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83 lines
3.2 KiB
83 lines
3.2 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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def pad_right_2d(x, target_length, axis=-1, mode='constant', **kwargs):
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x = np.asarray(x)
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assert len(
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x.shape) == 2, f'Only 2D arrays supported, but got shape: {x.shape}'
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w = target_length - x.shape[axis]
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assert w >= 0, f'Target length {target_length} is less than origin length {x.shape[axis]}'
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if axis == 0:
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pad_width = [[0, w], [0, 0]]
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else:
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pad_width = [[0, 0], [0, w]]
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return np.pad(x, pad_width, mode=mode, **kwargs)
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def batch_feature_normalize(batch, mean_norm: bool=True, std_norm: bool=True):
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ids = [item['id'] for item in batch]
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lengths = np.asarray([item['feat'].shape[1] for item in batch])
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feats = list(
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map(lambda x: pad_right_2d(x, lengths.max()),
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[item['feat'] for item in batch]))
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feats = np.stack(feats)
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# Features normalization if needed
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for i in range(len(feats)):
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feat = feats[i][:, :lengths[i]] # Excluding pad values.
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mean = feat.mean(axis=-1, keepdims=True) if mean_norm else 0
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std = feat.std(axis=-1, keepdims=True) if std_norm else 1
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feats[i][:, :lengths[i]] = (feat - mean) / std
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assert feats[i][:, lengths[
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i]:].sum() == 0 # Padding valus should all be 0.
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# Converts into ratios.
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# the utterance of the max length doesn't need to padding
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# the remaining utterances need to padding and all of them will be padded to max length
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# we convert the original length of each utterance to the ratio of the max length
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lengths = (lengths / lengths.max()).astype(np.float32)
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return {'ids': ids, 'feats': feats, 'lengths': lengths} |