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from collections import defaultdict |
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import os |
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import json |
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import csv |
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import datasets |
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_DESCRIPTION = """ |
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A large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation. |
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""" |
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_CITATION = """ |
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@inproceedings{wang-etal-2021-voxpopuli, |
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title = "{V}ox{P}opuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, |
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Semi-Supervised Learning and Interpretation", |
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author = "Wang, Changhan and |
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Riviere, Morgane and |
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Lee, Ann and |
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Wu, Anne and |
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Talnikar, Chaitanya and |
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Haziza, Daniel and |
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Williamson, Mary and |
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Pino, Juan and |
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Dupoux, Emmanuel", |
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booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics |
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and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", |
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month = aug, |
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year = "2021", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2021.acl-long.80", |
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doi = "10.18653/v1/2021.acl-long.80", |
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pages = "993--1003", |
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} |
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""" |
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_HOMEPAGE = "https://github.com/facebookresearch/voxpopuli" |
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_LICENSE = "CC0, also see https://www.europarl.europa.eu/legal-notice/en/" |
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_ASR_LANGUAGES = [ |
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"en", "de", "fr", "es", "pl", "it", "ro", "hu", "cs", "nl", "fi", "hr", |
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"sk", "sl", "et", "lt" |
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] |
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_ASR_ACCENTED_LANGUAGES = [ |
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"en_accented" |
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] |
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_LANGUAGES = _ASR_LANGUAGES + _ASR_ACCENTED_LANGUAGES |
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_BASE_DATA_DIR = "https://huggingface.co/datasets/polinaeterna/voxpopuli/resolve/main/data/" |
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_N_SHARDS_FILE = _BASE_DATA_DIR + "n_files.json" |
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_AUDIO_ARCHIVE_PATH = _BASE_DATA_DIR + "{lang}/{split}/{split}_part_{n_shard}.tar.gz" |
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_METADATA_PATH = _BASE_DATA_DIR + "{lang}/asr_{split}.tsv" |
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class VoxpopuliConfig(datasets.BuilderConfig): |
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"""BuilderConfig for VoxPopuli.""" |
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def __init__(self, name, languages="all", **kwargs): |
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""" |
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Args: |
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name: `string` or `List[string]`: |
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name of a config: either one of the supported languages or "multilang" for many languages. |
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By default, "multilang" config includes all languages, including accented ones. |
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To specify a custom set of languages, pass them to the `languages` parameter |
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languages: `List[string]`: if config is "multilang" can be either "all" for all available languages, |
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including accented ones (default), or a custom list of languages. |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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if name == "multilang": |
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self.languages = _ASR_LANGUAGES if languages == "all" else languages |
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name = "multilang" if languages == "all" else "_".join(languages) |
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else: |
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self.languages = [name] |
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super().__init__(name=name, **kwargs) |
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class Voxpopuli(datasets.GeneratorBasedBuilder): |
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"""The VoxPopuli dataset.""" |
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VERSION = datasets.Version("1.3.0") |
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BUILDER_CONFIGS = [ |
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VoxpopuliConfig( |
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name=name, |
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version=datasets.Version("1.3.0"), |
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) |
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for name in _LANGUAGES + ["multilang"] |
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] |
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DEFAULT_WRITER_BATCH_SIZE = 256 |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"audio_id": datasets.Value("string"), |
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"language": datasets.ClassLabel(names=_LANGUAGES), |
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"raw_text": datasets.Value("string"), |
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"normalized_text": datasets.Value("string"), |
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"gender": datasets.Value("string"), |
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"speaker_id": datasets.Value("string"), |
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"is_gold_transcript": datasets.Value("bool"), |
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"accent": datasets.Value("string"), |
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"audio": datasets.Audio(sampling_rate=16_000), |
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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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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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n_shards_path = dl_manager.download_and_extract(_N_SHARDS_FILE) |
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with open(n_shards_path) as f: |
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n_shards = json.load(f) |
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if self.config.name == "en_accented": |
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splits = ["test"] |
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else: |
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splits = ["train", "dev", "test"] |
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audio_urls = defaultdict(dict) |
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for split in splits: |
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for lang in self.config.languages: |
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audio_urls[split][lang] = [ |
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_AUDIO_ARCHIVE_PATH.format(lang=lang, split=split, n_shard=i) for i in range(n_shards[lang][split]) |
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] |
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meta_urls = defaultdict(dict) |
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for split in splits: |
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for lang in self.config.languages: |
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meta_urls[split][lang] = _METADATA_PATH.format(lang=lang, split=split) |
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meta_paths = dl_manager.download_and_extract(meta_urls) |
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audio_paths = dl_manager.download(audio_urls) |
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local_extracted_audio_paths = ( |
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dl_manager.extract(audio_paths) if not dl_manager.is_streaming else |
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{ |
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split: {lang: [None] * len(audio_paths[split][lang]) for lang in self.config.languages} for split in splits |
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} |
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) |
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if self.config.name == "en_accented": |
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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": { |
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lang: [dl_manager.iter_archive(archive) for archive in lang_archives] |
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for lang, lang_archives in audio_paths["test"].items() |
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}, |
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"local_extracted_archives_paths": local_extracted_audio_paths["test"], |
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"metadata_paths": meta_paths["test"], |
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} |
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), |
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] |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"audio_archives": { |
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lang: [dl_manager.iter_archive(archive) for archive in lang_archives] |
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for lang, lang_archives in audio_paths["train"].items() |
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}, |
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"local_extracted_archives_paths": local_extracted_audio_paths["train"], |
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"metadata_paths": meta_paths["train"], |
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} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"audio_archives": { |
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lang: [dl_manager.iter_archive(archive) for archive in lang_archives] |
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for lang, lang_archives in audio_paths["dev"].items() |
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}, |
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"local_extracted_archives_paths": local_extracted_audio_paths["dev"], |
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"metadata_paths": meta_paths["dev"], |
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} |
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), |
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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": { |
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lang: [dl_manager.iter_archive(archive) for archive in lang_archives] |
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for lang, lang_archives in audio_paths["test"].items() |
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}, |
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"local_extracted_archives_paths": local_extracted_audio_paths["test"], |
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"metadata_paths": meta_paths["test"], |
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} |
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), |
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] |
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def _generate_examples(self, audio_archives, local_extracted_archives_paths, metadata_paths): |
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assert len(metadata_paths) == len(audio_archives) == len(local_extracted_archives_paths) |
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features = ["raw_text", "normalized_text", "speaker_id", "gender", "is_gold_transcript", "accent"] |
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for lang in self.config.languages: |
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assert len(audio_archives[lang]) == len(local_extracted_archives_paths[lang]) |
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meta_path = metadata_paths[lang] |
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with open(meta_path) as f: |
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metadata = {x["id"]: x for x in csv.DictReader(f, delimiter="\t")} |
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for audio_archive, local_extracted_archive_path in zip(audio_archives[lang], local_extracted_archives_paths[lang]): |
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for audio_filename, audio_file in audio_archive: |
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audio_id = audio_filename.split(os.sep)[-1].split(".wav")[0] |
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path = os.path.join(local_extracted_archive_path, audio_filename) if local_extracted_archive_path else audio_filename |
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yield audio_id, { |
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"audio_id": audio_id, |
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"language": lang, |
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**{feature: metadata[audio_id][feature] for feature in features}, |
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"audio": {"path": path, "bytes": audio_file.read()}, |
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} |
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