Datasets:

cahya
/

librivox-indonesia

	# 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