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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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from paddlespeech.t2s.audio.codec import encode_mu_law
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from paddlespeech.t2s.audio.codec import float_2_label
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from paddlespeech.t2s.audio.codec import label_2_float
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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 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, batch):
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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. Audio shape (T, ), features shape(T', C).
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Returns:
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Tensor:
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Target signal batch (B, 1, T).
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Tensor:
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Auxiliary feature batch (B, C, T'), where
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T = (T' - 2 * aux_context_window) * hop_size.
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"""
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# check length
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batch = [
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self._adjust_length(b['wave'], b['feats']) for b in batch
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if b['feats'].shape[0] > self.mel_threshold
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]
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xs, cs = [b[0] for b in batch], [b[1] for b in batch]
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# make batch with random cut
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c_lengths = [c.shape[0] 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 = np.stack(
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[x[start:end] for x, start, end in zip(xs, x_starts, x_ends)])
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c_batch = np.stack(
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[c[start:end] for c, start, end in zip(cs, c_starts, c_ends)])
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# convert each batch to tensor, assume 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 y_batch, c_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) < c.shape[0] * self.hop_size:
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x = np.pad(x, (0, c.shape[0] * self.hop_size - len(x)), mode="edge")
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elif len(x) > c.shape[0] * self.hop_size:
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x = x[:c.shape[0] * self.hop_size]
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# check the legnth is valid
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assert len(x) == c.shape[
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0] * self.hop_size, f"wave length: ({len(x)}), mel length: ({c.shape[0]})"
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return x, c
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class WaveRNNClip(Clip):
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def __init__(self,
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mode: str='RAW',
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batch_max_steps: int=4500,
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hop_size: int=300,
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aux_context_window: int=2,
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bits: int=9,
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mu_law: bool=True):
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self.mode = mode
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self.mel_win = batch_max_steps // hop_size + 2 * aux_context_window
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self.batch_max_steps = batch_max_steps
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self.hop_size = hop_size
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self.aux_context_window = aux_context_window
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self.mu_law = mu_law
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self.batch_max_frames = batch_max_steps // hop_size
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self.mel_threshold = self.batch_max_frames + 2 * aux_context_window
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if self.mode == 'MOL':
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self.bits = 16
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else:
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self.bits = bits
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def to_quant(self, wav):
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if self.mode == 'RAW':
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if self.mu_law:
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quant = encode_mu_law(wav, mu=2**self.bits)
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else:
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quant = float_2_label(wav, bits=self.bits)
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elif self.mode == 'MOL':
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quant = float_2_label(wav, bits=16)
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quant = quant.astype(np.int64)
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return quant
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def __call__(self, batch):
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# voc_pad = 2 this will pad the input so that the resnet can 'see' wider than input length
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# max_offsets = n_frames - 2 - (mel_win + 2 * hp.voc_pad) = n_frames - 15
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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. Audio shape (T, ), features shape(T', C).
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Returns:
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Tensor: Input signal batch (B, 1, T).
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Tensor: Target signal batch (B, 1, T).
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Tensor: Auxiliary feature batch (B, C, T'),
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where T = (T' - 2 * aux_context_window) * hop_size.
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"""
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# check length
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batch = [
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self._adjust_length(b['wave'], b['feats']) for b in batch
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if b['feats'].shape[0] > self.mel_threshold
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]
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wav, mel = [b[0] for b in batch], [b[1] for b in batch]
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# mel 此处需要转置
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mel = [x.T for x in mel]
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max_offsets = [
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x.shape[-1] - 2 - (self.mel_win + 2 * self.aux_context_window)
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for x in mel
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]
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# the slice point of mel selecting randomly
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mel_offsets = [np.random.randint(0, offset) for offset in max_offsets]
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# the slice point of wav selecting randomly, which is behind 2(=pad) frames
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sig_offsets = [(offset + self.aux_context_window) * self.hop_size
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for offset in mel_offsets]
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# mels.shape[1] = voc_seq_len // hop_length + 2 * voc_pad
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mels = [
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x[:, mel_offsets[i]:mel_offsets[i] + self.mel_win]
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for i, x in enumerate(mel)
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]
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# label.shape[1] = voc_seq_len + 1
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wav = [self.to_quant(x) for x in wav]
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labels = [
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x[sig_offsets[i]:sig_offsets[i] + self.batch_max_steps + 1]
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for i, x in enumerate(wav)
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]
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mels = np.stack(mels).astype(np.float32)
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labels = np.stack(labels).astype(np.int64)
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mels = paddle.to_tensor(mels)
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labels = paddle.to_tensor(labels, dtype='int64')
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# x is input, y is label
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x = labels[:, :self.batch_max_steps]
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y = labels[:, 1:]
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'''
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mode = RAW:
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mu_law = True:
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quant: bits = 9 0, 1, 2, ..., 509, 510, 511 int
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mu_law = False
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quant bits = 9 [0， 511] float
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mode = MOL:
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quant: bits = 16 [0. 65536] float
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'''
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# x should be normalizes in.[0, 1] in RAW mode
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x = label_2_float(paddle.cast(x, dtype='float32'), self.bits)
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# y should be normalizes in.[0, 1] in MOL mode
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if self.mode == 'MOL':
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y = label_2_float(paddle.cast(y, dtype='float32'), self.bits)
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return x, y, mels
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# for paddleslim
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class Clip_static(Clip):
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"""Collate functor for training vocoders.
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"""
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def __call__(self, batch):
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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. Audio shape (T, ), features shape(T', C).
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Returns:
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Dict[str, np.array]:
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Auxiliary feature batch (B, C, T'), where
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T = (T' - 2 * aux_context_window) * hop_size.
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"""
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# check length
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batch = [
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self._adjust_length(b['wave'], b['feats']) for b in batch
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if b['feats'].shape[0] > self.mel_threshold
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]
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xs, cs = [b[0] for b in batch], [b[1] for b in batch]
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# make batch with random cut
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c_lengths = [c.shape[0] 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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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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c_batch = np.stack(
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[c[start:end] for c, start, end in zip(cs, c_starts, c_ends)])
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# infer axis (T',C) is different with train axis (B, C, T')
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# c_batch = c_batch.transpose([0, 2, 1]) # (B, C, T')
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# do not need batch axis in infer
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c_batch = c_batch[0]
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batch = {"logmel": c_batch}
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return batch
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