Datasets:
Size:
10K - 100K
License:
File size: 2,566 Bytes
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import os
from glob import glob
from pathlib import Path
import datasets
from datasets import Features, Value, Audio
os.system('pip install music-tag')
from music_tag import load_file
_DESCRIPTION = """
This dataset contains speech generated for anecdotes from the [baneks dataset](https://huggingface.co/datasets/zeio/baneks)
"""
_HOMEPAGE = "https://huggingface.co/datasets/zeio/baneks-speech"
_LICENSE = "Apache License Version 2.0"
_URL = "https://huggingface.co/datasets/zeio/baneks-speech/resolve/main/speech/{first:06d}-{last:06d}.tar.xz"
_N_TOTAL = 46_554
_N_BATCH = 1_000
class BaneksSpeech(datasets.GeneratorBasedBuilder):
"""Speech generated for anecdotes from the baneks dataset"""
VERSION = datasets.Version("10.10.2023")
def _info(self):
return datasets.DatasetInfo(
description = _DESCRIPTION,
features = Features(
{
"text": Value("string"),
"audio": Audio(sampling_rate = 22_050),
"artist": Value("string"),
"id": Value("int32"),
"source": Value("string")
}
),
homepage = _HOMEPAGE,
license = _LICENSE
)
def _split_generators(self, dl_manager):
offset = 0
paths = []
while offset < _N_TOTAL:
url = _URL.format(first = offset + 1, last = min(offset := offset + _N_BATCH, _N_TOTAL))
paths.append(dl_manager.download_and_extract(url))
break
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"paths": paths
}
)
]
def _generate_examples(self, paths: list[str]):
for path in paths:
for file in sorted(glob(os.path.join(path, "*"))):
components = Path(file).stem.split('.', maxsplit = 1)
assert len(components) == 2, f'Incorrect file name: {file}'
id_ = int(components[0])
source = components[1]
meta = load_file(file)
# artist = meta['artist']
# text = meta['lyrics']
# print(id_, source, artist, text)
yield f'{id_:08d}-{source}', {
'text': meta['lyrics'],
'audio': file,
'artist': meta['artist'],
'id': id_,
'source': source
}
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