from collections import defaultdict import os import glob import csv from pathlib import Path from tqdm.auto import tqdm import datasets _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] _YEARS = list(range(2009, 2020 + 1)) # unnecessary _CONFIG_TO_LANGS = { "400k": _LANGUAGES, "100k": _LANGUAGES, "10k": _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 _BASE_URL = "https://dl.fbaipublicfiles.com/voxpopuli/" _DATA_URL = _BASE_URL + "audios/{lang}_{year}.tar" _META_URL = _BASE_URL + "annotations/unlabelled_v2.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) self.languages = [name] if name in _LANGUAGES else _LANGUAGES class Voxpopuli(datasets.GeneratorBasedBuilder): """The Voxpopuli dataset.""" VERSION = datasets.Version("1.0.0") # TODO ?? BUILDER_CONFIGS = [ VoxpopuliConfig( name=name, # version=VERSION, description="", # TODO ) for name in _LANGUAGES + ["10k", "100k", "400k"] ] # DEFAULT_CONFIG_NAME = "400k" # DEFAULT_WRITER_BATCH_SIZE = 1 def _info(self): 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(self, metadata_path): # TODO: check for predicate?? # @ https://github.com/facebookresearch/voxpopuli/blob/main/voxpopuli/get_unlabelled_data.py#L34 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 audio_id, segment_id, start, end = row event_id, lang = audio_id.rsplit("_", 1)[-2:] if lang in self.languages: # if lang in ["hr", "et"]: metadata[audio_id].append((float(start), float(end))) return metadata def _split_generators(self, dl_manager): metadata_path = dl_manager.download_and_extract(_META_URL) years = _CONFIG_TO_YEARS[self.config.name] # urls = [_DATA_URL.format(lang=language, year=year) for language in ["hr", "et"] for year in [2020]] # , "et"] urls = [_DATA_URL.format(lang=language, year=year) for language in self.languages for year in 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): try: import torch import torchaudio except ImportError as e: raise ValueError( "Loading voxpopuli requires `torchaudio` to be installed." "You can install torchaudio with `pip install torchaudio`." + e ) metadata = self._read_metadata(metadata_path) for data_dir in data_dirs: for file in glob.glob(f"{data_dir}/**/*.ogg", recursive=True)[:5]: path_components = file.split(os.sep) language, year, audio_filename = path_components[-3:] audio_id, _ = os.path.splitext(audio_filename) timestamps = metadata[audio_id] waveform, sr = torchaudio.load(file) duration = waveform.size(1) 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, }