Datasets:
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import os
import csv
import datasets
class NepaliASRConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super().__init__(**kwargs)
class NepaliASR(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
NepaliASRConfig(version=datasets.Version("1.0.0"), description="validation_nepali_asr"),
]
def _info(self):
return datasets.DatasetInfo(
features=datasets.Features(
{
"utterance_id": datasets.Value("string"),
"speaker_id": datasets.Value("string"),
"audio": datasets.Audio(sampling_rate=16000),
"transcription": datasets.Value("string"),
"num_frames": datasets.Value("int32"),
}
),
supervised_keys=None,
homepage="",
license="",
citation="",
)
def _split_generators(self, dl_manager):
return [
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"transcriptions_path": os.path.join("validation_dataset", "validation_transcriptions.tsv"),
"data_dir": "validation_dataset",
},
),
]
def _generate_examples(self, transcriptions_path, data_dir):
with open(transcriptions_path, encoding="utf-8") as f:
reader = csv.DictReader(f, delimiter="\t")
for idx, row in enumerate(reader):
# Join data_dir with utterance_path for the audio file
audio_path = os.path.join(data_dir, row["utterance_path"])
yield idx, {
"utterance_id": row["utterance_id"],
"speaker_id": row["speaker_id"],
"audio": audio_path,
"transcription": row["transcription"],
"num_frames": int(row["num_frames"]),
}
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