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# coding=utf-8
# Copyright 2024 The HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
from pathlib import Path
from random import shuffle
import datasets
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A bark detection dataset with positive and negative samples of 1 second},
author={Rodrigo Marcos García},
year={2024}
}
"""
_DESCRIPTION = """\
This is a bark detection dataset with positive and negative samples of 1 second
"""
_HOMEPAGE = "https://huggingface.co/datasets/rmarcosg/bark-detection"
_LICENSE = "Apache 2.0"
class BarkDetection(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("0.0.1")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"file": datasets.Value("string"),
"audio": datasets.Audio(sampling_rate=44_100),
"label": datasets.Value("string"),
}
),
supervised_keys=("file", "label"),
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": "train",
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": "validation",
"split": "validation",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": "test",
"split": "test"
},
),
]
def _generate_examples(self, archive_path, split):
"""Yields examples."""
key = 0
audio_files_dir = Path(archive_path) / split
for audio_file_path in shuffle(audio_files_dir.glob("*/*.wav")):
filename = audio_file_path.stem
label = audio_file_path.parent.stem
yield key, {
"file": filename,
"audio": str(audio_file_path),
"label": label,
}
key += 1
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