from collections import defaultdict import os import glob import csv from tqdm.auto import tqdm 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/" _LANGUAGES = sorted( [ "en", "de", "fr", "es", "pl", "it", "ro", "hu", "cs", "nl", "fi", "hr", "sk", "sl", "et", "lt", "pt", "bg", "el", "lv", "mt", "sv", "da" ] ) _LANGUAGES_V2 = [f"{x}_v2" for x in _LANGUAGES] _ASR_LANGUAGES = [ "en", "de", "fr", "es", "pl", "it", "ro", "hu", "cs", "nl", "fi", "hr", "sk", "sl", "et", "lt" ] _ASR_ACCENTED_LANGUAGES = [ "en_accented" ] _YEARS = list(range(2009, 2020 + 1)) # unnecessary _CONFIG_TO_LANGS = { "400k": _LANGUAGES, "100k": _LANGUAGES, "10k": _LANGUAGES, "asr": _ASR_LANGUAGES, # + _ASR_ACCENTED_LANGUAGES } _CONFIG_TO_YEARS = { "400k": _YEARS + [f"{y}_2" for y in _YEARS], "100k": _YEARS, "10k": [2019, 2020], "asr": _YEARS, } for lang in _LANGUAGES: _CONFIG_TO_YEARS[lang] = _YEARS # _CONFIG_TO_YEARS[lang] = [2020] for lang in _LANGUAGES_V2: _CONFIG_TO_YEARS[lang] = _YEARS + [f"{y}_2" for y in _YEARS] _BASE_URL = "https://dl.fbaipublicfiles.com/voxpopuli/" _DATA_URL = _BASE_URL + "audios/{lang}_{year}.tar" _ASR_DATA_URL = _BASE_URL + "audios/original_{year}.tar" _UNLABELLED_META_URL = _BASE_URL + "annotations/unlabelled_v2.tsv.gz" _ASR_META_URL = _BASE_URL + "annotations/asr/asr_{lang}.tsv.gz" class VoxpopuliConfig(datasets.BuilderConfig): """BuilderConfig for VoxPopuli.""" def __init__(self, name, **kwargs): """ Args: name: `string`, name of dataset config **kwargs: keyword arguments forwarded to super. """ super().__init__(name=name, **kwargs) name = name.split("_")[0] self.languages = [name] if name in _LANGUAGES else _CONFIG_TO_LANGS[name] self.years = _CONFIG_TO_YEARS[name] class Voxpopuli(datasets.GeneratorBasedBuilder): """The VoxPopuli dataset.""" VERSION = datasets.Version("1.3.0") # not sure BUILDER_CONFIGS = [ VoxpopuliConfig( name=name, version=datasets.Version("1.3.0"), ) for name in _LANGUAGES + _LANGUAGES_V2 + ["10k", "100k", "400k"] ] # DEFAULT_CONFIG_NAME = "400k" DEFAULT_WRITER_BATCH_SIZE = 256 # SET THIS TO A LOWER VALUE IF IT USES TOO MUCH RAM SPACE def _info(self): try: import torch import torchaudio except ImportError as e: raise ValueError( f"{str(e)}.\n" + "Loading voxpopuli requires `torchaudio` to be installed." "You can install torchaudio with `pip install torchaudio`." ) global torchaudio features = datasets.Features( { "path": datasets.Value("string"), "language": datasets.ClassLabel(names=_LANGUAGES), "year": datasets.Value("int16"), "audio": datasets.Audio(sampling_rate=16_000), "segment_id": datasets.Value("int16"), } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _read_metadata_unlabelled(self, metadata_path): # from https://github.com/facebookresearch/voxpopuli/blob/main/voxpopuli/get_unlabelled_data.py#L34 def predicate(id_): is_plenary = id_.find("PLENARY") > -1 if self.config.name == "10k": # in {"10k", "10k_sd"} return is_plenary and 20190101 <= int(id_[:8]) < 20200801 elif self.config.name == "100k": return is_plenary elif self.config.name in _LANGUAGES: return is_plenary and id_.endswith(self.config.name) elif self.config.name in _LANGUAGES_V2: return id_.endswith(self.config.name.split("_")[0]) return True metadata = defaultdict(list) with open(metadata_path, encoding="utf-8") as csv_file: csv_reader = csv.reader(csv_file, delimiter="\t") for i, row in tqdm(enumerate(csv_reader)): if i == 0: continue event_id, segment_id, start, end = row _, lang = event_id.rsplit("_", 1)[-2:] if lang in self.config.languages and predicate(event_id): metadata[event_id].append((float(start), float(end))) return metadata def _read_metadata_asr(self, metadata_paths): pass def _split_generators(self, dl_manager): metadata_path = dl_manager.download_and_extract(_UNLABELLED_META_URL) urls = [_DATA_URL.format(lang=language, year=year) for language in self.config.languages for year in self.config.years] dl_manager.download_config.num_proc = len(urls) data_dirs = dl_manager.download_and_extract(urls) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "data_dirs": data_dirs, "metadata_path": metadata_path, } ), ] def _generate_examples(self, data_dirs, metadata_path): metadata = self._read_metadata_unlabelled(metadata_path) for data_dir in data_dirs: for file in glob.glob(f"{data_dir}/**/*.ogg", recursive=True): path_components = file.split(os.sep) language, year, audio_filename = path_components[-3:] audio_id, _ = os.path.splitext(audio_filename) if audio_id not in metadata: continue timestamps = metadata[audio_id] waveform, sr = torchaudio.load(file) duration = waveform.size(1) # split audio on the fly and yield segments as arrays - they will be converted to bytes by Audio feature for segment_id, (start, stop) in enumerate(timestamps): segment = waveform[:, int(start * sr): min(int(stop * sr), duration)] yield f"{audio_filename}_{segment_id}", { "path": file, "language": language, "year": year, "audio": { "array": segment[0], # segment is a 2-dim array "sampling_rate": 16_000 }, "segment_id": segment_id, }