bark_detection.py

# 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