Datasets:
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import os
import glob
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))
_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 + "https://dl.fbaipublicfiles.com/voxpopuli/annotations/unlabelled_v2.tsv.gz"
class Voxpopuli(datasets.GeneratorBasedBuilder):
"""The Voxpopuli dataset."""
VERSION = datasets.Version("1.0.0") # TODO ??
BUILDER_CONFIGS = [
datasets.BuilderConfig(
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),
}
)
return datasets.DatasetInfo(
# description=_DESCRIPTION,
features=features,
# homepage=_HOMEPAGE,
# license=_LICENSE,
# citation=_CITATION,
)
def _split_generators(self, dl_manager):
# dl_manager.download_config.num_proc = len(_VOXPOPULI_AUDIO_URLS) # TODO
# metadata_path = dl_manager.download_and_extract(_META_URL)
languages = [self.config.name] if self.config.name in _LANGUAGES else _LANGUAGES
years = _CONFIG_TO_YEARS[self.config.name]
# urls = [_DATA_URL.format(lang=language, year=year) for language in ["hr", "et"] for year in [2020]]
urls = [_DATA_URL.format(lang=language, year=year) for language in languages for year in years]
langs_data_dirs = dl_manager.download_and_extract(urls)
print(langs_data_dirs)
print(glob.glob(f"{langs_data_dirs[0]}/**/*.ogg", recursive=True))
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_dirs": langs_data_dirs,
}
),
]
def _generate_examples(self, data_dirs):
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 = path_components[-3:-1]
with open(file) as f:
yield file, {
"path": file,
"language": language,
"year": year,
"audio": {"path": file}
}
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