Datasets:
Tasks:
Automatic Speech Recognition
Multilinguality:
multilingual
Size Categories:
1K<n<10K
Language Creators:
crowdsourced
Annotations Creators:
crowdsourced
Source Datasets:
librivox
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. | |
""" LibriVox-Indonesia Dataset""" | |
import csv | |
import os | |
import datasets | |
from datasets.utils.py_utils import size_str | |
from .languages import LANGUAGES | |
from .release_stats import STATS | |
_CITATION = """\ | |
""" | |
_HOMEPAGE = "https://huggingface.co/indonesian-nlp/librivox-indonesia" | |
_LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/" | |
_DATA_URL = "https://huggingface.co/datasets/indonesian-nlp/librivox-indonesia/resolve/main/data" | |
class LibriVoxIndonesiaConfig(datasets.BuilderConfig): | |
"""BuilderConfig for LibriVoxIndonesia.""" | |
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.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"LibriVox-Indonesia speech to text dataset in {self.language} released on {self.release_date}. " | |
f"The dataset comprises {self.total_hr} hours of transcribed speech data" | |
) | |
super(LibriVoxIndonesiaConfig, self).__init__( | |
name=name, | |
version=datasets.Version(version), | |
description=description, | |
**kwargs, | |
) | |
class LibriVoxIndonesia(datasets.GeneratorBasedBuilder): | |
DEFAULT_CONFIG_NAME = "_all_" | |
BUILDER_CONFIGS = [ | |
LibriVoxIndonesiaConfig( | |
name=lang, | |
version=STATS["version"], | |
language=LANGUAGES[lang], | |
release_date=STATS["date"], | |
num_clips=lang_stats["clips"], | |
num_speakers=lang_stats["users"], | |
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_hours = self.config.total_hr | |
description = ( | |
"LibriVox-Indonesia is a speech dataset generated from LibriVox with only languages from Indonesia." | |
f"The dataset currently consists of {total_hours} hours of speech " | |
f"in {total_languages} languages, but more voices and languages are always added." | |
) | |
features = datasets.Features( | |
{ | |
"path": datasets.Value("string"), | |
"language": datasets.Value("string"), | |
"reader": datasets.Value("string"), | |
"sentence": datasets.Value("string"), | |
"audio": datasets.features.Audio(sampling_rate=44100) | |
} | |
) | |
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): | |
"""Returns SplitGenerators.""" | |
dl_manager.download_config.ignore_url_params = True | |
audio_path = {} | |
local_extracted_archive = {} | |
metadata_path = {} | |
split_type = {"train": datasets.Split.TRAIN, "test": datasets.Split.TEST} | |
for split in split_type: | |
audio_path[split] = dl_manager.download(f"{_DATA_URL}/audio_{split}.tgz") | |
local_extracted_archive[split] = dl_manager.extract(audio_path[split]) if not dl_manager.is_streaming else None | |
metadata_path[split] = dl_manager.download_and_extract(f"{_DATA_URL}/metadata_{split}.csv.gz") | |
path_to_clips = "librivox-indonesia" | |
return [ | |
datasets.SplitGenerator( | |
name=split_type[split], | |
gen_kwargs={ | |
"local_extracted_archive": local_extracted_archive[split], | |
"audio_files": dl_manager.iter_archive(audio_path[split]), | |
"metadata_path": dl_manager.download_and_extract(metadata_path[split]), | |
"path_to_clips": path_to_clips, | |
}, | |
) for split in split_type | |
] | |
def _generate_examples( | |
self, | |
local_extracted_archive, | |
audio_files, | |
metadata_path, | |
path_to_clips, | |
): | |
"""Yields examples.""" | |
data_fields = list(self._info().features.keys()) | |
metadata = {} | |
with open(metadata_path, "r", encoding="utf-8") as f: | |
reader = csv.DictReader(f) | |
for row in reader: | |
if self.config.name == "_all_" or self.config.name == row["language"]: | |
row["path"] = os.path.join(path_to_clips, row["path"]) | |
# if data is incomplete, fill with empty values | |
for field in data_fields: | |
if field not in row: | |
row[field] = "" | |
metadata[row["path"]] = row | |
id_ = 0 | |
for path, f in audio_files: | |
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()} | |
result["path"] = path | |
yield id_, result | |
id_ += 1 | |