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PaddleSpeech/paddlespeech/t2s/models/vits/posterior_encoder.py

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4.4 KiB

# 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.
"""Text encoder module in VITS.
This code is based on https://github.com/jaywalnut310/vits.
"""
from typing import Optional
from typing import Tuple
import paddle
from paddle import nn
from paddlespeech.t2s.models.vits.wavenet.wavenet import WaveNet
from paddlespeech.t2s.modules.nets_utils import make_non_pad_mask
class PosteriorEncoder(nn.Layer):
"""Posterior encoder module in VITS.
This is a module of posterior encoder described 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=513,
out_channels: int=192,
hidden_channels: int=192,
kernel_size: int=5,
layers: int=16,
stacks: int=1,
base_dilation: int=1,
global_channels: int=-1,
dropout_rate: float=0.0,
bias: bool=True,
use_weight_norm: bool=True, ):
"""Initilialize PosteriorEncoder module.
Args:
in_channels (int): Number of input channels.
out_channels (int): Number of output channels.
hidden_channels (int): Number of hidden channels.
kernel_size (int): Kernel size in WaveNet.
layers (int): Number of layers of WaveNet.
stacks (int): Number of repeat stacking of WaveNet.
base_dilation (int): Base dilation factor.
global_channels (int): Number of global conditioning channels.
dropout_rate (float): Dropout rate.
bias (bool): Whether to use bias parameters in conv.
use_weight_norm (bool): Whether to apply weight norm.
"""
super().__init__()
# define modules
self.input_conv = nn.Conv1D(in_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, )
self.proj = nn.Conv1D(hidden_channels, out_channels * 2, 1)
def forward(
self,
x: paddle.Tensor,
x_lengths: paddle.Tensor,
g: Optional[paddle.Tensor]=None
) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor, paddle.Tensor]:
"""Calculate forward propagation.
Args:
x (Tensor): Input tensor (B, in_channels, T_feats).
x_lengths (Tensor): Length tensor (B,).
g (Optional[Tensor]): Global conditioning tensor (B, global_channels, 1).
Returns:
Tensor: Encoded hidden representation tensor (B, out_channels, T_feats).
Tensor: Projected mean tensor (B, out_channels, T_feats).
Tensor: Projected scale tensor (B, out_channels, T_feats).
Tensor: Mask tensor for input tensor (B, 1, T_feats).
"""
x_mask = make_non_pad_mask(x_lengths).unsqueeze(1)
x = self.input_conv(x) * x_mask
x = self.encoder(x, x_mask, g=g)
stats = self.proj(x) * x_mask
m, logs = paddle.split(stats, 2, axis=1)
z = (m + paddle.randn(paddle.shape(m)) * paddle.exp(logs)) * x_mask
return z, m, logs, x_mask