File size: 5,893 Bytes
b9d97ff d65446a b9d97ff d65446a b9d97ff 3932de4 b9d97ff 3a00820 b9d97ff 21d92d1 b9d97ff 5637f96 b9d97ff 5fc16f6 b9d97ff 01d1771 b9d97ff 766aa8b b9d97ff |
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 |
# 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.
"""sil-ai/audio-kw-in-context is a subset of MLCommons/ml_spoken_words focusing on keywords found in the Bible'"""
import json
import os
import datasets
_CITATION = """\
@InProceedings{huggingface:audio-kw-in-context,
title = {audio-kw-in-context},
author={Joshua Nemecek
},
year={2022}
}
"""
_DESCRIPTION = 'sil-ai/audio-kw-in-context is a subset of MLCommons/ml_spoken_words focusing on keywords found in the Bible'
_LANGUAGES = ['eng', 'ind', 'spa']
_LANG_ISO_DICT = {'en':'eng','es':'spa','id':'ind'}
_HOMEPAGE = 'https://ai.sil.org'
_URLS = {"metadata": "bible-keyword-context.json",
"files": {lang: f'https://audio-keyword-spotting.s3.amazonaws.com/HF-context-v2/{lang}-kw-archive.tar.gz' for lang in _LANGUAGES},
}
_LICENSE = 'CC-BY 4.0'
_GENDERS = ["MALE", "FEMALE", "OTHER", "NAN"]
class AudioKwInContextConfig(datasets.BuilderConfig):
"""BuilderConfig for Audio-Kw-In-Context"""
def __init__(self, language='', **kwargs):
super(AudioKwInContextConfig, self).__init__(**kwargs)
self.language = _LANG_ISO_DICT.get(language, language)
class AudioKwInContext(datasets.GeneratorBasedBuilder):
"""Audio-Kw-In-Context class"""
BUILDER_CONFIGS = [AudioKwInContextConfig(name=x, description=f'Audio keyword spotting for language code {x}', language=x) for x in _LANGUAGES]
DEFAULT_CONFIG_NAME = ''
BUILDER_CONFIG_CLASS = AudioKwInContextConfig
VERSION = datasets.Version("0.0.2")
def _info(self):
features = datasets.Features(
{
"file": datasets.Value("string"),
"language": datasets.Value("string"),
"speaker_id": datasets.Value("string"),
"sentence": datasets.Value("string"),
"keywords": datasets.Sequence(datasets.Value("string")),
"audio": datasets.Audio(sampling_rate=16_000),
"start_times": datasets.Sequence(datasets.Value("float32")),
"end_times": datasets.Sequence(datasets.Value("float32")),
"confidence": datasets.Sequence(datasets.Value("float32")),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
if self.config.language == '':
raise ValueError('Please specify a language.')
elif self.config.language not in _LANGUAGES:
raise ValueError(f'{self.config.language} does not appear in the list of languages: {_LANGUAGES}')
data_dir = dl_manager.download(_URLS['metadata'])
with open(data_dir, 'r') as f:
filemeta = json.load(f)
audio_dir = dl_manager.download_and_extract(_URLS['files'][self.config.name])
langmeta = filemeta[self.config.language]
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"audio_dir": audio_dir,
"data": langmeta,
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"audio_dir": audio_dir,
"data": langmeta,
"split": "dev",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"audio_dir": audio_dir,
"data": langmeta,
"split": "test",
},
),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, audio_dir, data, split):
for key, row in enumerate(data[split]):
try:
tfile = os.path.join(audio_dir, row['file'])
if not tfile.endswith('.mp3'):
os.rename(tfile, tfile + '.mp3')
tfile += '.mp3'
yield key, {
"file": tfile,
"sentence": row.get('sentence'),
"language": self.config.language,
"speaker_id": row.get('speaker_id',row.get('client_id')),
"keywords": row['keywords'],
"audio": tfile,
"start_times": row.get('start_times'),
"end_times": row.get('end_times'),
"confidence": row.get('confidence'),
}
except Exception as e:
print(e)
print(f'In split {split}: {row["file"]} failed to load. Data may be missing.')
pass |