File size: 6,758 Bytes
2bdfded 3d86fd5 2bdfded 5728ae1 2bdfded 45c5167 2bdfded 8761f7d 3d86fd5 8761f7d 3d86fd5 27369e5 2bdfded 6dce44c 2bdfded |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 |
""" Kathbath Dataset"""
import csv
import os
import tarfile
import datasets
from datasets.utils.py_utils import size_str
from .languages import LANGUAGES
from .release_stats import STATS
_CITATION = """\
@misc{https://doi.org/10.48550/arxiv.2208.11761,
doi = {10.48550/ARXIV.2208.11761},
url = {https://arxiv.org/abs/2208.11761},
author = {Javed, Tahir and Bhogale, Kaushal Santosh and Raman, Abhigyan and Kunchukuttan, Anoop and Kumar, Pratyush and Khapra, Mitesh M.},
title = {IndicSUPERB: A Speech Processing Universal Performance Benchmark for Indian languages},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}
"""
_HOMEPAGE = "https://ai4bharat.iitm.ac.in/indic-superb/"
_LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/"
_DATA_URL = "https://huggingface.co/datasets/ai4bharat/kathbath/resolve/main/data"
class KathbathConfig(datasets.BuilderConfig):
"""BuilderConfig for Kathbath."""
def __init__(self, name, version, **kwargs):
self.language = kwargs.pop("language", None)
self.release_date = kwargs.pop("release_date", None)
self.num_clips = kwargs.pop("num_clips", None)
self.num_speakers = kwargs.pop("num_speakers", None)
self.total_hr = kwargs.pop("total_hr", None)
self.size_bytes = kwargs.pop("size_bytes", None)
self.size_human = size_str(self.size_bytes)
description = (
f"Kathbath speech to text dataset in {self.language} released on {self.release_date}. "
f"The dataset comprises {self.total_hr} hours of transcribed speech data"
)
super(KathbathConfig, self).__init__(
name=name,
version=datasets.Version(version),
description=description,
**kwargs,
)
class Kathbath(datasets.GeneratorBasedBuilder):
DEFAULT_CONFIG_NAME = "_all_"
BUILDER_CONFIGS = [
KathbathConfig(
name=lang,
version=STATS["version"],
language=LANGUAGES[lang],
release_date=STATS["date"],
# num_clips=lang_stats["clips"],
# num_speakers=lang_stats["users"],
total_hr=float(lang_stats["totalHrs"]) if lang_stats["totalHrs"] else None,
# size_bytes=int(lang_stats["size"]) if lang_stats["size"] else None,
)
for lang, lang_stats in STATS["locales"].items()
]
def _info(self):
total_languages = len(STATS["locales"])
total_hours = self.config.total_hr
description = (
"LibriVox-Indonesia is a speech dataset generated from LibriVox with only languages from Indonesia."
f"The dataset currently consists of {total_hours} hours of speech "
f"in {total_languages} languages, but more voices and languages are always added."
)
features = datasets.Features(
{
"path": datasets.Value("string"),
"language": datasets.Value("string"),
"speaker": datasets.Value("string"),
"sentence": datasets.Value("string"),
"audio": datasets.features.Audio(sampling_rate=16000)
}
)
return datasets.DatasetInfo(
description=description,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
version=self.config.version,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
dl_manager.download_config.ignore_url_params = True
audio_path = {}
local_extracted_archive = {}
metadata_path = {}
split_type = {"train": datasets.Split.TRAIN, "valid": datasets.Split.VALIDATION, "test_unknown": datasets.Split.TEST, "test_known": datasets.Split.TEST}
for split in split_type:
if split == 'train':
audio_paths = [
f"{_DATA_URL}/audio_{split}.tar.partaa",
f"{_DATA_URL}/audio_{split}.tar.partab",
f"{_DATA_URL}/audio_{split}.tar.partac",
]
audio_path[split] = dl_manager.download(audio_paths)
for path in audio_path[split]:
try:
local_extracted_archive[split] = dl_manager.extract(audio_path[split]) if not dl_manager.is_streaming else None
except tarfile.ReadError:
pass
else:
audio_paths = [f"{_DATA_URL}/audio_{split}.tar"]
audio_path[split] = dl_manager.download(audio_paths)
local_extracted_archive[split] = dl_manager.extract(audio_path[split]) if not dl_manager.is_streaming else None
metadata_path[split] = dl_manager.download(f"{_DATA_URL}/metata_{split}.tsv")
path_to_clips = "kb_data_clean_m4a"
return [
datasets.SplitGenerator(
name=split_type[split],
gen_kwargs={
"local_extracted_archive": local_extracted_archive[split],
"audio_files": dl_manager.iter_archive(audio_path[split]),
"metadata_path": metadata_path[split],
"path_to_clips": path_to_clips,
},
) for split in split_type
]
def _generate_examples(
self,
local_extracted_archive,
audio_files,
metadata_path,
path_to_clips,
):
"""Yields examples."""
data_fields = list(self._info().features.keys())
metadata = {}
with open(metadata_path, "r", encoding="utf-8") as f:
reader = csv.DictReader(f, delimiter="\t")
for row in reader:
if self.config.name == "_all_" or self.config.name == row["language"]:
row["path"] = os.path.join(path_to_clips, row["path"])
# if data is incomplete, fill with empty values
for field in data_fields:
if field not in row:
row[field] = ""
metadata[row["path"]] = row
id_ = 0
for path in audio_files:
print(path)
if path in metadata:
result = dict(metadata[path])
# set the audio feature and the path to the extracted file
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
|