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105 lines
3.9 KiB
105 lines
3.9 KiB
3 years ago
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import collections
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import os
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from typing import List
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from typing import Tuple
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from ..utils.download import download_and_decompress
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2 years ago
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from ..utils.env import DATA_HOME
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3 years ago
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from .dataset import AudioClassificationDataset
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__all__ = ['UrbanSound8K']
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class UrbanSound8K(AudioClassificationDataset):
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"""
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UrbanSound8K dataset contains 8732 labeled sound excerpts (<=4s) of urban
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sounds from 10 classes: air_conditioner, car_horn, children_playing, dog_bark,
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drilling, enginge_idling, gun_shot, jackhammer, siren, and street_music. The
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classes are drawn from the urban sound taxonomy.
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Reference:
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A Dataset and Taxonomy for Urban Sound Research
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https://dl.acm.org/doi/10.1145/2647868.2655045
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"""
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archieves = [
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{
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'url':
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'https://zenodo.org/record/1203745/files/UrbanSound8K.tar.gz',
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'md5': '9aa69802bbf37fb986f71ec1483a196e',
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},
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]
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label_list = [
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"air_conditioner", "car_horn", "children_playing", "dog_bark",
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"drilling", "engine_idling", "gun_shot", "jackhammer", "siren",
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"street_music"
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]
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meta = os.path.join('UrbanSound8K', 'metadata', 'UrbanSound8K.csv')
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meta_info = collections.namedtuple(
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'META_INFO', ('filename', 'fsid', 'start', 'end', 'salience', 'fold',
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'class_id', 'label'))
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audio_path = os.path.join('UrbanSound8K', 'audio')
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def __init__(self,
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mode: str='train',
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split: int=1,
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feat_type: str='raw',
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**kwargs):
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files, labels = self._get_data(mode, split)
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super(UrbanSound8K, self).__init__(
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files=files, labels=labels, feat_type=feat_type, **kwargs)
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"""
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Ags:
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mode (:obj:`str`, `optional`, defaults to `train`):
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It identifies the dataset mode (train or dev).
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split (:obj:`int`, `optional`, defaults to 1):
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It specify the fold of dev dataset.
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feat_type (:obj:`str`, `optional`, defaults to `raw`):
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It identifies the feature type that user wants to extrace of an audio file.
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"""
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def _get_meta_info(self):
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ret = []
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with open(os.path.join(DATA_HOME, self.meta), 'r') as rf:
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for line in rf.readlines()[1:]:
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ret.append(self.meta_info(*line.strip().split(',')))
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return ret
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def _get_data(self, mode: str, split: int) -> Tuple[List[str], List[int]]:
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if not os.path.isdir(os.path.join(DATA_HOME, self.audio_path)) or \
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not os.path.isfile(os.path.join(DATA_HOME, self.meta)):
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download_and_decompress(self.archieves, DATA_HOME)
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meta_info = self._get_meta_info()
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files = []
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labels = []
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for sample in meta_info:
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filename, _, _, _, _, fold, target, _ = sample
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if mode == 'train' and int(fold) != split:
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files.append(
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os.path.join(DATA_HOME, self.audio_path, f'fold{fold}',
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filename))
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labels.append(int(target))
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if mode != 'train' and int(fold) == split:
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files.append(
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os.path.join(DATA_HOME, self.audio_path, f'fold{fold}',
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filename))
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labels.append(int(target))
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return files, labels
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