Datasets:
Tasks:
Automatic Speech Recognition
Multilinguality:
multilingual
Language Creators:
crowdsourced
Annotations Creators:
crowdsourced
Source Datasets:
extended|common_voice
ArXiv:
Tags:
License:
# coding=utf-8 | |
# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
""" Common Voice Dataset""" | |
import csv | |
import os | |
import urllib | |
import datasets | |
import requests | |
from datasets.utils.py_utils import size_str | |
from huggingface_hub import HfApi, HfFolder | |
from .languages import LANGUAGES | |
from .release_stats import STATS | |
_CITATION = """\ | |
@inproceedings{commonvoice:2020, | |
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.}, | |
title = {Common Voice: A Massively-Multilingual Speech Corpus}, | |
booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)}, | |
pages = {4211--4215}, | |
year = 2020 | |
} | |
""" | |
_HOMEPAGE = "https://commonvoice.mozilla.org/en/datasets" | |
_LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/" | |
_API_URL = "https://commonvoice.mozilla.org/api/v1" | |
class CommonVoiceConfig(datasets.BuilderConfig): | |
"""BuilderConfig for CommonVoice.""" | |
def __init__(self, name, version, **kwargs): | |
self.language = kwargs.pop("language", None) | |
self.release_date = kwargs.pop("release_date", None) | |
self.num_clips = kwargs.pop("num_clips", None) | |
self.num_speakers = kwargs.pop("num_speakers", None) | |
self.validated_hr = kwargs.pop("validated_hr", None) | |
self.total_hr = kwargs.pop("total_hr", None) | |
self.size_bytes = kwargs.pop("size_bytes", None) | |
self.size_human = size_str(self.size_bytes) | |
description = ( | |
f"Common Voice speech to text dataset in {self.language} released on {self.release_date}. " | |
f"The dataset comprises {self.validated_hr} hours of validated transcribed speech data " | |
f"out of {self.total_hr} hours in total from {self.num_speakers} speakers. " | |
f"The dataset contains {self.num_clips} audio clips and has a size of {self.size_human}." | |
) | |
super(CommonVoiceConfig, self).__init__( | |
name=name, | |
version=datasets.Version(version), | |
description=description, | |
**kwargs, | |
) | |
class CommonVoice(datasets.GeneratorBasedBuilder): | |
DEFAULT_CONFIG_NAME = "en" | |
DEFAULT_WRITER_BATCH_SIZE = 1000 | |
BUILDER_CONFIGS = [ | |
CommonVoiceConfig( | |
name=lang, | |
version=STATS["version"], | |
language=LANGUAGES[lang], | |
release_date=STATS["date"], | |
num_clips=lang_stats["clips"], | |
num_speakers=lang_stats["users"], | |
validated_hr=float(lang_stats["validHrs"]) if lang_stats["validHrs"] else None, | |
total_hr=float(lang_stats["totalHrs"]) if lang_stats["totalHrs"] else None, | |
size_bytes=int(lang_stats["size"]) if lang_stats["size"] else None, | |
) | |
for lang, lang_stats in STATS["locales"].items() | |
] | |
def _info(self): | |
total_languages = len(STATS["locales"]) | |
total_valid_hours = STATS["totalValidHrs"] | |
description = ( | |
"Common Voice is Mozilla's initiative to help teach machines how real people speak. " | |
f"The dataset currently consists of {total_valid_hours} validated hours of speech " | |
f" in {total_languages} languages, but more voices and languages are always added." | |
) | |
features = datasets.Features( | |
{ | |
"client_id": datasets.Value("string"), | |
"path": datasets.Value("string"), | |
"audio": datasets.features.Audio(sampling_rate=48_000), | |
"sentence": datasets.Value("string"), | |
"up_votes": datasets.Value("int64"), | |
"down_votes": datasets.Value("int64"), | |
"age": datasets.Value("string"), | |
"gender": datasets.Value("string"), | |
"accent": datasets.Value("string"), | |
"locale": datasets.Value("string"), | |
"segment": datasets.Value("string"), | |
} | |
) | |
return datasets.DatasetInfo( | |
description=description, | |
features=features, | |
supervised_keys=None, | |
homepage=_HOMEPAGE, | |
license=_LICENSE, | |
citation=_CITATION, | |
version=self.config.version, | |
# task_templates=[ | |
# AutomaticSpeechRecognition(audio_file_path_column="path", transcription_column="sentence") | |
# ], | |
) | |
def _get_bundle_url(self, locale, url_template): | |
# path = encodeURIComponent(path) | |
path = url_template.replace("{locale}", locale) | |
path = urllib.parse.quote(path.encode("utf-8"), safe="~()*!.'") | |
# use_cdn = self.config.size_bytes < 20 * 1024 * 1024 * 1024 | |
# response = requests.get(f"{_API_URL}/bucket/dataset/{path}/{use_cdn}", timeout=10.0).json() | |
response = requests.get(f"{_API_URL}/bucket/dataset/{path}", timeout=10.0).json() | |
return response["url"] | |
def _log_download(self, locale, bundle_version, auth_token): | |
if isinstance(auth_token, bool): | |
auth_token = HfFolder().get_token() | |
whoami = HfApi().whoami(auth_token) | |
email = whoami["email"] if "email" in whoami else "" | |
payload = {"email": email, "locale": locale, "dataset": bundle_version} | |
requests.post(f"{_API_URL}/{locale}/downloaders", json=payload).json() | |
def _split_generators(self, dl_manager): | |
"""Returns SplitGenerators.""" | |
hf_auth_token = dl_manager.download_config.use_auth_token | |
if hf_auth_token is None: | |
raise ConnectionError( | |
"Please set use_auth_token=True or use_auth_token='<TOKEN>' to download this dataset" | |
) | |
bundle_url_template = STATS["bundleURLTemplate"] | |
bundle_version = bundle_url_template.split("/")[0] | |
dl_manager.download_config.ignore_url_params = True | |
self._log_download(self.config.name, bundle_version, hf_auth_token) | |
archive_path = dl_manager.download(self._get_bundle_url(self.config.name, bundle_url_template)) | |
local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else None | |
if self.config.version < datasets.Version("5.0.0"): | |
path_to_data = "" | |
else: | |
path_to_data = "/".join([bundle_version, self.config.name]) | |
path_to_clips = "/".join([path_to_data, "clips"]) if path_to_data else "clips" | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={ | |
"local_extracted_archive": local_extracted_archive, | |
"archive_iterator": dl_manager.iter_archive(archive_path), | |
"metadata_filepath": "/".join([path_to_data, "train.tsv"]) if path_to_data else "train.tsv", | |
"path_to_clips": path_to_clips, | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs={ | |
"local_extracted_archive": local_extracted_archive, | |
"archive_iterator": dl_manager.iter_archive(archive_path), | |
"metadata_filepath": "/".join([path_to_data, "test.tsv"]) if path_to_data else "test.tsv", | |
"path_to_clips": path_to_clips, | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
gen_kwargs={ | |
"local_extracted_archive": local_extracted_archive, | |
"archive_iterator": dl_manager.iter_archive(archive_path), | |
"metadata_filepath": "/".join([path_to_data, "dev.tsv"]) if path_to_data else "dev.tsv", | |
"path_to_clips": path_to_clips, | |
}, | |
), | |
datasets.SplitGenerator( | |
name="other", | |
gen_kwargs={ | |
"local_extracted_archive": local_extracted_archive, | |
"archive_iterator": dl_manager.iter_archive(archive_path), | |
"metadata_filepath": "/".join([path_to_data, "other.tsv"]) if path_to_data else "other.tsv", | |
"path_to_clips": path_to_clips, | |
}, | |
), | |
datasets.SplitGenerator( | |
name="invalidated", | |
gen_kwargs={ | |
"local_extracted_archive": local_extracted_archive, | |
"archive_iterator": dl_manager.iter_archive(archive_path), | |
"metadata_filepath": "/".join([path_to_data, "invalidated.tsv"]) | |
if path_to_data | |
else "invalidated.tsv", | |
"path_to_clips": path_to_clips, | |
}, | |
), | |
] | |
def _generate_examples( | |
self, | |
local_extracted_archive, | |
archive_iterator, | |
metadata_filepath, | |
path_to_clips, | |
): | |
"""Yields examples.""" | |
data_fields = list(self._info().features.keys()) | |
metadata = {} | |
metadata_found = False | |
for path, f in archive_iterator: | |
if path == metadata_filepath: | |
metadata_found = True | |
lines = (line.decode("utf-8") for line in f) | |
reader = csv.DictReader(lines, delimiter="\t", quoting=csv.QUOTE_NONE) | |
for row in reader: | |
# set absolute path for mp3 audio file | |
if not row["path"].endswith(".mp3"): | |
row["path"] += ".mp3" | |
row["path"] = os.path.join(path_to_clips, row["path"]) | |
# accent -> accents in CV 8.0 | |
if "accents" in row: | |
row["accent"] = row["accents"] | |
del row["accents"] | |
# if data is incomplete, fill with empty values | |
for field in data_fields: | |
if field not in row: | |
row[field] = "" | |
metadata[row["path"]] = row | |
elif path.startswith(path_to_clips): | |
assert metadata_found, "Found audio clips before the metadata TSV file." | |
if not metadata: | |
break | |
if path in metadata: | |
result = dict(metadata[path]) | |
# set the audio feature and the path to the extracted file | |
path = os.path.join(local_extracted_archive, path) if local_extracted_archive else path | |
result["audio"] = {"path": path, "bytes": f.read()} | |
# set path to None if the audio file doesn't exist locally (i.e. in streaming mode) | |
result["path"] = path if local_extracted_archive else None | |
yield path, result | |