Datasets:
Tasks:
Automatic Speech Recognition
Multilinguality:
multilingual
Size Categories:
1K<n<10K
Language Creators:
crowdsourced
Annotations Creators:
crowdsourced
Source Datasets:
librivox
License:
File size: 6,264 Bytes
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# coding=utf-8
# Copyright 2022 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.
""" LibriVox-Indonesia Dataset"""
import csv
import os
import datasets
from datasets.utils.py_utils import size_str
from .languages import LANGUAGES
from .release_stats import STATS
_CITATION = """\
"""
_HOMEPAGE = "https://huggingface.co/indonesian-nlp/librivox-indonesia"
_LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/"
_DATA_URL = "https://huggingface.co/datasets/indonesian-nlp/librivox-indonesia/resolve/main/data"
class LibriVoxIndonesiaConfig(datasets.BuilderConfig):
"""BuilderConfig for LibriVoxIndonesia."""
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"LibriVox-Indonesia 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(LibriVoxIndonesiaConfig, self).__init__(
name=name,
version=datasets.Version(version),
description=description,
**kwargs,
)
class LibriVoxIndonesia(datasets.GeneratorBasedBuilder):
DEFAULT_CONFIG_NAME = "all"
BUILDER_CONFIGS = [
LibriVoxIndonesiaConfig(
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"),
"reader": datasets.Value("string"),
"sentence": datasets.Value("string"),
"audio": datasets.features.Audio(sampling_rate=44100)
}
)
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, "test": datasets.Split.TEST}
for split in split_type:
audio_path[split] = dl_manager.download(f"{_DATA_URL}/audio_{split}.tgz")
local_extracted_archive[split] = dl_manager.extract(audio_path[split]) if not dl_manager.is_streaming else None
metadata_path[split] = dl_manager.download_and_extract(f"{_DATA_URL}/metadata_{split}.csv.gz")
path_to_clips = "librivox-indonesia"
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": dl_manager.download_and_extract(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)
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, f in audio_files:
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
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