File size: 11,420 Bytes
a733f91 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 |
# 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
|