Datasets:

ai4bharat
/

kathbath

	""" Kathbath Dataset"""

	import csv
	import os
	import tarfile

	import datasets
	from datasets.utils.py_utils import size_str

	from .languages import LANGUAGES
	from .release_stats import STATS

	_CITATION = """\
	@misc{https://doi.org/10.48550/arxiv.2208.11761,
	doi = {10.48550/ARXIV.2208.11761},
	url = {https://arxiv.org/abs/2208.11761},
	author = {Javed, Tahir and Bhogale, Kaushal Santosh and Raman, Abhigyan and Kunchukuttan, Anoop and Kumar, Pratyush and Khapra, Mitesh M.},
	title = {IndicSUPERB: A Speech Processing Universal Performance Benchmark for Indian languages},
	publisher = {arXiv},
	year = {2022},
	copyright = {arXiv.org perpetual, non-exclusive license}
	}
	"""

	_HOMEPAGE = "https://ai4bharat.iitm.ac.in/indic-superb/"

	_LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/"

	_DATA_URL = "https://huggingface.co/datasets/ai4bharat/kathbath/resolve/main/data"


	class KathbathConfig(datasets.BuilderConfig):
	"""BuilderConfig for Kathbath."""

	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"Kathbath 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(KathbathConfig, self).__init__(
	name=name,
	version=datasets.Version(version),
	description=description,
	**kwargs,
	)


	class Kathbath(datasets.GeneratorBasedBuilder):
	DEFAULT_CONFIG_NAME = "_all_"

	BUILDER_CONFIGS = [
	KathbathConfig(
	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"),
	"speaker": datasets.Value("string"),
	"sentence": datasets.Value("string"),
	"audio": datasets.features.Audio(sampling_rate=16000)
	}
	)

	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, "valid": datasets.Split.VALIDATION, "test_unknown": datasets.Split.TEST, "test_known": datasets.Split.TEST}
	for split in split_type:

	if split == 'train':
	audio_paths = [
	f"{_DATA_URL}/audio_{split}.tar.partaa",
	f"{_DATA_URL}/audio_{split}.tar.partab",
	f"{_DATA_URL}/audio_{split}.tar.partac",
	]
	audio_path[split] = dl_manager.download(audio_paths)
	for path in audio_path[split]:
	try:
	local_extracted_archive[split] = dl_manager.extract(audio_path[split]) if not dl_manager.is_streaming else None
	except tarfile.ReadError:
	pass
	else:
	audio_paths = [f"{_DATA_URL}/audio_{split}.tar"]
	audio_path[split] = dl_manager.download(audio_paths)
	local_extracted_archive[split] = dl_manager.extract(audio_path[split]) if not dl_manager.is_streaming else None
	metadata_path[split] = dl_manager.download(f"{_DATA_URL}/metata_{split}.tsv")
	path_to_clips = "kb_data_clean_m4a"

	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": 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, delimiter="\t")
	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 in audio_files:
	print(path)
	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