Datasets:

parambharat
/

mile_dataset

	# Copyright 2020 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.

	"""IISc-MILE Tamil ASR Corpus contains transcribed speech corpus for training ASR systems for Tamil language. It contains ~150 hours of read speech data collected from 531 speakers in a noise-free recording environment with high quality USB microphones. """


	import json
	import os

	import datasets

	_CITATION = """\
	@misc{mile_1,
	doi = {10.48550/ARXIV.2207.13331},
	url = {https://arxiv.org/abs/2207.13331},
	author = {A, Madhavaraj and Pilar, Bharathi and G, Ramakrishnan A},
	title = {Subword Dictionary Learning and Segmentation Techniques for Automatic Speech Recognition in Tamil and Kannada},
	publisher = {arXiv},
	year = {2022},
	}

	@misc{mile_2,
	doi = {10.48550/ARXIV.2207.13333},
	url = {https://arxiv.org/abs/2207.13333},
	author = {A, Madhavaraj and Pilar, Bharathi and G, Ramakrishnan A},
	title = {Knowledge-driven Subword Grammar Modeling for Automatic Speech Recognition in Tamil and Kannada},
	publisher = {arXiv},
	year = {2022},
	}
	"""

	_DESCRIPTION = """\
	IISc-MILE Tamil ASR Corpus contains transcribed speech corpus for training ASR systems for Tamil language. It contains ~150 hours of read speech data collected from 531 speakers in a noise-free recording environment with high quality USB microphones.
	"""

	_HOMEPAGE = "https://www.openslr.org/127/"

	_LICENSE = "Attribution 2.0 Generic (CC BY 2.0)"


	_METADATA_URLS = {
	"train": "data/train.jsonl",
	"test": "data/test.jsonl"
	}
	_URLS = {
	"train": "data/train.tar.gz",
	"test": "data/test.tar.gz",

	}

	class MileDataset(datasets.GeneratorBasedBuilder):
	"""IISc-MILE Tamil ASR Corpus contains transcribed speech corpus for training ASR systems for Tamil language."""

	VERSION = datasets.Version("1.1.0")
	def _info(self):
	features = datasets.Features(
	{
	"audio": datasets.Audio(sampling_rate=16_000),
	"file_name": datasets.Value("string"),
	"sentence": datasets.Value("string"),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=("sentence", "label"),
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	metadata_paths = dl_manager.download(_METADATA_URLS)
	train_archive = dl_manager.download(_URLS["train"])
	test_archive = dl_manager.download(_URLS["test"])
	local_extracted_train_archive = dl_manager.extract(train_archive) if not dl_manager.is_streaming else None
	local_extracted_test_archive = dl_manager.extract(test_archive) if not dl_manager.is_streaming else None
	test_archive = dl_manager.download(_URLS["test"])
	train_dir = "train"
	test_dir = "test"

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"metadata_path": metadata_paths["train"],
	"local_extracted_archive": local_extracted_train_archive,
	"path_to_clips": train_dir + "/mp3",
	"audio_files": dl_manager.iter_archive(train_archive),
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"metadata_path": metadata_paths["test"],
	"local_extracted_archive": local_extracted_test_archive,
	"path_to_clips": test_dir + "/mp3",
	"audio_files": dl_manager.iter_archive(test_archive),
	},
	),

	]

	def _generate_examples(self, metadata_path, local_extracted_archive, path_to_clips, audio_files):
	"""Yields examples as (key, example) tuples."""
	examples = {}
	with open(metadata_path, encoding="utf-8") as f:
	for key, row in enumerate(f):
	data = json.loads(row)
	examples[data["file_name"]] = data
	inside_clips_dir = False
	id_ = 0
	for path, f in audio_files:
	if path.startswith(path_to_clips):
	inside_clips_dir = True
	if path in examples:
	result = examples[path]
	path = os.path.join(local_extracted_archive, path) if local_extracted_archive else path
	result["audio"] = {"path": path, "bytes": f.read()}
	result["file_name"] = path
	yield id_, result
	id_ += 1
	elif inside_clips_dir:
	break