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89 lines
2.8 KiB
89 lines
2.8 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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from typing import List
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import numpy as np
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import paddle
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from ..backends import load as load_audio
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from ..features import melspectrogram
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from ..features import mfcc
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feat_funcs = {
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'raw': None,
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'melspectrogram': melspectrogram,
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'mfcc': mfcc,
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}
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class AudioClassificationDataset(paddle.io.Dataset):
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"""
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Base class of audio classification dataset.
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"""
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def __init__(self,
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files: List[str],
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labels: List[int],
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feat_type: str='raw',
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sample_rate: int=None,
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**kwargs):
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"""
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Ags:
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files (:obj:`List[str]`): A list of absolute path of audio files.
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labels (:obj:`List[int]`): Labels of audio files.
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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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super(AudioClassificationDataset, self).__init__()
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if feat_type not in feat_funcs.keys():
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raise RuntimeError(
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f"Unknown feat_type: {feat_type}, it must be one in {list(feat_funcs.keys())}"
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)
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self.files = files
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self.labels = labels
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self.feat_type = feat_type
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self.sample_rate = sample_rate
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self.feat_config = kwargs # Pass keyword arguments to customize feature config
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def _get_data(self, input_file: str):
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raise NotImplementedError
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def _convert_to_record(self, idx):
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file, label = self.files[idx], self.labels[idx]
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if self.sample_rate is None:
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waveform, sample_rate = load_audio(file)
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else:
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waveform, sample_rate = load_audio(file, sr=self.sample_rate)
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feat_func = feat_funcs[self.feat_type]
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record = {}
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record['feat'] = feat_func(
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waveform, sample_rate,
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**self.feat_config) if feat_func else waveform
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record['label'] = label
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return record
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def __getitem__(self, idx):
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record = self._convert_to_record(idx)
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return np.array(record['feat']).transpose(), np.array(
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record['label'], dtype=np.int64)
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def __len__(self):
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return len(self.files)
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