wehub-resource-sync
99 lines
3.1 KiB
Python
99 lines
3.1 KiB
Python
# Copyright (c) 2022 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 __future__ import annotations
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import paddle
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from ..features import MFCC, LogMelSpectrogram, MelSpectrogram, Spectrogram
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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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'logmelspectrogram': LogMelSpectrogram,
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'spectrogram': Spectrogram,
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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__(
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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 = None,
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**kwargs,
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):
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"""
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Args:
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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 extract an audio file.
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"""
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super().__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 = (
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kwargs # Pass keyword arguments to customize feature config
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)
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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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waveform, sample_rate = paddle.audio.load(file)
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self.sample_rate = sample_rate
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feat_func = feat_funcs[self.feat_type]
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record = {}
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if len(waveform.shape) == 2:
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waveform = waveform.squeeze(0) # 1D input
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waveform = paddle.to_tensor(waveform, dtype=paddle.float32)
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if feat_func is not None:
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waveform = waveform.unsqueeze(0) # (batch_size, T)
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if self.feat_type != 'spectrogram':
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feature_extractor = feat_func(
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sr=self.sample_rate, **self.feat_config
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)
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else:
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feature_extractor = feat_func(**self.feat_config)
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record['feat'] = feature_extractor(waveform).squeeze(0)
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else:
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record['feat'] = 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 record['feat'], record['label']
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def __len__(self):
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return len(self.files)
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