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"""Multilingual Librispeech automatic speech recognition dataset.""" |
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from functools import partial |
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import os |
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import datasets |
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from datasets.tasks import AutomaticSpeechRecognition |
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_CITATION = """\ |
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@article{Pratap2020MLSAL, |
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title={MLS: A Large-Scale Multilingual Dataset for Speech Research}, |
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author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert}, |
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journal={ArXiv}, |
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year={2020}, |
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volume={abs/2012.03411} |
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} |
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""" |
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_DESCRIPTION = """\ |
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This is a streamable version of the Multilingual LibriSpeech (MLS) dataset. |
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The data archives were restructured from the original ones from [OpenSLR](http://www.openslr.org/94) |
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to make it easier to stream. |
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MLS dataset is a large multilingual corpus suitable for speech research. |
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The dataset is derived from read audiobooks from LibriVox and consists of 8 languages: |
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English, German, Dutch, Spanish, French, Italian, Portuguese, Polish. |
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""" |
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_URL = "http://www.openslr.org/94" |
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_DL_URL_FORMAT = "data/mls_{name}" |
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class MultilingualLibrispeechConfig(datasets.BuilderConfig): |
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"""BuilderConfig for MultilingualLibrispeech.""" |
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def __init__(self, name, **kwargs): |
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""" |
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Args: |
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name: `string`, name of dataset config (=language) |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(MultilingualLibrispeechConfig, self).__init__( |
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version=datasets.Version("2.1.0", ""), name=name, **kwargs |
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) |
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self.data_root_dir = _DL_URL_FORMAT.format(name=name) |
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class MultilingualLibrispeech(datasets.GeneratorBasedBuilder): |
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"""Multilingual Librispeech dataset.""" |
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BUILDER_CONFIGS = [ |
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MultilingualLibrispeechConfig(name="german", description="German LibriSpeech dataset"), |
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MultilingualLibrispeechConfig(name="dutch", description="Dutch LibriSpeech dataset"), |
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MultilingualLibrispeechConfig(name="french", description="French LibriSpeech dataset"), |
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MultilingualLibrispeechConfig(name="spanish", description="Spanish LibriSpeech dataset"), |
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MultilingualLibrispeechConfig(name="italian", description="Italian LibriSpeech dataset"), |
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MultilingualLibrispeechConfig(name="portuguese", description="Portuguese LibriSpeech dataset"), |
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MultilingualLibrispeechConfig(name="polish", description="Polish LibriSpeech dataset"), |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"file": datasets.Value("string"), |
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"audio": datasets.features.Audio(sampling_rate=16_000), |
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"text": datasets.Value("string"), |
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"speaker_id": datasets.Value("int64"), |
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"chapter_id": datasets.Value("int64"), |
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"id": datasets.Value("string"), |
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} |
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), |
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supervised_keys=("file", "text"), |
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homepage=_URL, |
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citation=_CITATION, |
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task_templates=[AutomaticSpeechRecognition(audio_file_path_column="file", transcription_column="text")], |
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) |
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def _split_generators(self, dl_manager): |
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download_transcript = partial( |
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download_extract_transcript, dl_manager=dl_manager, root_dir=self.config.data_root_dir |
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) |
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download_audio = partial( |
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download_audio_archives, dl_manager=dl_manager, root_dir=self.config.data_root_dir |
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) |
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download_limited_ids = partial( |
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download_extract_limited_ids, dl_manager=dl_manager, root_dir=self.config.data_root_dir |
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) |
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train_kwargs = { |
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"transcript_path": download_transcript(split="train"), |
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"audio_archives": download_audio(split="train") |
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} |
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train_splits = [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, gen_kwargs=train_kwargs |
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), |
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datasets.SplitGenerator( |
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name="train.9h", |
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gen_kwargs={ |
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**train_kwargs, |
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"limited_ids_paths": download_limited_ids(sub_folder="limited_supervision/9hr"), |
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}, |
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), |
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datasets.SplitGenerator( |
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name="train.1h", |
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gen_kwargs={ |
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**train_kwargs, |
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"limited_ids_paths": download_limited_ids(sub_folder="limited_supervision/1hr"), |
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}, |
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), |
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] |
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return train_splits + [ |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, gen_kwargs={ |
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"transcript_path": download_transcript(split="dev"), |
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"audio_archives": download_audio(split="dev"), |
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} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, gen_kwargs={ |
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"transcript_path": download_transcript(split="test"), |
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"audio_archives": download_audio(split="test"), |
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} |
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), |
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] |
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def _generate_examples(self, transcript_path, audio_archives, limited_ids_paths=None): |
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"""Generate examples from a Multilingual LibriSpeech data dir.""" |
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transcripts = dict() |
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with open(transcript_path, "r", encoding="utf-8") as file: |
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for line in file: |
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audio_id, transcript = line.split("\t") |
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transcripts[audio_id] = transcript |
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limited_ids, limited_ids_archives_names = [], [] |
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if limited_ids_paths: |
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for path in limited_ids_paths: |
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with open(path, "r", encoding="utf-8") as file: |
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limited_ids.extend([line.strip() for line in file.readlines()]) |
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limited_ids = set(limited_ids) |
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for audio_archive in audio_archives: |
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for audio_filename, file in audio_archive: |
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speaker_id, chapter_id = audio_filename.split("_")[:2] |
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speaker_id, chapter_id = int(speaker_id), int(chapter_id) |
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audio_id = audio_filename.split(".flac")[0] |
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audio_transcript = transcripts[audio_id] |
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if limited_ids and audio_id not in limited_ids: |
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continue |
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yield audio_filename, { |
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"file": audio_filename, |
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"audio": {"path": audio_filename, "bytes": file.read()}, |
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"text": audio_transcript, |
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"speaker_id": speaker_id, |
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"chapter_id": chapter_id, |
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"id": audio_id |
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} |
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def download_extract_limited_ids(dl_manager, root_dir, sub_folder): |
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"""Download and extract all handles.txt files containing ids for limited supervision train sets. """ |
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sub_path = os.path.join(root_dir, "train", sub_folder) |
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if sub_folder.endswith("9hr"): |
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limited_ids_paths = [os.path.join(sub_path, "handles.txt")] |
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else: |
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limited_ids_paths = [os.path.join(sub_path, str(i), "handles.txt") for i in range(6)] |
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limited_ids_paths = dl_manager.download_and_extract(limited_ids_paths) |
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return limited_ids_paths |
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def download_extract_transcript(dl_manager, root_dir, split): |
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"""Downloading and extracting file with audio transcriptions. """ |
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transcript_path = os.path.join(root_dir, split, "transcripts.txt") |
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return dl_manager.download_and_extract(transcript_path) |
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def download_audio_archives(dl_manager, root_dir, split): |
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"""Prepare archives with audio files for iterating over them. |
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Return: |
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audio_archives (List `Generator`): list of generators to iterate over files in each audio archive. |
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""" |
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split_dir = os.path.join(root_dir, split) |
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audio_filenames_path = dl_manager.download_and_extract(os.path.join(split_dir, "audio_filenames.txt")) |
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with open(audio_filenames_path, "r", encoding="utf-8") as file: |
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audio_filenames = [line.strip() for line in file.readlines()] |
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archive_paths = dl_manager.download([os.path.join(split_dir, "audio", filename) for filename in audio_filenames]) |
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audio_archives = [dl_manager.iter_archive(archive_path) for archive_path in archive_paths] |
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return audio_archives |
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