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43 lines
1.6 KiB
43 lines
1.6 KiB
# 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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import numpy as np
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import paddle
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def waveform_collate_fn(batch):
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waveforms = np.stack([item['feat'] for item in batch])
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labels = np.stack([item['label'] for item in batch])
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return {'waveforms': waveforms, 'labels': labels}
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def feature_normalize(feats: paddle.Tensor,
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mean_norm: bool=True,
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std_norm: bool=True,
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convert_to_numpy: bool=False):
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# Features normalization if needed
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# numpy.mean is a little with paddle.mean about 1e-6
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if convert_to_numpy:
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feats_np = feats.numpy()
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mean = feats_np.mean(axis=-1, keepdims=True) if mean_norm else 0
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std = feats_np.std(axis=-1, keepdims=True) if std_norm else 1
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feats_np = (feats_np - mean) / std
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feats = paddle.to_tensor(feats_np, dtype=feats.dtype)
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else:
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mean = feats.mean(axis=-1, keepdim=True) if mean_norm else 0
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std = feats.std(axis=-1, keepdim=True) if std_norm else 1
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feats = (feats - mean) / std
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return feats
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