Datasets:
Tasks:
Translation
File size: 5,735 Bytes
b0b69c2 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 |
# coding=utf-8
# Copyright 2021 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.
""" NLPGhana Voice Dataset"""
from __future__ import absolute_import, division, print_function
import os
import datasets
#_DATA_URL = "https://zenodo.org/record/4641533/files/ak.tar.gz?download=1"
#_DATA_URL = 'https://www.dropbox.com/s/o6k13voiy8kdhhk/ak.tar.gz?dl=1'
_DATA_URL = "ak.tar.gz"
_CITATION = """\
"""
_DESCRIPTION = """\
This work is comprised of audio data of Twi, a low resourced language spoken by the Akan people in Ghana.
This has been adapted by NLPGhana.
"""
_HOMEPAGE = "https://ghananlp.org/"
_LICENSE = ""
_LANGUAGES = {
"ak": {
"Language": "Twi",
"Date": "2023-07-08",
"Size": "753 MB",
"Version": "tw_05_2023-07-08",
},
}
class NLPGhanaVoiceConfig(datasets.BuilderConfig):
"""BuilderConfig for NLPGhana."""
def __init__(self, name, sub_version, **kwargs):
"""
Args:
data_dir: `string`, the path to the folder containing the files in the
downloaded .tar
citation: `string`, citation for the data set
url: `string`, url for information about the data set
**kwargs: keyword arguments forwarded to super.
"""
self.sub_version = sub_version
self.language = kwargs.pop("language", None)
self.date_of_snapshot = kwargs.pop("date", None)
self.size = kwargs.pop("size", None)
description = f"NLPGhana speech to text dataset in {self.language} version {self.sub_version} of {self.date_of_snapshot}. The dataset has a size of {self.size}"
super(NLPGhanaVoiceConfig, self).__init__(
name=name, version=datasets.Version("1.0.5", ""), description=description, **kwargs
)
class NLPGhanaVoice(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
NLPGhanaVoiceConfig(
name=lang_id,
language=_LANGUAGES[lang_id]["Language"],
sub_version=_LANGUAGES[lang_id]["Version"],
date=_LANGUAGES[lang_id]["Date"],
size=_LANGUAGES[lang_id]["Size"],
)
for lang_id in _LANGUAGES.keys()
]
def _info(self):
features = datasets.Features(
{
"user_id": datasets.Value("string"),
"path": datasets.Value("string"),
"text": datasets.Value("string"),
"durationMsec": datasets.Value("int64"),
"sampleRate": datasets.Value("int64"),
"speaker_gender": datasets.Value("string"),
"mother_tongue": datasets.Value("string"),
"date": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=None,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
dl_path = dl_manager.download_and_extract(_DATA_URL)
abs_path_to_data = os.path.join(dl_path, self.config.name)
abs_path_to_clips = os.path.join(abs_path_to_data, "clips")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(abs_path_to_data, "train.tsv"),
"path_to_clips": abs_path_to_clips,
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": os.path.join(abs_path_to_data, "test.tsv"),
"path_to_clips": abs_path_to_clips,
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": os.path.join(abs_path_to_data, "validation.tsv"),
"path_to_clips": abs_path_to_clips,
},
),
]
def _generate_examples(self, filepath, path_to_clips):
""" Yields examples. """
data_fields = list(self._info().features.keys())
path_idx = data_fields.index("path")
with open(filepath, encoding="utf-8") as f:
lines = f.readlines()
headline = lines[0]
column_names = headline.strip().split("\t")
assert (
column_names == data_fields
), f"The file should have {data_fields} as column names, but has {column_names}"
for id_, line in enumerate(lines[1:]):
field_values = line.strip().split("\t")
# set absolute path for mp3 audio file
field_values[path_idx] = os.path.join(path_to_clips, field_values[path_idx])
# if data is incomplete, fill with empty values
if len(field_values) < len(data_fields):
field_values += (len(data_fields) - len(field_values)) * ["''"]
yield id_, {key: value for key, value in zip(data_fields, field_values)} |