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# 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 json
import datasets
from datasets.utils.py_utils import size_str
from tqdm import tqdm
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/"
# TODO: change "streaming" to "main" after merge!
_BASE_URL = "https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/resolve/streaming/"
_AUDIO_URL = _BASE_URL + "audio/{lang}/{split}/{lang}_{split}_{shard_idx}.tar"
_TRANSCRIPT_URL = _BASE_URL + "transcript/{lang}/{split}.tsv"
_N_SHARDS_URL = _BASE_URL + "n_shards.json"
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_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,
)
def _split_generators(self, dl_manager):
lang = self.config.name
n_shards_path = dl_manager.download_and_extract(_N_SHARDS_URL)
with open(n_shards_path, encoding="utf-8") as f:
n_shards = json.load(f)
audio_urls = {}
splits = ("train", "dev", "test", "other", "invalidated")
for split in splits:
audio_urls[split] = [
_AUDIO_URL.format(lang=lang, split=split, shard_idx=i) for i in range(n_shards[lang][split])
]
archive_paths = dl_manager.download(audio_urls)
local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}
meta_urls = {split: _TRANSCRIPT_URL.format(lang=lang, split=split) for split in splits}
meta_paths = dl_manager.download_and_extract(meta_urls)
split_generators = []
split_names = {
"train": datasets.Split.TRAIN,
"dev": datasets.Split.VALIDATION,
"test": datasets.Split.TEST,
}
for split in splits:
split_generators.append(
datasets.SplitGenerator(
name=split_names.get(split, split),
gen_kwargs={
"local_extracted_archive_paths": local_extracted_archive_paths.get(split),
"archives": [dl_manager.iter_archive(path) for path in archive_paths.get(split)],
"meta_path": meta_paths[split],
},
),
)
return split_generators
def _generate_examples(self, local_extracted_archive_paths, archives, meta_path):
data_fields = list(self._info().features.keys())
metadata = {}
with open(meta_path, encoding="utf-8") as f:
reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
for row in tqdm(reader, desc="Reading metadata..."):
if not row["path"].endswith(".mp3"):
row["path"] += ".mp3"
# 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
for i, audio_archive in enumerate(archives):
for filename, file in audio_archive:
_, filename = os.path.split(filename)
if filename in metadata:
result = dict(metadata[filename])
# set the audio feature and the path to the extracted file
path = os.path.join(local_extracted_archive_paths[i], filename) if local_extracted_archive_paths else filename
result["audio"] = {"path": path, "bytes": file.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_paths else filename
yield path, result
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