File size: 2,506 Bytes
eec5401 00193e9 7e4da78 eec5401 7e4da78 ac80f9d eec5401 7e4da78 eec5401 7e4da78 20a8dfe cc75534 20a8dfe eec5401 00193e9 1b082c9 d4a7562 00193e9 eec5401 00193e9 3b01e6c 00193e9 1b082c9 d4a7562 00193e9 eec5401 d4a7562 7e4da78 00193e9 7e4da78 00193e9 7e4da78 d4a7562 00193e9 7e4da78 00193e9 0127588 00193e9 |
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 |
import datasets
import os
import random
import json
class MyDataset(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.DatasetInfo(
description="My dataset with text and audio.",
features=datasets.Features({
"sentence": datasets.Value("string"),
"audio": datasets.Audio(sampling_rate=16_000), # Defines the audio feature
}),
homepage="https://huggingface.co/datasets/my-dataset",
license="MIT",
)
def _split_generators(self, dl_manager):
# Download and extract the dataset
# data_dir = dl_manager.download_and_extract("https://github.com/atulksingh011/test-voice-data/raw/main/audios.tar.gz")
# metadata = dl_manager.download("https://github.com/atulksingh011/test-voice-data/raw/main/metadata.jsonl")
data_dir = dl_manager.download_and_extract("https://raw.githubusercontent.com/atulksingh011/test-voice-data/refs/heads/record-names/audio.tar.gz")
metadata = dl_manager.download("https://github.com/atulksingh011/test-voice-data/raw/record-names/metadata.jsonl")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": metadata,
"audio_dir": data_dir,
"split": "train"
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": metadata,
"audio_dir": data_dir,
"split": "eval"
},
)
]
def _generate_examples(self, filepath, audio_dir, split):
# Read and parse the metadata JSONL file
with open(filepath, "r", encoding="utf-8") as f:
data = [json.loads(line) for line in f]
# Shuffle the data for randomness
random.shuffle(data)
# Calculate split index for training and evaluation
split_index = int(len(data) * 0.8)
if split == "train":
examples = data[:split_index]
else:
examples = data[split_index:]
# Yield the examples
for idx, record in enumerate(examples):
yield idx, {
"sentence": record["sentence"],
"audio": os.path.join(audio_dir, record["file_name"]), # Correct path to the audio file
}
|