You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
123 lines
4.3 KiB
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
|