# 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
import numpy as np
import paddle
def waveform_collate_fn(batch):
"""Wrap the waveform into a batch form
Args:
batch (list): the waveform list from the dataloader
the item of data include several field
feat: the utterance waveform data
label: the utterance label encoding data
Returns:
dict: the batch data to dataloader
"""
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):
"""Do one utterance feature normalization
Args:
feats (paddle.Tensor): the original utterance feat, such as fbank, mfcc
mean_norm (bool, optional): mean norm flag. Defaults to True.
std_norm (bool, optional): std norm flag. Defaults to True.
convert_to_numpy (bool, optional): convert the paddle.tensor to numpy
and do feature norm with numpy. Defaults to False.
Returns:
paddle.Tensor : the normalized feats
"""
# 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
def pad_right_2d(x, target_length, axis=-1, mode='constant', **kwargs):
x = np.asarray(x)
assert len(
x.shape) == 2, f'Only 2D arrays supported, but got shape: {x.shape}'
w = target_length - x.shape[axis]
assert w >= 0, f'Target length {target_length} is less than origin length {x.shape[axis]}'
if axis == 0:
pad_width = [[0, w], [0, 0]]
else:
pad_width = [[0, 0], [0, w]]
return np.pad(x, pad_width, mode=mode, **kwargs)
def batch_feature_normalize(batch, mean_norm: bool=True, std_norm: bool=True):
"""Do batch utterance features normalization
Args:
batch (list): the batch feature from dataloader
mean_norm (bool, optional): mean normalization flag. Defaults to True.
std_norm (bool, optional): std normalization flag. Defaults to True.
Returns:
dict: the normalized batch features
"""
ids = [item['utt_id'] for item in batch]
lengths = np.asarray([item['feat'].shape[1] for item in batch])
feats = list(
map(lambda x: pad_right_2d(x, lengths.max()),
[item['feat'] for item in batch]))
feats = np.stack(feats)
# Features normalization if needed
for i in range(len(feats)):
feat = feats[i][:, :lengths[i]] # Excluding pad values.
mean = feat.mean(axis=-1, keepdims=True) if mean_norm else 0
std = feat.std(axis=-1, keepdims=True) if std_norm else 1
feats[i][:, :lengths[i]] = (feat - mean) / std
assert feats[i][:, lengths[
i]:].sum() == 0 # Padding valus should all be 0.
# Converts into ratios.
# the utterance of the max length doesn't need to padding
# the remaining utterances need to padding and all of them will be padded to max length
# we convert the original length of each utterance to the ratio of the max length
lengths = (lengths / lengths.max()).astype(np.float32)
return {'ids': ids, 'feats': feats, 'lengths': lengths}
def pad_right_to(array, target_shape, mode="constant", value=0):
"""
This function takes a numpy array of arbitrary shape and pads it to target
shape by appending values on the right.
Args:
array: input numpy array. Input array whose dimension we need to pad.
target_shape : (list, tuple). Target shape we want for the target array its len must be equal to array.ndim
mode : str. Pad mode, please refer to numpy.pad documentation.
value : float. Pad value, please refer to numpy.pad documentation.
Returns:
array: numpy.array. Padded array.
valid_vals : list. List containing proportion for each dimension of original, non-padded values.
"""
assert len(target_shape) == array.ndim
pads = [] # this contains the abs length of the padding for each dimension.
valid_vals = [] # this contains the relative lengths for each dimension.
i = 0 # iterating over target_shape ndims
while i < len(target_shape):
assert (target_shape[i] >= array.shape[i]
), "Target shape must be >= original shape for every dim"
pads.append([0, target_shape[i] - array.shape[i]])
valid_vals.append(array.shape[i] / target_shape[i])
i += 1
array = numpy.pad(array, pads, mode=mode, constant_values=value)
return array, valid_vals
def batch_pad_right(arrays, mode="constant", value=0):
"""Given a list of numpy arrays it batches them together by padding to the right
on each dimension in order to get same length for all.
Args:
arrays : list. List of array we wish to pad together.
mode : str. Padding mode see numpy.pad documentation.
value : float. Padding value see numpy.pad documentation.
Returns:
array : numpy.array. Padded array.
valid_vals : list. List containing proportion for each dimension of original, non-padded values.
"""
if not len(arrays):
raise IndexError("arrays list must not be empty")
if len(arrays) == 1:
# if there is only one array in the batch we simply unsqueeze it.
return numpy.expand_dims(arrays[0], axis=0), numpy.array([1.0])
if not (any(
[arrays[i].ndim == arrays[0].ndim for i in range(1, len(arrays))])):
raise IndexError("All arrays must have same number of dimensions")
# FIXME we limit the support here: we allow padding of only the last dimension
# need to remove this when feat extraction is updated to handle multichannel.
max_shape = []
for dim in range(arrays[0].ndim):
if dim != (arrays[0].ndim - 1):
if not all(
[x.shape[dim] == arrays[0].shape[dim] for x in arrays[1:]]):
raise EnvironmentError(
"arrays should have same dimensions except for last one")
max_shape.append(max([x.shape[dim] for x in arrays]))
batched = []
valid = []
for t in arrays:
# for each array we apply pad_right_to
padded, valid_percent = pad_right_to(
t, max_shape, mode=mode, value=value)
batched.append(padded)
valid.append(valid_percent[-1])
batched = numpy.stack(batched)
return batched, numpy.array(valid)