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PaddleSpeech/paddlespeech/t2s/modules/tacotron2/decoder.py

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# 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.
3 years ago
# Modified from espnet(https://github.com/espnet/espnet)
"""Tacotron2 decoder related modules."""
import paddle
import paddle.nn.functional as F
from paddle import nn
from paddlespeech.t2s.modules.tacotron2.attentions import AttForwardTA
class Prenet(nn.Layer):
"""Prenet module for decoder of Spectrogram prediction network.
This is a module of Prenet in the decoder of Spectrogram prediction network,
which described in `Natural TTS
Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions`_.
The Prenet preforms nonlinear conversion
of inputs before input to auto-regressive lstm,
which helps to learn diagonal attentions.
Notes
----------
This module alway applies dropout even in evaluation.
See the detail in `Natural TTS Synthesis by
Conditioning WaveNet on Mel Spectrogram Predictions`_.
.. _`Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions`:
https://arxiv.org/abs/1712.05884
"""
def __init__(self, idim, n_layers=2, n_units=256, dropout_rate=0.5):
"""Initialize prenet module.
Args:
idim (int):
Dimension of the inputs.
odim (int):
Dimension of the outputs.
n_layers (int, optional):
The number of prenet layers.
n_units (int, optional):
The number of prenet units.
"""
super().__init__()
self.dropout_rate = dropout_rate
self.prenet = nn.LayerList()
for layer in range(n_layers):
n_inputs = idim if layer == 0 else n_units
self.prenet.append(
nn.Sequential(nn.Linear(n_inputs, n_units), nn.ReLU()))
def forward(self, x):
"""Calculate forward propagation.
Args:
x (Tensor):
Batch of input tensors (B, ..., idim).
Returns:
Tensor: Batch of output tensors (B, ..., odim).
"""
for i in range(len(self.prenet)):
# F.dropout 引入了随机, tacotron2 的 dropout 是不能去掉的
x = F.dropout(self.prenet[i](x))
return x
class Postnet(nn.Layer):
"""Postnet module for Spectrogram prediction network.
This is a module of Postnet in Spectrogram prediction network,
which described in `Natural TTS Synthesis by
Conditioning WaveNet on Mel Spectrogram Predictions`_.
The Postnet predicts refines the predicted
Mel-filterbank of the decoder,
which helps to compensate the detail sturcture of spectrogram.
.. _`Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions`:
https://arxiv.org/abs/1712.05884
"""
def __init__(
self,
idim,
odim,
n_layers=5,
n_chans=512,
n_filts=5,
dropout_rate=0.5,
use_batch_norm=True, ):
"""Initialize postnet module.
Args:
idim (int): Dimension of the inputs.
odim (int): Dimension of the outputs.
n_layers (int, optional): The number of layers.
n_filts (int, optional): The number of filter size.
n_units (int, optional): The number of filter channels.
use_batch_norm (bool, optional): Whether to use batch normalization..
dropout_rate (float, optional): Dropout rate..
"""
super().__init__()
self.postnet = nn.LayerList()
for layer in range(n_layers - 1):
ichans = odim if layer == 0 else n_chans
ochans = odim if layer == n_layers - 1 else n_chans
if use_batch_norm:
self.postnet.append(
nn.Sequential(
nn.Conv1D(
ichans,
ochans,
n_filts,
stride=1,
padding=(n_filts - 1) // 2,
bias_attr=False, ),
nn.BatchNorm1D(ochans),
nn.Tanh(),
nn.Dropout(dropout_rate), ))
else:
self.postnet.append(
nn.Sequential(
nn.Conv1D(
ichans,
ochans,
n_filts,
stride=1,
padding=(n_filts - 1) // 2,
bias_attr=False, ),
nn.Tanh(),
nn.Dropout(dropout_rate), ))
ichans = n_chans if n_layers != 1 else odim
if use_batch_norm:
self.postnet.append(
nn.Sequential(
nn.Conv1D(
ichans,
odim,
n_filts,
stride=1,
padding=(n_filts - 1) // 2,
bias_attr=False, ),
nn.BatchNorm1D(odim),
nn.Dropout(dropout_rate), ))
else:
self.postnet.append(
nn.Sequential(
nn.Conv1D(
ichans,
odim,
n_filts,
stride=1,
padding=(n_filts - 1) // 2,
bias_attr=False, ),
nn.Dropout(dropout_rate), ))
def forward(self, xs):
"""Calculate forward propagation.
Args:
xs (Tensor): Batch of the sequences of padded input tensors (B, idim, Tmax).
Returns:
Tensor: Batch of padded output tensor. (B, odim, Tmax).
"""
for i in range(len(self.postnet)):
xs = self.postnet[i](xs)
return xs
class ZoneOutCell(nn.Layer):
"""ZoneOut Cell module.
This is a module of zoneout described in
`Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations`_.
This code is modified from `eladhoffer/seq2seq.pytorch`_.
Examples
----------
>>> lstm = paddle.nn.LSTMCell(16, 32)
>>> lstm = ZoneOutCell(lstm, 0.5)
.. _`Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations`:
https://arxiv.org/abs/1606.01305
.. _`eladhoffer/seq2seq.pytorch`:
https://github.com/eladhoffer/seq2seq.pytorch
"""
def __init__(self, cell, zoneout_rate=0.1):
"""Initialize zone out cell module.
Args:
cell (nn.Layer): Paddle recurrent cell module
e.g. `paddle.nn.LSTMCell`.
zoneout_rate (float, optional): Probability of zoneout from 0.0 to 1.0.
"""
super().__init__()
self.cell = cell
self.hidden_size = cell.hidden_size
self.zoneout_rate = zoneout_rate
if zoneout_rate > 1.0 or zoneout_rate < 0.0:
raise ValueError(
"zoneout probability must be in the range from 0.0 to 1.0.")
def forward(self, inputs, hidden):
"""Calculate forward propagation.
Args:
inputs (Tensor):
Batch of input tensor (B, input_size).
hidden (tuple):
- Tensor: Batch of initial hidden states (B, hidden_size).
- Tensor: Batch of initial cell states (B, hidden_size).
Returns:
Tensor:
Batch of next hidden states (B, hidden_size).
tuple:
- Tensor: Batch of next hidden states (B, hidden_size).
- Tensor: Batch of next cell states (B, hidden_size).
"""
# we only use the second output of LSTMCell in paddle
_, next_hidden = self.cell(inputs, hidden)
next_hidden = self._zoneout(hidden, next_hidden, self.zoneout_rate)
# to have the same output format with LSTMCell in paddle
return next_hidden[0], next_hidden
def _zoneout(self, h, next_h, prob):
# apply recursively
if isinstance(h, tuple):
num_h = len(h)
if not isinstance(prob, tuple):
prob = tuple([prob] * num_h)
return tuple(
[self._zoneout(h[i], next_h[i], prob[i]) for i in range(num_h)])
if self.training:
mask = paddle.bernoulli(paddle.ones([*paddle.shape(h)]) * prob)
return mask * h + (1 - mask) * next_h
else:
return prob * h + (1 - prob) * next_h
class Decoder(nn.Layer):
"""Decoder module of Spectrogram prediction network.
This is a module of decoder of Spectrogram prediction network in Tacotron2,
which described in `Natural TTS
Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions`_.
The decoder generates the sequence of
features from the sequence of the hidden states.
.. _`Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions`:
https://arxiv.org/abs/1712.05884
"""
def __init__(
self,
idim,
odim,
att,
dlayers=2,
dunits=1024,
prenet_layers=2,
prenet_units=256,
postnet_layers=5,
postnet_chans=512,
postnet_filts=5,
output_activation_fn=None,
cumulate_att_w=True,
use_batch_norm=True,
use_concate=True,
dropout_rate=0.5,
zoneout_rate=0.1,
reduction_factor=1, ):
"""Initialize Tacotron2 decoder module.
Args:
idim (int):
Dimension of the inputs.
odim (int):
Dimension of the outputs.
att (nn.Layer):
Instance of attention class.
dlayers (int, optional):
The number of decoder lstm layers.
dunits (int, optional):
The number of decoder lstm units.
prenet_layers (int, optional):
The number of prenet layers.
prenet_units (int, optional):
The number of prenet units.
postnet_layers (int, optional):
The number of postnet layers.
postnet_filts (int, optional):
The number of postnet filter size.
postnet_chans (int, optional):
The number of postnet filter channels.
output_activation_fn (nn.Layer, optional):
Activation function for outputs.
cumulate_att_w (bool, optional):
Whether to cumulate previous attention weight.
use_batch_norm (bool, optional):
Whether to use batch normalization.
use_concate (bool, optional):
Whether to concatenate encoder embedding with decoder lstm outputs.
dropout_rate (float, optional):
Dropout rate.
zoneout_rate (float, optional):
Zoneout rate.
reduction_factor (int, optional):
Reduction factor.
"""
super().__init__()
# store the hyperparameters
self.idim = idim
self.odim = odim
self.att = att
self.output_activation_fn = output_activation_fn
self.cumulate_att_w = cumulate_att_w
self.use_concate = use_concate
self.reduction_factor = reduction_factor
# check attention type
if isinstance(self.att, AttForwardTA):
self.use_att_extra_inputs = True
else:
self.use_att_extra_inputs = False
# define lstm network
prenet_units = prenet_units if prenet_layers != 0 else odim
self.lstm = nn.LayerList()
for layer in range(dlayers):
iunits = idim + prenet_units if layer == 0 else dunits
lstm = nn.LSTMCell(iunits, dunits)
if zoneout_rate > 0.0:
lstm = ZoneOutCell(lstm, zoneout_rate)
self.lstm.append(lstm)
# define prenet
if prenet_layers > 0:
self.prenet = Prenet(
idim=odim,
n_layers=prenet_layers,
n_units=prenet_units,
dropout_rate=dropout_rate, )
else:
self.prenet = None
# define postnet
if postnet_layers > 0:
self.postnet = Postnet(
idim=idim,
odim=odim,
n_layers=postnet_layers,
n_chans=postnet_chans,
n_filts=postnet_filts,
use_batch_norm=use_batch_norm,
dropout_rate=dropout_rate, )
else:
self.postnet = None
# define projection layers
iunits = idim + dunits if use_concate else dunits
self.feat_out = nn.Linear(
iunits, odim * reduction_factor, bias_attr=False)
self.prob_out = nn.Linear(iunits, reduction_factor)
def _zero_state(self, hs):
init_hs = paddle.zeros([paddle.shape(hs)[0], self.lstm[0].hidden_size])
return init_hs
def forward(self, hs, hlens, ys):
"""Calculate forward propagation.
Args:
hs (Tensor):
Batch of the sequences of padded hidden states (B, Tmax, idim).
hlens (Tensor(int64) padded):
Batch of lengths of each input batch (B,).
ys (Tensor):
Batch of the sequences of padded target features (B, Lmax, odim).
Returns:
Tensor:
Batch of output tensors after postnet (B, Lmax, odim).
Tensor:
Batch of output tensors before postnet (B, Lmax, odim).
Tensor:
Batch of logits of stop prediction (B, Lmax).
Tensor:
Batch of attention weights (B, Lmax, Tmax).
Note:
This computation is performed in teacher-forcing manner.
"""
# thin out frames (B, Lmax, odim) -> (B, Lmax/r, odim)
if self.reduction_factor > 1:
ys = ys[:, self.reduction_factor - 1::self.reduction_factor]
# length list should be list of int
# hlens = list(map(int, hlens))
# initialize hidden states of decoder
c_list = [self._zero_state(hs)]
z_list = [self._zero_state(hs)]
for _ in range(1, len(self.lstm)):
c_list.append(self._zero_state(hs))
z_list.append(self._zero_state(hs))
prev_out = paddle.zeros([paddle.shape(hs)[0], self.odim])
# initialize attention
prev_att_ws = []
prev_att_w = paddle.zeros(paddle.shape(hlens))
prev_att_ws.append(prev_att_w)
self.att.reset()
# loop for an output sequence
outs, logits, att_ws = [], [], []
for y in ys.transpose([1, 0, 2]):
if self.use_att_extra_inputs:
att_c, att_w = self.att(hs, hlens, z_list[0], prev_att_ws[-1],
prev_out)
else:
att_c, att_w = self.att(hs, hlens, z_list[0], prev_att_ws[-1])
prenet_out = self.prenet(
prev_out) if self.prenet is not None else prev_out
xs = paddle.concat([att_c, prenet_out], axis=1)
# we only use the second output of LSTMCell in paddle
_, next_hidden = self.lstm[0](xs, (z_list[0], c_list[0]))
z_list[0], c_list[0] = next_hidden
for i in range(1, len(self.lstm)):
# we only use the second output of LSTMCell in paddle
_, next_hidden = self.lstm[i](z_list[i - 1],
(z_list[i], c_list[i]))
z_list[i], c_list[i] = next_hidden
zcs = (paddle.concat([z_list[-1], att_c], axis=1)
if self.use_concate else z_list[-1])
outs.append(
self.feat_out(zcs).reshape([paddle.shape(hs)[0], self.odim, -1
]))
logits.append(self.prob_out(zcs))
att_ws.append(att_w)
# teacher forcing
prev_out = y
if self.cumulate_att_w and paddle.sum(prev_att_w) != 0:
prev_att_w = prev_att_w + att_w # Note: error when use +=
else:
prev_att_w = att_w
prev_att_ws.append(prev_att_w)
# (B, Lmax)
logits = paddle.concat(logits, axis=1)
# (B, odim, Lmax)
before_outs = paddle.concat(outs, axis=2)
# (B, Lmax, Tmax)
att_ws = paddle.stack(att_ws, axis=1)
if self.reduction_factor > 1:
# (B, odim, Lmax)
before_outs = before_outs.reshape(
[paddle.shape(before_outs)[0], self.odim, -1])
if self.postnet is not None:
# (B, odim, Lmax)
after_outs = before_outs + self.postnet(before_outs)
else:
after_outs = before_outs
# (B, Lmax, odim)
before_outs = before_outs.transpose([0, 2, 1])
# (B, Lmax, odim)
after_outs = after_outs.transpose([0, 2, 1])
logits = logits
# apply activation function for scaling
if self.output_activation_fn is not None:
before_outs = self.output_activation_fn(before_outs)
after_outs = self.output_activation_fn(after_outs)
return after_outs, before_outs, logits, att_ws
def inference(
self,
h,
threshold=0.5,
minlenratio=0.0,
maxlenratio=10.0,
use_att_constraint=False,
backward_window=None,
forward_window=None, ):
"""Generate the sequence of features given the sequences of characters.
Args:
h(Tensor):
Input sequence of encoder hidden states (T, C).
threshold(float, optional, optional):
Threshold to stop generation. (Default value = 0.5)
minlenratio(float, optional, optional):
Minimum length ratio. If set to 1.0 and the length of input is 10,
the minimum length of outputs will be 10 * 1 = 10. (Default value = 0.0)
maxlenratio(float, optional, optional):
Minimum length ratio. If set to 10 and the length of input is 10,
the maximum length of outputs will be 10 * 10 = 100. (Default value = 0.0)
use_att_constraint(bool, optional):
Whether to apply attention constraint introduced in `Deep Voice 3`_. (Default value = False)
backward_window(int, optional):
Backward window size in attention constraint. (Default value = None)
forward_window(int, optional):
(Default value = None)
Returns:
Tensor:
Output sequence of features (L, odim).
Tensor:
Output sequence of stop probabilities (L,).
Tensor:
Attention weights (L, T).
Note:
This computation is performed in auto-regressive manner.
.. _`Deep Voice 3`: https://arxiv.org/abs/1710.07654
"""
# setup
assert len(paddle.shape(h)) == 2
hs = h.unsqueeze(0)
ilens = paddle.shape(h)[0]
# 本来 maxlen 和 minlen 外面有 int(),防止动转静的问题此处删除
maxlen = paddle.shape(h)[0] * maxlenratio
minlen = paddle.shape(h)[0] * minlenratio
# 本来是直接使用 threshold 的,此处为了防止动转静的问题把 threshold 转成 tensor
threshold = paddle.ones([1]) * threshold
# initialize hidden states of decoder
c_list = [self._zero_state(hs)]
z_list = [self._zero_state(hs)]
for _ in range(1, len(self.lstm)):
c_list.append(self._zero_state(hs))
z_list.append(self._zero_state(hs))
prev_out = paddle.zeros([1, self.odim])
# initialize attention
prev_att_ws = []
prev_att_w = paddle.zeros([ilens])
prev_att_ws.append(prev_att_w)
self.att.reset()
# setup for attention constraint
if use_att_constraint:
last_attended_idx = 0
else:
last_attended_idx = -1
# loop for an output sequence
idx = 0
outs, att_ws, probs = [], [], []
prob = paddle.zeros([1])
while paddle.to_tensor(True):
z_list = z_list
c_list = c_list
# updated index
idx += self.reduction_factor
# decoder calculation
if self.use_att_extra_inputs:
att_c, att_w = self.att(
hs,
ilens,
z_list[0],
prev_att_ws[-1],
prev_out,
last_attended_idx=last_attended_idx,
backward_window=backward_window,
forward_window=forward_window, )
else:
att_c, att_w = self.att(
hs,
ilens,
z_list[0],
prev_att_ws[-1],
last_attended_idx=last_attended_idx,
backward_window=backward_window,
forward_window=forward_window, )
att_ws.append(att_w)
prenet_out = self.prenet(
prev_out) if self.prenet is not None else prev_out
xs = paddle.concat([att_c, prenet_out], axis=1)
# we only use the second output of LSTMCell in paddle
_, next_hidden = self.lstm[0](xs, (z_list[0], c_list[0]))
z_list[0], c_list[0] = next_hidden
for i in range(1, len(self.lstm)):
# we only use the second output of LSTMCell in paddle
_, next_hidden = self.lstm[i](z_list[i - 1],
(z_list[i], c_list[i]))
z_list[i], c_list[i] = next_hidden
zcs = (paddle.concat([z_list[-1], att_c], axis=1)
if self.use_concate else z_list[-1])
# [(1, odim, r), ...]
outs.append(self.feat_out(zcs).reshape([1, self.odim, -1]))
prob = F.sigmoid(self.prob_out(zcs))[0]
probs.append(prob)
if self.output_activation_fn is not None:
prev_out = self.output_activation_fn(
outs[-1][:, :, -1]) # (1, odim)
else:
prev_out = outs[-1][:, :, -1] # (1, odim)
if self.cumulate_att_w and paddle.sum(prev_att_w) != 0:
prev_att_w = prev_att_w + att_w # Note: error when use +=
else:
prev_att_w = att_w
prev_att_ws.append(prev_att_w)
if use_att_constraint:
last_attended_idx = int(att_w.argmax())
# tacotron2 ljspeech 动转静的问题应该是这里没有正确判断 prob >= threshold 导致的
if prob >= threshold or idx >= maxlen:
# check mininum length
if idx < minlen:
continue
break
"""
仅解开 665~667 行的代码块动转静时会卡死但是动态图时可以正确生成音频证明模型没问题
同时解开 665~667 668 ~ 670 行的代码块动转静时不会卡死但是生成的音频末尾有多余的噪声
证明动转静没有进入 prob >= threshold 的判断但是静态图可以进入 prob >= threshold 并退出循环
动转静时是通过 idx >= maxlen 退出循环所以没有这个逻辑的时候会一直循环也就是卡死
没有在模型判断该结束的时候结束而是在超出最大长度时结束所以合成的音频末尾有很长的额外预测的噪声
动转静用 prob <= threshold 的条件可以退出循环虽然结果不正确证明条件参数的类型本身没问题可能是 prob 有问题
"""
# if prob >= threshold:
# print("prob >= threshold")
# break
# elif idx >= maxlen:
# print("idx >= maxlen")
# break
# (1, odim, L)
outs = paddle.concat(outs, axis=2)
if self.postnet is not None:
# (1, odim, L)
outs = outs + self.postnet(outs)
# (L, odim)
outs = outs.transpose([0, 2, 1]).squeeze(0)
probs = paddle.concat(probs, axis=0)
att_ws = paddle.concat(att_ws, axis=0)
if self.output_activation_fn is not None:
outs = self.output_activation_fn(outs)
return outs, probs, att_ws
def calculate_all_attentions(self, hs, hlens, ys):
"""Calculate all of the attention weights.
Args:
hs (Tensor):
Batch of the sequences of padded hidden states (B, Tmax, idim).
hlens (Tensor(int64)):
Batch of lengths of each input batch (B,).
ys (Tensor):
Batch of the sequences of padded target features (B, Lmax, odim).
Returns:
numpy.ndarray:
Batch of attention weights (B, Lmax, Tmax).
Note:
This computation is performed in teacher-forcing manner.
"""
# thin out frames (B, Lmax, odim) -> (B, Lmax/r, odim)
if self.reduction_factor > 1:
ys = ys[:, self.reduction_factor - 1::self.reduction_factor]
# length list should be list of int
hlens = list(map(int, hlens))
# initialize hidden states of decoder
c_list = [self._zero_state(hs)]
z_list = [self._zero_state(hs)]
for _ in range(1, len(self.lstm)):
c_list.append(self._zero_state(hs))
z_list.append(self._zero_state(hs))
prev_out = paddle.zeros([paddle.shape(hs)[0], self.odim])
# initialize attention
prev_att_w = None
self.att.reset()
# loop for an output sequence
att_ws = []
for y in ys.transpose([1, 0, 2]):
if self.use_att_extra_inputs:
att_c, att_w = self.att(hs, hlens, z_list[0], prev_att_w,
prev_out)
else:
att_c, att_w = self.att(hs, hlens, z_list[0], prev_att_w)
att_ws.append(att_w)
prenet_out = self.prenet(
prev_out) if self.prenet is not None else prev_out
xs = paddle.concat([att_c, prenet_out], axis=1)
# we only use the second output of LSTMCell in paddle
_, next_hidden = self.lstm[0](xs, (z_list[0], c_list[0]))
z_list[0], c_list[0] = next_hidden
for i in range(1, len(self.lstm)):
z_list[i], c_list[i] = self.lstm[i](z_list[i - 1],
(z_list[i], c_list[i]))
# teacher forcing
prev_out = y
if self.cumulate_att_w and prev_att_w is not None:
# Note: error when use +=
prev_att_w = prev_att_w + att_w
else:
prev_att_w = att_w
# (B, Lmax, Tmax)
att_ws = paddle.stack(att_ws, axis=1)
return att_ws