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