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PaddleSpeech/paddlespeech/vector/io/batch.py

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1.6 KiB

# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import paddle
def waveform_collate_fn(batch):
waveforms = np.stack([item['feat'] for item in batch])
labels = np.stack([item['label'] for item in batch])
return {'waveforms': waveforms, 'labels': labels}
def feature_normalize(feats: paddle.Tensor,
mean_norm: bool=True,
std_norm: bool=True,
convert_to_numpy: bool=False):
# Features normalization if needed
# numpy.mean is a little with paddle.mean about 1e-6
if convert_to_numpy:
feats_np = feats.numpy()
mean = feats_np.mean(axis=-1, keepdims=True) if mean_norm else 0
std = feats_np.std(axis=-1, keepdims=True) if std_norm else 1
feats_np = (feats_np - mean) / std
feats = paddle.to_tensor(feats_np, dtype=feats.dtype)
else:
mean = feats.mean(axis=-1, keepdim=True) if mean_norm else 0
std = feats.std(axis=-1, keepdim=True) if std_norm else 1
feats = (feats - mean) / std
return feats