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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.
"""Filtered Malayalam ASR corpus collected from common_voice 11, fleurs, openslr63, and ucla corpora filtered for duration between 3 - 30 secs"""
import json
import os
import datasets
_CITATION = """\
@misc{https://doi.org/10.48550/arxiv.2211.09536,
doi = {10.48550/ARXIV.2211.09536},
url = {https://arxiv.org/abs/2211.09536},
author = {Kumar, Gokul Karthik and S, Praveen and Kumar, Pratyush and Khapra, Mitesh M. and Nandakumar, Karthik},
keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), Sound (cs.SD), Audio and Speech Processing (eess.AS), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering},
title = {Towards Building Text-To-Speech Systems for the Next Billion Users},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}
@inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
pages = {4211--4215},
year = 2020
}
@misc{https://doi.org/10.48550/arxiv.2205.12446,
doi = {10.48550/ARXIV.2205.12446},
url = {https://arxiv.org/abs/2205.12446},
author = {Conneau, Alexis and Ma, Min and Khanuja, Simran and Zhang, Yu and Axelrod, Vera and Dalmia, Siddharth and Riesa, Jason and Rivera, Clara and Bapna, Ankur},
keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), Sound (cs.SD), Audio and Speech Processing (eess.AS), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering},
title = {FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
"""
_DESCRIPTION = """\
The corpus contains roughly 10 hours of audio and trasncripts in Malayalam language. The transcripts have beedn de-duplicated using exact match deduplication.
"""
_HOMEPAGE = ""
_LICENSE = "https://creativecommons.org/licenses/"
_METADATA_URLS = {
"train": "data/train.jsonl",
"test": "data/test.jsonl"
}
_URLS = {
"train": "data/train.tar.gz",
"test": "data/test.tar.gz",
}
class MalayalamASRCorpus(datasets.GeneratorBasedBuilder):
"""Malayalam ASR Corpus contains transcribed speech corpus for training ASR systems for Malayalam language."""
VERSION = datasets.Version("1.1.0")
def _info(self):
features = datasets.Features(
{
"audio": datasets.Audio(sampling_rate=16_000),
"path": datasets.Value("string"),
"sentence": datasets.Value("string"),
"length": datasets.Value("float")
}
)
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,
"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,
"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["path"]] = 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["path"] = path
yield id_, result
id_ += 1
elif inside_clips_dir:
break
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