# Copyright (c) 2022 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.
"""Residual affine coupling modules in VITS.
This code is based on https://github.com/jaywalnut310/vits.
"""
from typing import Optional
from typing import Tuple
from typing import Union
import paddle
from paddle import nn
from paddlespeech.t2s.models.vits.flow import FlipFlow
from paddlespeech.t2s.models.vits.wavenet.wavenet import WaveNet
[docs]class ResidualAffineCouplingBlock(nn.Layer):
"""Residual affine coupling block module.
This is a module of residual affine coupling block, which used as "Flow" in
`Conditional Variational Autoencoder with Adversarial Learning for End-to-End
Text-to-Speech`_.
.. _`Conditional Variational Autoencoder with Adversarial Learning for End-to-End
Text-to-Speech`: https://arxiv.org/abs/2006.04558
"""
def __init__(
self,
in_channels: int=192,
hidden_channels: int=192,
flows: int=4,
kernel_size: int=5,
base_dilation: int=1,
layers: int=4,
global_channels: int=-1,
dropout_rate: float=0.0,
use_weight_norm: bool=True,
bias: bool=True,
use_only_mean: bool=True, ):
"""Initilize ResidualAffineCouplingBlock module.
Args:
in_channels (int):
Number of input channels.
hidden_channels (int):
Number of hidden channels.
flows (int):
Number of flows.
kernel_size (int):
Kernel size for WaveNet.
base_dilation (int):
Base dilation factor for WaveNet.
layers (int):
Number of layers of WaveNet.
stacks (int):
Number of stacks of WaveNet.
global_channels (int):
Number of global channels.
dropout_rate (float):
Dropout rate.
use_weight_norm (bool):
Whether to use weight normalization in WaveNet.
bias (bool):
Whether to use bias paramters in WaveNet.
use_only_mean (bool):
Whether to estimate only mean.
"""
super().__init__()
self.flows = nn.LayerList()
for i in range(flows):
self.flows.append(
ResidualAffineCouplingLayer(
in_channels=in_channels,
hidden_channels=hidden_channels,
kernel_size=kernel_size,
base_dilation=base_dilation,
layers=layers,
stacks=1,
global_channels=global_channels,
dropout_rate=dropout_rate,
use_weight_norm=use_weight_norm,
bias=bias,
use_only_mean=use_only_mean, ))
self.flows.append(FlipFlow())
[docs] def forward(
self,
x: paddle.Tensor,
x_mask: paddle.Tensor,
g: Optional[paddle.Tensor]=None,
inverse: bool=False, ) -> paddle.Tensor:
"""Calculate forward propagation.
Args:
x (Tensor):
Input tensor (B, in_channels, T).
x_mask (Tensor):
Length tensor (B, 1, T).
g (Optional[Tensor]):
Global conditioning tensor (B, global_channels, 1).
inverse (bool):
Whether to inverse the flow.
Returns:
Tensor: Output tensor (B, in_channels, T).
"""
if not inverse:
for flow in self.flows:
x, _ = flow(x, x_mask, g=g, inverse=inverse)
else:
for flow in reversed(self.flows):
x = flow(x, x_mask, g=g, inverse=inverse)
return x
[docs]class ResidualAffineCouplingLayer(nn.Layer):
"""Residual affine coupling layer."""
def __init__(
self,
in_channels: int=192,
hidden_channels: int=192,
kernel_size: int=5,
base_dilation: int=1,
layers: int=5,
stacks: int=1,
global_channels: int=-1,
dropout_rate: float=0.0,
use_weight_norm: bool=True,
bias: bool=True,
use_only_mean: bool=True, ):
"""Initialzie ResidualAffineCouplingLayer module.
Args:
in_channels (int):
Number of input channels.
hidden_channels (int):
Number of hidden channels.
kernel_size (int):
Kernel size for WaveNet.
base_dilation (int):
Base dilation factor for WaveNet.
layers (int):
Number of layers of WaveNet.
stacks (int):
Number of stacks of WaveNet.
global_channels (int):
Number of global channels.
dropout_rate (float):
Dropout rate.
use_weight_norm (bool):
Whether to use weight normalization in WaveNet.
bias (bool):
Whether to use bias paramters in WaveNet.
use_only_mean (bool):
Whether to estimate only mean.
"""
assert in_channels % 2 == 0, "in_channels should be divisible by 2"
super().__init__()
self.half_channels = in_channels // 2
self.use_only_mean = use_only_mean
# define modules
self.input_conv = nn.Conv1D(
self.half_channels,
hidden_channels,
1, )
self.encoder = WaveNet(
in_channels=-1,
out_channels=-1,
kernel_size=kernel_size,
layers=layers,
stacks=stacks,
base_dilation=base_dilation,
residual_channels=hidden_channels,
aux_channels=-1,
gate_channels=hidden_channels * 2,
skip_channels=hidden_channels,
global_channels=global_channels,
dropout_rate=dropout_rate,
bias=bias,
use_weight_norm=use_weight_norm,
use_first_conv=False,
use_last_conv=False,
scale_residual=False,
scale_skip_connect=True, )
if use_only_mean:
self.proj = nn.Conv1D(
hidden_channels,
self.half_channels,
1, )
else:
self.proj = nn.Conv1D(
hidden_channels,
self.half_channels * 2,
1, )
weight = paddle.zeros(paddle.shape(self.proj.weight))
self.proj.weight = paddle.create_parameter(
shape=weight.shape,
dtype=str(weight.numpy().dtype),
default_initializer=paddle.nn.initializer.Assign(weight))
bias = paddle.zeros(paddle.shape(self.proj.bias))
self.proj.bias = paddle.create_parameter(
shape=bias.shape,
dtype=str(bias.numpy().dtype),
default_initializer=paddle.nn.initializer.Assign(bias))
[docs] def forward(
self,
x: paddle.Tensor,
x_mask: paddle.Tensor,
g: Optional[paddle.Tensor]=None,
inverse: bool=False,
) -> Union[paddle.Tensor, Tuple[paddle.Tensor, paddle.Tensor]]:
"""Calculate forward propagation.
Args:
x (Tensor):
Input tensor (B, in_channels, T).
x_lengths (Tensor):
Length tensor (B,).
g (Optional[Tensor]):
Global conditioning tensor (B, global_channels, 1).
inverse (bool):
Whether to inverse the flow.
Returns:
Tensor:
Output tensor (B, in_channels, T).
Tensor:
Log-determinant tensor for NLL (B,) if not inverse.
"""
xa, xb = paddle.split(x, 2, axis=1)
h = self.input_conv(xa) * x_mask
h = self.encoder(h, x_mask, g=g)
stats = self.proj(h) * x_mask
if not self.use_only_mean:
m, logs = paddle.split(stats, 2, axis=1)
else:
m = stats
logs = paddle.zeros(paddle.shape(m))
if not inverse:
xb = m + xb * paddle.exp(logs) * x_mask
x = paddle.concat([xa, xb], 1)
logdet = paddle.sum(logs, [1, 2])
return x, logdet
else:
xb = (xb - m) * paddle.exp(-logs) * x_mask
x = paddle.concat([xa, xb], 1)
return x