Datasets:
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# 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
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