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