chore: import upstream snapshot with attribution

scripts/dataset_processing/tts/ljspeech/ds_conf/ds_for_fastpitch_align.yaml

@@ -0,0 +1,49 @@
+name: "ds_for_fastpitch_align"
+
+manifest_filepath: "train_manifest.json"
+sup_data_path: "sup_data"
+sup_data_types: [ "align_prior_matrix", "pitch" ]
+phoneme_dict_path: "scripts/tts_dataset_files/cmudict-0.7b_nv22.10"
+heteronyms_path: "scripts/tts_dataset_files/heteronyms-052722"
+
+dataset:
+  _target_: nemo.collections.tts.data.dataset.TTSDataset
+  manifest_filepath: ${manifest_filepath}
+  sample_rate: 22050
+  sup_data_path: ${sup_data_path}
+  sup_data_types: ${sup_data_types}
+  n_fft: 1024
+  win_length: 1024
+  hop_length: 256
+  window: "hann"
+  n_mels: 80
+  lowfreq: 0
+  highfreq: 8000
+  max_duration: null
+  min_duration: 0.1
+  ignore_file: null
+  trim: false
+  pitch_fmin: 65.40639132514966
+  pitch_fmax: 2093.004522404789
+
+  text_normalizer:
+    _target_: nemo_text_processing.text_normalization.normalize.Normalizer
+    lang: en
+    input_case: cased
+
+  text_normalizer_call_kwargs:
+    verbose: false
+    punct_pre_process: true
+    punct_post_process: true
+
+  text_tokenizer:
+    _target_: nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.EnglishPhonemesTokenizer
+    punct: true
+    stresses: true
+    chars: true
+    apostrophe: true
+    pad_with_space: true
+    g2p:
+      _target_: nemo.collections.tts.g2p.models.en_us_arpabet.EnglishG2p
+      phoneme_dict: ${phoneme_dict_path}
+      heteronyms: ${heteronyms_path}

scripts/dataset_processing/tts/ljspeech/ds_conf/ds_for_mixer_tts.yaml

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+name: "ds_for_mixer_tts"
+
+manifest_filepath: "train_manifest.json"
+sup_data_path: "sup_data"
+sup_data_types: [ "align_prior_matrix", "pitch" ]
+phoneme_dict_path: "scripts/tts_dataset_files/cmudict-0.7b_nv22.10"
+heteronyms_path: "scripts/tts_dataset_files/heteronyms-052722"
+
+dataset:
+  _target_: nemo.collections.tts.data.dataset.TTSDataset
+  manifest_filepath: ${manifest_filepath}
+  sample_rate: 22050
+  sup_data_path: ${sup_data_path}
+  sup_data_types: ${sup_data_types}
+  n_fft: 1024
+  win_length: 1024
+  hop_length: 256
+  window: "hann"
+  n_mels: 80
+  lowfreq: 0
+  highfreq: 8000
+  max_duration: null
+  min_duration: 0.1
+  ignore_file: null
+  trim: false
+  pitch_fmin: 65.40639132514966
+  pitch_fmax: 2093.004522404789
+
+  text_normalizer:
+    _target_: nemo_text_processing.text_normalization.normalize.Normalizer
+    lang: en
+    input_case: cased
+
+  text_normalizer_call_kwargs:
+    verbose: false
+    punct_pre_process: true
+    punct_post_process: true
+
+  text_tokenizer:
+    _target_: nemo.collections.common.tokenizers.text_to_speech.tts_tokenizers.EnglishPhonemesTokenizer
+    punct: true
+    stresses: true
+    chars: true
+    apostrophe: true
+    pad_with_space: true
+    g2p:
+      _target_: nemo.collections.tts.g2p.models.en_us_arpabet.EnglishG2p
+      phoneme_dict: ${phoneme_dict_path}
+      heteronyms: ${heteronyms_path}

scripts/dataset_processing/tts/ljspeech/get_data.py

@@ -0,0 +1,134 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.  All rights reserved.
+#
+# 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.
+
+import argparse
+import json
+import tarfile
+import urllib.request
+from pathlib import Path
+
+from tqdm import tqdm
+
+from nemo.utils.tar_utils import safe_extract
+
+try:
+    from nemo_text_processing.text_normalization.normalize import Normalizer
+except (ImportError, ModuleNotFoundError):
+    raise ModuleNotFoundError(
+        "The package `nemo_text_processing` was not installed in this environment. Please refer to"
+        " https://github.com/NVIDIA/NeMo-text-processing and install this package before using "
+        "this script"
+    )
+
+
+def get_args():
+    parser = argparse.ArgumentParser(description='Download LJSpeech and create manifests with predefined split')
+    parser.add_argument("--data-root", required=True, type=Path)
+
+    args = parser.parse_args()
+    return args
+
+
+URL = "https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2"
+FILELIST_BASE = 'https://raw.githubusercontent.com/NVIDIA/tacotron2/master/filelists'
+
+
+def _load_sox():
+    try:
+        import sox
+    except ImportError:
+        raise ImportError(
+            "Optional dependency 'sox' is required by this script. Install it with: pip install sox"
+        ) from None
+
+    return sox
+
+
+def __maybe_download_file(source_url, destination_path):
+    if not destination_path.exists():
+        tmp_file_path = destination_path.with_suffix('.tmp')
+        urllib.request.urlretrieve(source_url, filename=str(tmp_file_path))
+        tmp_file_path.rename(destination_path)
+
+
+def __extract_file(filepath, data_dir):
+    try:
+        with tarfile.open(filepath) as tar:
+            safe_extract(tar, str(data_dir))
+    except Exception:
+        print(f"Error while extracting {filepath}. Already extracted?")
+
+
+def __process_data(data_root):
+    sox = _load_sox()
+    text_normalizer = Normalizer(
+        lang="en",
+        input_case="cased",
+        overwrite_cache=True,
+        cache_dir=data_root / "cache_dir",
+    )
+    text_normalizer_call_kwargs = {"punct_pre_process": True, "punct_post_process": True}
+    normalizer_call = lambda x: text_normalizer.normalize(x, **text_normalizer_call_kwargs)
+
+    # Create manifests (based on predefined NVIDIA's split)
+    filelists = ['train', 'val', 'test']
+    for split in tqdm(filelists):
+        # Download file list if necessary
+        filelist_path = data_root / f"ljs_audio_text_{split}_filelist.txt"
+
+        if not filelist_path.exists():
+            urllib.request.urlretrieve(
+                f"{FILELIST_BASE}/ljs_audio_text_{split}_filelist.txt",
+                filename=str(filelist_path),
+            )
+
+        manifest_target = data_root / f"{split}_manifest.json"
+        with open(manifest_target, 'w') as f_out:
+            with open(filelist_path, 'r') as filelist:
+                print(f"\nCreating {manifest_target}...")
+                for line in tqdm(filelist):
+                    basename = line[6:16]
+
+                    text = line[21:].strip()
+                    norm_text = normalizer_call(text)
+
+                    # Make sure corresponding wavfile exists
+                    wav_path = data_root / 'wavs' / f"{basename}.wav"
+                    assert wav_path.exists(), f"{wav_path} does not exist!"
+
+                    entry = {
+                        'audio_filepath': str(wav_path),
+                        'duration': sox.file_info.duration(wav_path),
+                        'text': text,
+                        'normalized_text': norm_text,
+                    }
+
+                    f_out.write(json.dumps(entry) + '\n')
+
+
+def main():
+    args = get_args()
+
+    tarred_data_path = args.data_root / "LJSpeech-1.1.tar.bz2"
+
+    __maybe_download_file(URL, tarred_data_path)
+    __extract_file(str(tarred_data_path), str(args.data_root))
+
+    data_root = args.data_root / "LJSpeech-1.1"
+
+    __process_data(data_root)
+
+
+if __name__ == '__main__':
+    main()

scripts/dataset_processing/tts/ljspeech/lj_speech.tsv

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+Mr.	mister
+Mrs.	misses
+Dr.	doctor
+Drs.	doctors
+Co.	company
+Lt.	lieutenant
+Sgt.	sergeant
+St.	saint
+Jr.	junior
+Maj.	major
+Hon.	honorable
+Gov.	governor
+Capt.	captain
+Esq.	esquire
+Gen.	general
+Ltd.	limited
+Rev.	reverend
+Col.	colonel
+Mt.	mount
+Ft.	fort
+etc.	et cetera