Datasets:
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from collections import defaultdict
import os
import glob
import csv
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 = 256
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):
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)
# split audio on the fly and write segments as arrays
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,
}
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