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200 lines
7.5 KiB
200 lines
7.5 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
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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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"""Wrap the waveform into a batch form
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Args:
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batch (list): the waveform list from the dataloader
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the item of data include several field
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feat: the utterance waveform data
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label: the utterance label encoding data
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Returns:
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dict: the batch data to dataloader
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"""
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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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"""Do one utterance feature normalization
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Args:
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feats (paddle.Tensor): the original utterance feat, such as fbank, mfcc
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mean_norm (bool, optional): mean norm flag. Defaults to True.
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std_norm (bool, optional): std norm flag. Defaults to True.
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convert_to_numpy (bool, optional): convert the paddle.tensor to numpy
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and do feature norm with numpy. Defaults to False.
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Returns:
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paddle.Tensor : the normalized feats
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"""
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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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"""Do batch utterance features normalization
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Args:
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batch (list): the batch feature from dataloader
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mean_norm (bool, optional): mean normalization flag. Defaults to True.
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std_norm (bool, optional): std normalization flag. Defaults to True.
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Returns:
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dict: the normalized batch features
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"""
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ids = [item['utt_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}
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def pad_right_to(array, target_shape, mode="constant", value=0):
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"""
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This function takes a numpy array of arbitrary shape and pads it to target
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shape by appending values on the right.
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Args:
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array: input numpy array. Input array whose dimension we need to pad.
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target_shape : (list, tuple). Target shape we want for the target array its len must be equal to array.ndim
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mode : str. Pad mode, please refer to numpy.pad documentation.
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value : float. Pad value, please refer to numpy.pad documentation.
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Returns:
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array: numpy.array. Padded array.
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valid_vals : list. List containing proportion for each dimension of original, non-padded values.
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"""
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assert len(target_shape) == array.ndim
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pads = [] # this contains the abs length of the padding for each dimension.
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valid_vals = [] # this contains the relative lengths for each dimension.
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i = 0 # iterating over target_shape ndims
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while i < len(target_shape):
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assert (target_shape[i] >= array.shape[i]
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), "Target shape must be >= original shape for every dim"
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pads.append([0, target_shape[i] - array.shape[i]])
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valid_vals.append(array.shape[i] / target_shape[i])
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i += 1
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array = numpy.pad(array, pads, mode=mode, constant_values=value)
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return array, valid_vals
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def batch_pad_right(arrays, mode="constant", value=0):
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"""Given a list of numpy arrays it batches them together by padding to the right
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on each dimension in order to get same length for all.
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Args:
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arrays : list. List of array we wish to pad together.
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mode : str. Padding mode see numpy.pad documentation.
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value : float. Padding value see numpy.pad documentation.
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Returns:
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array : numpy.array. Padded array.
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valid_vals : list. List containing proportion for each dimension of original, non-padded values.
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"""
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if not len(arrays):
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raise IndexError("arrays list must not be empty")
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if len(arrays) == 1:
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# if there is only one array in the batch we simply unsqueeze it.
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return numpy.expand_dims(arrays[0], axis=0), numpy.array([1.0])
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if not (any(
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[arrays[i].ndim == arrays[0].ndim for i in range(1, len(arrays))])):
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raise IndexError("All arrays must have same number of dimensions")
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# FIXME we limit the support here: we allow padding of only the last dimension
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# need to remove this when feat extraction is updated to handle multichannel.
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max_shape = []
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for dim in range(arrays[0].ndim):
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if dim != (arrays[0].ndim - 1):
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if not all(
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[x.shape[dim] == arrays[0].shape[dim] for x in arrays[1:]]):
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raise EnvironmentError(
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"arrays should have same dimensions except for last one")
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max_shape.append(max([x.shape[dim] for x in arrays]))
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batched = []
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valid = []
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for t in arrays:
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# for each array we apply pad_right_to
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padded, valid_percent = pad_right_to(
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t, max_shape, mode=mode, value=value)
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batched.append(padded)
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valid.append(valid_percent[-1])
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batched = numpy.stack(batched)
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return batched, numpy.array(valid)
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