Datasets:
Tasks:
Automatic Speech Recognition
Modalities:
Audio
Formats:
soundfolder
Languages:
Nepali
Size:
< 1K
File size: 2,027 Bytes
5ad0b6f |
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 |
import os
import csv
import datasets
from datasets import Audio
_DESCRIPTION = "Cleaned Nepali ASR dataset with audio and transcriptions."
_CITATION = ""
_HOMEPAGE = ""
class NepaliASRConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(NepaliASRConfig, self).__init__(**kwargs)
class NepaliASRDataset(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
NepaliASRConfig(name="default", version=datasets.Version("1.0.0"), description="Nepali ASR Dataset")
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features({
"utterance_id": datasets.Value("string"),
"speaker_id": datasets.Value("string"),
"transcription": datasets.Value("string"),
"audio": Audio(sampling_rate=16_000),
}),
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
archive_path = dl_manager.download_and_extract(self.config.data_dir)
return [
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"tsv_path": os.path.join(archive_path, "validation_transcriptions.tsv"),
"data_dir": archive_path,
},
)
]
def _generate_examples(self, tsv_path, data_dir):
with open(tsv_path, encoding="utf-8") as f:
reader = csv.DictReader(f, delimiter="\t")
for idx, row in enumerate(reader):
audio_path = os.path.join(data_dir, row["utterance_path"])
yield idx, {
"utterance_id": row["utterance_id"],
"speaker_id": row["speaker_id"],
"transcription": row["transcription"],
"audio": audio_path,
}
|