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Create libritts_asr_builder.py

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libritts_asr_builder.py ADDED
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+ # coding=utf-8
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+ # Copyright 2024 blabble.io
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
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+ # you may not use this file except in compliance with the License.
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+ # You may obtain a copy of the License at
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+ #
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+ # http://www.apache.org/licenses/LICENSE-2.0
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+ #
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+ # Unless required by applicable law or agreed to in writing, software
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+ # distributed under the License is distributed on an "AS IS" BASIS,
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+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+ # See the License for the specific language governing permissions and
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+ # limitations under the License.
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+ import os
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+
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+ import datasets
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+
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+ _CITATION = """\
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+ @ARTICLE{Koizumi2023-hs,
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+ title = "{LibriTTS-R}: A restored multi-speaker text-to-speech corpus",
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+ author = "Koizumi, Yuma and Zen, Heiga and Karita, Shigeki and Ding,
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+ Yifan and Yatabe, Kohei and Morioka, Nobuyuki and Bacchiani,
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+ Michiel and Zhang, Yu and Han, Wei and Bapna, Ankur",
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+ abstract = "This paper introduces a new speech dataset called
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+ ``LibriTTS-R'' designed for text-to-speech (TTS) use. It is
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+ derived by applying speech restoration to the LibriTTS
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+ corpus, which consists of 585 hours of speech data at 24 kHz
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+ sampling rate from 2,456 speakers and the corresponding
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+ texts. The constituent samples of LibriTTS-R are identical
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+ to those of LibriTTS, with only the sound quality improved.
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+ Experimental results show that the LibriTTS-R ground-truth
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+ samples showed significantly improved sound quality compared
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+ to those in LibriTTS. In addition, neural end-to-end TTS
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+ trained with LibriTTS-R achieved speech naturalness on par
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+ with that of the ground-truth samples. The corpus is freely
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+ available for download from
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+ \textbackslashurl\{http://www.openslr.org/141/\}.",
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+ month = may,
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+ year = 2023,
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+ copyright = "http://creativecommons.org/licenses/by-nc-nd/4.0/",
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+ archivePrefix = "arXiv",
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+ primaryClass = "eess.AS",
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+ eprint = "2305.18802"
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+ }
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+ """
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+
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+ _DESCRIPTION = """\
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+ LibriTTS-R [1] is a sound quality improved version of the LibriTTS corpus (http://www.openslr.org/60/) which is a
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+ multi-speaker English corpus of approximately 585 hours of read English speech at 24kHz sampling rate,
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+ published in 2019. The constituent samples of LibriTTS-R are identical to those of LibriTTS, with only the sound
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+ quality improved. To improve sound quality, a speech restoration model, Miipher proposed by Yuma Koizumi [2], was used.
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+ """
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+
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+ _HOMEPAGE = "https://www.openslr.org/141/"
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+
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+ _LICENSE = "CC BY 4.0"
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+
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+ _DL_URL = "https://us.openslr.org/resources/141/"
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+
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+ _DATA_URLS = {
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+ 'dev.clean': _DL_URL + 'dev_clean.tar.gz',
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+ 'dev.other': _DL_URL + 'dev_other.tar.gz',
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+ 'test.clean': _DL_URL + 'test_clean.tar.gz',
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+ 'test.other': _DL_URL + 'test_other.tar.gz',
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+ 'train.clean.100': _DL_URL + 'train_clean_100.tar.gz',
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+ 'train.clean.360': _DL_URL + 'train_clean_360.tar.gz',
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+ 'train.other.500': _DL_URL + 'train_other_500.tar.gz',
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+ }
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+
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+
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+ def _generate_transcripts(transcript_csv_file):
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+ """Generates partial examples from transcript CSV file."""
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+ for line in transcript_csv_file:
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+ key, text_original, text_normalized = line.decode("utf-8").replace('\n', '').split("\t")
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+ speaker_id, chapter_id = [int(el) for el in key.split("_")[:2]]
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+ example = {
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+ "text_normalized": text_normalized,
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+ "text_original": text_original,
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+ "speaker_id": speaker_id,
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+ "chapter_id": chapter_id,
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+ "id_": key,
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+ }
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+ yield example
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+
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+
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+ class LibriTTS_R_Dataset(datasets.GeneratorBasedBuilder):
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+ """
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+ LibriTTS-R [1] is a sound quality improved version of the LibriTTS corpus (http://www.openslr.org/60/) which is a
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+ multi-speaker English corpus of approximately 585 hours of read English speech at 24kHz sampling rate,
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+ published in 2019.
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+ """
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+
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+ VERSION = datasets.Version("1.0.0")
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+
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+ DEFAULT_CONFIG_NAME = "all"
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+ BUILDER_CONFIGS = [
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+ datasets.BuilderConfig(name="dev", description="Only the 'dev.clean' split."),
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+ datasets.BuilderConfig(name="clean", description="'Clean' speech."),
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+ datasets.BuilderConfig(name="other", description="'Other', more challenging, speech."),
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+ datasets.BuilderConfig(name="all", description="Combined clean and other dataset."),
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+ ]
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+
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+ def _info(self):
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+ return datasets.DatasetInfo(
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+ # This is the description that will appear on the datasets page.
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+ description=_DESCRIPTION,
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+ features=datasets.Features(
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+ {
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+ "audio": datasets.Audio(sampling_rate=24_000),
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+ "text_normalized": datasets.Value("string"),
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+ "text_original": datasets.Value("string"),
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+ "speaker_id": datasets.Value("string"),
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+ "path": datasets.Value("string"),
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+ "chapter_id": datasets.Value("string"),
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+ "id": datasets.Value("string"),
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+ }
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+ ),
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+ supervised_keys=None,
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+ homepage=_HOMEPAGE,
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+ license=_LICENSE,
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+ citation=_CITATION,
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ split_names = _DATA_URLS.keys()
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+
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+ if self.config.name == "clean":
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+ split_names = [k for k in _DATA_URLS.keys() if 'clean' in k]
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+ elif self.config.name == "other":
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+ split_names = [k for k in _DATA_URLS.keys() if 'other' in k]
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+
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+ archive_path = dl_manager.download({k: v for k, v in _DATA_URLS.items() if k in split_names})
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+
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+ # (Optional) In non-streaming mode, we can extract the archive locally to have actual local audio files:
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+ local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else {}
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+
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+ all_splits = [
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+ datasets.SplitGenerator(
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+ name=split_name,
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+ gen_kwargs={
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+ "local_extracted_archive": local_extracted_archive.get(split_name),
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+ "files": dl_manager.iter_archive(archive_path[split_name]),
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+ "split_name": split_name
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+ },
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+ ) for split_name in split_names
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+ ]
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+
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+ return all_splits
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+
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+ def _generate_examples(self, split_name, files, local_extracted_archive):
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+ """Generate examples from a LibriTTS-R archive_path."""
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+ audio_extension = '.wav'
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+
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+ key = 0
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+ all_audio_data = {}
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+ transcripts = {}
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+
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+ def get_return_data(transcript, audio_data):
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+ nonlocal key
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+
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+ audio = {"path": transcript["path"], "bytes": audio_data}
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+ key += 1
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+
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+ return key, {"audio": audio, **transcript}
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+
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+ for path, f in files:
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+ if path.endswith(audio_extension):
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+ id_ = path.split("/")[-1][: -len(audio_extension)]
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+
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+ audio_data = f.read()
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+
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+ # If we already have the transcript for this audio, yield it right away
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+ # Otherwise, save it for when we get the transcript.
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+ transcript = transcripts.get(id_, None)
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+
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+ if transcript is not None:
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+ yield get_return_data(transcript, audio_data)
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+ del transcripts[id_]
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+ else:
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+ all_audio_data[id_] = f.read()
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+
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+ elif path.endswith(".trans.tsv"):
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+ for example in _generate_transcripts(f):
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+ example_id = example['id_']
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+
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+ audio_file = f"{example_id}{audio_extension}"
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+
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+ # TODO: this path is probably not right, there are subdirectories
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+ audio_file = (
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+ os.path.join(
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+ local_extracted_archive, 'LibriTTS_R',
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+ split_name.replace('.', '-'),
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+ str(example['speaker_id']), str(example['chapter_id']), audio_file)
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+ if local_extracted_archive
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+ else audio_file
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+ )
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+
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+ transcript = {
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+ "id": example_id,
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+ "speaker_id": example['speaker_id'],
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+ "chapter_id": example['chapter_id'],
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+ "text_normalized": example['text_normalized'],
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+ "text_original": example['text_original'],
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+ "path": audio_file,
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+ }
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+
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+ # If we already have the audio for this transcript, yield it right away
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+ # Otherwise, save it for when we get the audio.
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+ audio_data = all_audio_data.get(example_id, None)
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+ if audio_data is not None:
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+ yield get_return_data(transcript, audio_data)
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+ del all_audio_data[example_id]
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+ else:
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+ transcripts[example_id] = transcript
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+
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+ for id_, audio_data in all_audio_data.items():
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+ transcript = transcripts.get(id_, None)
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+
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+ if transcript is None:
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+ # for debugging, this dataset has extra audio
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+ # print(f"[libritts_r {split_name}] Audio without transcript: {id_}")
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+ continue
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+
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+ else:
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+ yield get_return_data(transcript, audio_data)
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+ del transcripts[id_]
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+
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+ for id_, transcript in transcripts.items():
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+ audio_data = all_audio_data.get(id_, None)
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+
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+ if audio_data is None:
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+ # for debugging, this dataset has extra transcripts
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+ # print(f"[libritts_r {split_name}] Transcript without audio: {id_}")
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+ continue
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+
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+ else:
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+ yield get_return_data(audio_data, transcript)
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+ # no del needed here