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""" |
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Aishell dataset. |
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""" |
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import os |
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import datasets |
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from huggingface_hub import list_repo_files |
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import gzip |
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import json |
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repo_id = "yuekai/aishell" |
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_DESCRIPTION = """\ |
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aishell |
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""" |
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_HOMEPAGE = "https://github.com/SpeechColab/Aishell" |
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_SUBSETS = ("train", "dev", "test") |
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_BASE_DATA_URL = f"https://huggingface.co/datasets/{repo_id}/resolve/main/" |
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_AUDIO_ARCHIVE_URL = _BASE_DATA_URL + "data/aishell_cuts_{subset}.{archive_id:08}.tar.gz" |
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_META_URL = _BASE_DATA_URL + "data/aishell_cuts_{subset}.{archive_id:08}.jsonl.gz" |
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FILES = list_repo_files(repo_id, repo_type="dataset") |
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logger = datasets.utils.logging.get_logger(__name__) |
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class CustomAudioConfig(datasets.BuilderConfig): |
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"""BuilderConfig for the dataset.""" |
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def __init__(self, name, *args, **kwargs): |
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"""BuilderConfig for the dataset. |
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""" |
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super().__init__(name=name, *args, **kwargs) |
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assert name in _SUBSETS, f"Unknown subset {name}" |
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self.subsets_to_download = (name,) |
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class Aishell(datasets.GeneratorBasedBuilder): |
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""" |
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Aishell is an evolving, multi-domain English speech recognition corpus with 10,000 hours of high quality |
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labeled audio suitable for supervised training, and 40,000 hours of total audio suitable for semi-supervised |
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and unsupervised training (this implementation contains only labelled data for now). |
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Around 40,000 hours of transcribed audio is first collected from audiobooks, podcasts |
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and YouTube, covering both read and spontaneous speaking styles, and a variety of topics, such as arts, science, |
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sports, etc. A new forced alignment and segmentation pipeline is proposed to create sentence segments suitable |
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for speech recognition training, and to filter out segments with low-quality transcription. For system training, |
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Aishell provides five subsets of different sizes, 10h, 250h, 1000h, 2500h, and 10000h. |
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For our 10,000-hour XL training subset, we cap the word error rate at 4% during the filtering/validation stage, |
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and for all our other smaller training subsets, we cap it at 0%. The DEV and TEST evaluation sets, on the other hand, |
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are re-processed by professional human transcribers to ensure high transcription quality. |
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""" |
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VERSION = datasets.Version("1.0.0") |
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BUILDER_CONFIGS = [CustomAudioConfig(name=subset) for subset in _SUBSETS] |
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DEFAULT_WRITER_BATCH_SIZE = 128 |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"segment_id": datasets.Value("string"), |
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"speaker": datasets.Value("string"), |
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"text": datasets.Value("string"), |
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"audio": datasets.Audio(sampling_rate=16_000), |
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"original_full_path": datasets.Value("string"), |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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) |
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@property |
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def _splits_to_subsets(self): |
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return { |
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"train": ['train'], |
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"dev": ["dev"], |
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"test": ["test"] |
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} |
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def _split_generators(self, dl_manager): |
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splits_to_subsets = self._splits_to_subsets |
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splits = (self.config.name,) |
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split_to_n_archives = { |
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split: int(len([file for file in FILES if f"cuts_{splits_to_subsets[split][0]}" in file]) / 2) |
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for split in splits |
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} |
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audio_archives_urls = { |
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split: |
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[ |
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_AUDIO_ARCHIVE_URL.format(subset=splits_to_subsets[split][0], |
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archive_id=i) |
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for i in range(split_to_n_archives[split]) |
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] |
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for split in splits |
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} |
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audio_archives_paths = dl_manager.download(audio_archives_urls) |
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local_audio_archives_paths = dl_manager.extract(audio_archives_paths) if not dl_manager.is_streaming \ |
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else None |
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meta_urls = { |
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split: [ |
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_META_URL.format(subset=splits_to_subsets[split][0], archive_id=i) |
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for i in range(split_to_n_archives[split]) |
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] |
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for split in splits |
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} |
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meta_paths = dl_manager.download(meta_urls) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"audio_archives_iterators": [ |
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dl_manager.iter_archive(archive_path) for archive_path in audio_archives_paths[self.config.name] |
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], |
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"local_audio_archives_paths": local_audio_archives_paths[ |
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self.config.name] if local_audio_archives_paths else None, |
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"meta_paths": meta_paths[self.config.name] |
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}, |
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), |
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] |
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def _generate_examples(self, audio_archives_iterators, local_audio_archives_paths, meta_paths): |
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def load_meta(file_path): |
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data = {} |
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with gzip.open(file_path, 'rt', encoding='utf-8') as f: |
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for line in f: |
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item = json.loads(line) |
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data[item["id"]] = item |
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return data |
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assert len(audio_archives_iterators) == len(meta_paths) |
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if local_audio_archives_paths: |
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assert len(audio_archives_iterators) == len(local_audio_archives_paths) |
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for i, (meta_path, audio_archive_iterator) in enumerate(zip(meta_paths, audio_archives_iterators)): |
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meta_dict = load_meta(meta_path) |
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for audio_path_in_archive, audio_file in audio_archive_iterator: |
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audio_filename = os.path.split(audio_path_in_archive)[-1] |
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audio_id = audio_filename.split(".wav")[0] |
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audio_meta = meta_dict[audio_id] |
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audio_meta["segment_id"] = audio_id |
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audio_meta["original_full_path"] = audio_meta["recording"]["sources"][0]["source"] |
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audio_meta["text"] = audio_meta['supervisions'][0]['text'] |
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audio_meta["speaker"] = audio_meta['supervisions'][0]['speaker'] |
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path = os.path.join(local_audio_archives_paths[i], audio_path_in_archive) if local_audio_archives_paths \ |
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else audio_path_in_archive |
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yield audio_id, { |
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"audio": {"path": path , "bytes": audio_file.read()}, |
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**{feature: value for feature, value in audio_meta.items() if feature in self.info.features} |
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} |
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