specaugment_setter is not None:
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specaugment_setter()
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if feature_transform is None:
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feature_transform = []
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else:
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writer.set_feature_transforms("*", feature_transform)
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if specaugment_policy is not None:
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writer.set_feature_transforms("_train", feature_transform + ["specaugment"])
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if len(audio_root) > 0:
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writer.set_audio_root(audio_root)
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if (
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vocoder_type is not None
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and vocoder_checkpoint is not None
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and vocoder_cfg is not None
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):
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writer.set_extra(
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{
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"vocoder": {
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"type": vocoder_type,
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"config": vocoder_cfg,
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"checkpoint": vocoder_checkpoint,
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}
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}
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)
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if extra is not None:
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writer.set_extra(extra)
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writer.flush()
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#!/usr/bin/env python3
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# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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import argparse
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import logging
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import os
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from pathlib import Path
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import shutil
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import torchaudio
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import soundfile as sf
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from tqdm import tqdm
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import pandas as pd
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from examples.speech_synthesis.data_utils import extract_logmel_spectrogram
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from examples.speech_to_speech.preprocessing.data_utils import gen_config_yaml
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from examples.speech_to_text.data_utils import create_zip, get_zip_manifest, save_df_to_tsv
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from fairseq.data.audio.audio_utils import convert_waveform
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logger = logging.getLogger(__name__)
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MANIFEST_COLUMNS = ["id", "src_audio", "src_n_frames", "tgt_audio", "tgt_n_frames"]
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def prepare_target_data(args, tgt_audios):
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feature_name = "logmelspec80"
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zip_path = args.output_root / f"{feature_name}.zip"
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if zip_path.exists():
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print(f"{zip_path} exists.")
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return zip_path
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feature_root = args.output_root / feature_name
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feature_root.mkdir(exist_ok=True)
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print("Extracting Mel spectrogram features...")
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for tgt_audio in tqdm(tgt_audios):
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sample_id = tgt_audio.stem
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waveform, sample_rate = torchaudio.load(tgt_audio.as_posix())
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waveform, sample_rate = convert_waveform(
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waveform, sample_rate, normalize_volume=args.normalize_volume,
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to_sample_rate=args.sample_rate
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)
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extract_logmel_spectrogram(
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waveform, sample_rate, feature_root / f"{sample_id}.npy",
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win_length=args.win_length, hop_length=args.hop_length,
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n_fft=args.n_fft, n_mels=args.n_mels, f_min=args.f_min,
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f_max=args.f_max
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)
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print("ZIPing features...")
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create_zip(feature_root, zip_path)
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shutil.rmtree(feature_root)
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return zip_path
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def process(args):
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os.makedirs(args.output_root, exist_ok=True)
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manifest = {}
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tgt_audios = []
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for split in args.data_split:
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print(f"Processing {split}...")
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manifest[split] = {c: [] for c in MANIFEST_COLUMNS}
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missing_tgt_audios = []
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src_audios = list(args.source_dir.glob(f"{split}/*.wav"))
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for src_audio in tqdm(src_audios):
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sample_id = src_audio.stem
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tgt_audio = args.target_dir / split / f"{sample_id}.wav"
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if not tgt_audio.is_file():
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missing_tgt_audios.append(sample_id)
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continue
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tgt_audios.append(tgt_audio)
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src_n_frames = sf.info(src_audio.as_posix()).frames
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manifest[split]["id"].append(sample_id)
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manifest[split]["src_audio"].append(src_audio.as_posix())
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manifest[split]["src_n_frames"].append(
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src_n_frames // 160
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) # estimation of 10-ms frame for 16kHz audio
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print(f"Processed {len(manifest[split]['id'])} samples")
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if len(missing_tgt_audios) > 0:
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print(
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f"{len(missing_tgt_audios)} with missing target data (first 3 examples: {', '.join(missing_tgt_audios[:3])})"
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)
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# Extract features and pack features into ZIP
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zip_path = prepare_target_data(args, tgt_audios)
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print("Fetching ZIP manifest...")
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tgt_audio_paths, tgt_audio_lengths = get_zip_manifest(zip_path)
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print("Generating manifest...")
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for split in args.data_split:
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print(f"Processing {split}...")
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for sample_id in tqdm(manifest[split]["id"]):
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manifest[split]["tgt_audio"].append(tgt_audio_paths[sample_id])
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manifest[split]["tgt_n_frames"].append(tgt_audio_lengths[sample_id])
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out_manifest = args.output_root / f"{split}.tsv"
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print(f"Writing manifest to {out_manifest}...")
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save_df_to_tsv(pd.DataFrame.from_dict(manifest[split]), out_manifest)
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# Generate config YAML
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win_len_t = args.win_length / args.sample_rate
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hop_len_t = args.hop_length / args.sample_rate
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extra = {
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"features": {
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"type": "spectrogram+melscale+log",
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"sample_rate": args.sample_rate,
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"eps": 1e-5, "n_mels": args.n_mels, "n_fft": args.n_fft,
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"window_fn": "hann", "win_length": args.win_length,
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"hop_length": args.hop_length,
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"win_len_t": win_len_t, "hop_len_t": hop_len_t,
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"f_min": args.f_min, "f_max": args.f_max,
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"n_stft": args.n_fft // 2 + 1
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}
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}
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gen_config_yaml(
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args.output_root,
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audio_root=args.output_root.as_posix(),
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specaugment_policy="lb",
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feature_transform=["utterance_cmvn", "delta_deltas"],
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extra=extra,
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)
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--source-dir", required=True, type=Path, help="source audio directory"
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)
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parser.add_argument(
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"--target-dir", required=True, type=Path, help="target audio directory"
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)
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parser.add_argument(
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"--data-split",
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default=["train", "valid", "test"],
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nargs="+",
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help="data split names",
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)
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parser.add_argument(
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"--output-root", required=True, type=Path, help="output directory"
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)
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# target feature related
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parser.add_argument("--win-length", type=int, default=1024)
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parser.add_argument("--hop-length", type=int, default=256)
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parser.add_argument("--n-fft", type=int, default=1024)
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parser.add_argument("--n-mels", type=int, default=80)
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parser.add_argument("--f-min", type=int, default=20)
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parser.add_argument("--f-max", type=int, default=8000)
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parser.add_argument("--sample-rate", type=int, default=22050)
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parser.add_argument("--normalize-volume", "-n", action="store_true")
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args = parser.parse_args()
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process(args)
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if __name__ == "__main__":
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main()
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#!/usr/bin/env python3
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# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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import argparse
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import logging
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from pathlib import Path
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import soundfile as sf
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from tqdm import tqdm
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import pandas as pd
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from examples.speech_to_speech.preprocessing.data_utils import gen_config_yaml
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from examples.speech_to_text.data_utils import save_df_to_tsv
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logger = logging.getLogger(__name__)
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MANIFEST_COLUMNS = ["id", "src_audio", "src_n_frames", "tgt_audio", "tgt_n_frames"]
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def load_units(in_file):
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out = {}
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with open(in_file) as f:
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for line in f:
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sample_id, units = line.strip().split("|", 1)
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out[sample_id] = units.split()
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return out
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def process_units(units, reduce=False):
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if not reduce:
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return units
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out = [u for i, u in enumerate(units) if i == 0 or u != units[i - 1]]
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return out
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def process(args):
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args.output_root.mkdir(exist_ok=True)
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print("Generating manifest...")
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for split in args.data_split:
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print(f"Processing {split}")
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# load target units
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target_unit_data = load_units(args.target_dir / f"{split}.txt")
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manifest = {c: [] for c in MANIFEST_COLUMNS}
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missing_tgt_audios = []
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src_audios = list(args.source_dir.glob(f"{split}/*.wav"))
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for src_audio in tqdm(src_audios):
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sample_id = src_audio.stem
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if sample_id not in target_unit_data:
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missing_tgt_audios.append(sample_id)
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continue
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src_n_frames = sf.info(src_audio.as_posix()).frames
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manifest["id"].append(sample_id)
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manifest["src_audio"].append(src_audio.as_posix())
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manifest["src_n_frames"].append(
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src_n_frames // 160
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) # estimation of 10-ms frame for 16kHz audio
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target_units = process_units(target_unit_data[sample_id], args.reduce_unit)
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manifest["tgt_audio"].append(" ".join(target_units))
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manifest["tgt_n_frames"].append(len(target_units))
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print(f"Processed {len(manifest['id'])} samples")
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if len(missing_tgt_audios) > 0:
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print(
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f"{len(missing_tgt_audios)} with missing target data (first 3 examples: {', '.join(missing_tgt_audios[:3])})"
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)
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out_manifest = args.output_root / f"{split}.tsv"
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print(f"Writing manifest to {out_manifest}...")
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save_df_to_tsv(pd.DataFrame.from_dict(manifest), out_manifest)
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# Generate config YAML
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gen_config_yaml(
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args.output_root,
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specaugment_policy="lb",
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feature_transform=["utterance_cmvn"],
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vocoder_type="code_hifigan",
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vocoder_checkpoint=args.vocoder_checkpoint,
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vocoder_cfg=args.vocoder_cfg,
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)
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--source-dir", required=True, type=Path, help="source audio directory"
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)
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parser.add_argument(
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"--target-dir", required=True, type=Path, help="target audio directory"
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)
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parser.add_argument(
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"--data-split",
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default=["train", "valid", "test"],
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nargs="+",
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help="data split names",
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)
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parser.add_argument(
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"--output-root", required=True, type=Path, help="output directory"
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)
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parser.add_argument(
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"--reduce-unit",
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action="store_true",
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help="reduce a target unit sequence to a unique unit sequence, i.e. '1 1 1 2 2' -> '1 2'",
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)
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parser.add_argument(
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"--vocoder-checkpoint", default=None, type=str, help="vocoder checkpoint"
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)
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parser.add_argument(
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"--vocoder-cfg", default=None, type=str, help="vocoder config file"
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)
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args = parser.parse_args()
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process(args)
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if __name__ == "__main__":
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main()
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