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116 lines
4.4 KiB
116 lines
4.4 KiB
# 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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import random
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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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from ..utils.env import DATA_HOME
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from .dataset import AudioClassificationDataset
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__all__ = ['GTZAN']
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class GTZAN(AudioClassificationDataset):
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"""
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The GTZAN dataset consists of 1000 audio tracks each 30 seconds long. It contains 10 genres,
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each represented by 100 tracks. The dataset is the most-used public dataset for evaluation
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in machine listening research for music genre recognition (MGR).
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Reference:
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Musical genre classification of audio signals
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https://ieeexplore.ieee.org/document/1021072/
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"""
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archieves = [
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{
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'url': 'http://opihi.cs.uvic.ca/sound/genres.tar.gz',
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'md5': '5b3d6dddb579ab49814ab86dba69e7c7',
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},
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]
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label_list = [
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'blues', 'classical', 'country', 'disco', 'hiphop', 'jazz', 'metal',
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'pop', 'reggae', 'rock'
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]
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meta = os.path.join('genres', 'input.mf')
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meta_info = collections.namedtuple('META_INFO', ('file_path', 'label'))
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audio_path = 'genres'
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def __init__(self,
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mode='train',
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seed=0,
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n_folds=5,
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split=1,
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feat_type='raw',
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**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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seed (:obj:`int`, `optional`, defaults to 0):
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Set the random seed to shuffle samples.
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n_folds (:obj:`int`, `optional`, defaults to 5):
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Split the dataset into n folds. 1 fold for dev dataset and n-1 for train dataset.
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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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assert split <= n_folds, f'The selected split should not be larger than n_fold, but got {split} > {n_folds}'
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files, labels = self._get_data(mode, seed, n_folds, split)
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super(GTZAN, self).__init__(
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files=files, labels=labels, feat_type=feat_type, **kwargs)
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def _get_meta_info(self) -> List[collections.namedtuple]:
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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():
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ret.append(self.meta_info(*line.strip().split('\t')))
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return ret
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def _get_data(self, mode, seed, n_folds,
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split) -> 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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random.seed(seed) # shuffle samples to split data
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random.shuffle(
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meta_info
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) # make sure using the same seed to create train and dev dataset
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files = []
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labels = []
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n_samples_per_fold = len(meta_info) // n_folds
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for idx, sample in enumerate(meta_info):
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file_path, label = sample
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filename = os.path.basename(file_path)
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target = self.label_list.index(label)
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fold = idx // n_samples_per_fold + 1
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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, label, filename))
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labels.append(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, label, filename))
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labels.append(target)
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return files, labels
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