File size: 5,789 Bytes
5926197 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 |
# 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 Kannada ASR corpus collected from fleurs, openslr79, 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 360 hours of audio and transcripts in Kannada language. The transcripts have beed de-duplicated using exact match deduplication.
"""
_HOMEPAGE = ""
_LICENSE = "https://creativecommons.org/licenses/"
_METADATA_URLS = {
"train": "data/train.jsonl",
}
_URLS = {
"train": "data/train.tar.gz",
}
class KannadaASRCorpus(datasets.GeneratorBasedBuilder):
"""Kannada ASR Corpus contains transcribed speech corpus for training ASR systems for Kannada 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"])
local_extracted_train_archive = dl_manager.extract(train_archive) if not dl_manager.is_streaming else None
train_dir = "train"
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),
},
),
]
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
|