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import csv
import os
import datasets
logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = "Custom dataset for extracting audio files and matching sentences."
_DATA_URL = "https://huggingface.co/datasets/ugshanyu/dataset/resolve/main" # Replace with the URL of your data
class CustomDataset(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
def _info(self):
features = datasets.Features(
{
"audio": datasets.Audio(sampling_rate=48_000),
"sentence": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=("audio", "sentence"),
homepage=None,
citation=None,
)
def _split_generators(self, dl_manager):
audio_path = dl_manager.download_and_extract(_DATA_URL+"/audo.zip")
csv_path = dl_manager.download_and_extract(_DATA_URL+"/comma.csv")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"audio_path": audio_path, "csv_path": csv_path},
)
]
def _generate_examples(self, audio_path, csv_path):
print(audio_path)
print(csv_path)
key = 0
print(os.listdir(audio_path))
with open(csv_path, encoding="utf-8") as csv_file:
csv_reader = csv.DictReader(csv_file)
for row in csv_reader:
original_sentence_id, sentence, locale = row.values()
audio_file = f"{original_sentence_id}.mp3"
audio_file_path = os.path.join(audio_path, audio_file)
yield key, {
"audio": audio_file_path,
"sentence": sentence,
}
key += 1
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