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PaddleSpeech/parakeet/datasets/vocoder_batch_fn.py

123 lines
4.3 KiB

# Copyright (c) 2021 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
class Clip(object):
"""Collate functor for training vocoders.
"""
def __init__(
self,
batch_max_steps=20480,
hop_size=256,
aux_context_window=0, ):
"""Initialize customized collater for DataLoader.
Parameters
----------
batch_max_steps : int
The maximum length of input signal in batch.
hop_size : int
Hop size of auxiliary features.
aux_context_window : int
Context window size for auxiliary feature conv.
"""
if batch_max_steps % hop_size != 0:
batch_max_steps += -(batch_max_steps % hop_size)
assert batch_max_steps % hop_size == 0
self.batch_max_steps = batch_max_steps
self.batch_max_frames = batch_max_steps // hop_size
self.hop_size = hop_size
self.aux_context_window = aux_context_window
# set useful values in random cutting
self.start_offset = aux_context_window
self.end_offset = -(self.batch_max_frames + aux_context_window)
self.mel_threshold = self.batch_max_frames + 2 * aux_context_window
def __call__(self, examples):
"""Convert into batch tensors.
Parameters
----------
batch : list
list of tuple of the pair of audio and features. Audio shape (T, ), features shape(T', C).
Returns
----------
Tensor
Auxiliary feature batch (B, C, T'), where
T = (T' - 2 * aux_context_window) * hop_size.
Tensor
Target signal batch (B, 1, T).
"""
# check length
examples = [
self._adjust_length(b['wave'], b['feats']) for b in examples
if b['feats'].shape[0] > self.mel_threshold
]
xs, cs = [b[0] for b in examples], [b[1] for b in examples]
# make batch with random cut
c_lengths = [c.shape[0] for c in cs]
start_frames = np.array([
np.random.randint(self.start_offset, cl + self.end_offset)
for cl in c_lengths
])
x_starts = start_frames * self.hop_size
x_ends = x_starts + self.batch_max_steps
c_starts = start_frames - self.aux_context_window
c_ends = start_frames + self.batch_max_frames + self.aux_context_window
y_batch = np.stack(
[x[start:end] for x, start, end in zip(xs, x_starts, x_ends)])
c_batch = np.stack(
[c[start:end] for c, start, end in zip(cs, c_starts, c_ends)])
# convert each batch to tensor, asuume that each item in batch has the same length
y_batch = paddle.to_tensor(
y_batch, dtype=paddle.float32).unsqueeze(1) # (B, 1, T)
c_batch = paddle.to_tensor(
c_batch, dtype=paddle.float32).transpose([0, 2, 1]) # (B, C, T')
return y_batch, c_batch
def _adjust_length(self, x, c):
"""Adjust the audio and feature lengths.
Note
-------
Basically we assume that the length of x and c are adjusted
through preprocessing stage, but if we use other library processed
features, this process will be needed.
"""
if len(x) < c.shape[0] * self.hop_size:
x = np.pad(x, (0, c.shape[0] * self.hop_size - len(x)), mode="edge")
elif len(x) > c.shape[0] * self.hop_size:
print(
f"wave length: ({len(x)}), mel length: ({c.shape[0]}), hop size: ({self.hop_size })"
)
x = x[:c.shape[1] * self.hop_size]
# check the legnth is valid
assert len(x) == c.shape[
0] * self.hop_size, f"wave length: ({len(x)}), mel length: ({c.shape[0]})"
return x, c