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139 lines
4.7 KiB
139 lines
4.7 KiB
# Copyright (c) 2022 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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"""Text encoder module in VITS.
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This code is based on https://github.com/jaywalnut310/vits.
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"""
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from typing import Optional
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from typing import Tuple
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import paddle
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from paddle import nn
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from paddlespeech.t2s.models.vits.wavenet.wavenet import WaveNet
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from paddlespeech.t2s.modules.nets_utils import make_non_pad_mask
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class PosteriorEncoder(nn.Layer):
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"""Posterior encoder module in VITS.
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This is a module of posterior encoder described in `Conditional Variational
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Autoencoder with Adversarial Learning for End-to-End Text-to-Speech`_.
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.. _`Conditional Variational Autoencoder with Adversarial Learning for End-to-End
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Text-to-Speech`: https://arxiv.org/abs/2006.04558
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"""
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def __init__(
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self,
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in_channels: int=513,
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out_channels: int=192,
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hidden_channels: int=192,
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kernel_size: int=5,
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layers: int=16,
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stacks: int=1,
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base_dilation: int=1,
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global_channels: int=-1,
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dropout_rate: float=0.0,
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bias: bool=True,
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use_weight_norm: bool=True, ):
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"""Initilialize PosteriorEncoder module.
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Args:
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in_channels (int):
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Number of input channels.
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out_channels (int):
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Number of output channels.
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hidden_channels (int):
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Number of hidden channels.
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kernel_size (int):
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Kernel size in WaveNet.
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layers (int):
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Number of layers of WaveNet.
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stacks (int):
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Number of repeat stacking of WaveNet.
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base_dilation (int):
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Base dilation factor.
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global_channels (int):
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Number of global conditioning channels.
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dropout_rate (float):
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Dropout rate.
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bias (bool):
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Whether to use bias parameters in conv.
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use_weight_norm (bool):
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Whether to apply weight norm.
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"""
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super().__init__()
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# define modules
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self.input_conv = nn.Conv1D(in_channels, hidden_channels, 1)
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self.encoder = WaveNet(
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in_channels=-1,
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out_channels=-1,
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kernel_size=kernel_size,
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layers=layers,
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stacks=stacks,
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base_dilation=base_dilation,
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residual_channels=hidden_channels,
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aux_channels=-1,
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gate_channels=hidden_channels * 2,
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skip_channels=hidden_channels,
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global_channels=global_channels,
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dropout_rate=dropout_rate,
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bias=bias,
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use_weight_norm=use_weight_norm,
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use_first_conv=False,
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use_last_conv=False,
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scale_residual=False,
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scale_skip_connect=True, )
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self.proj = nn.Conv1D(hidden_channels, out_channels * 2, 1)
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def forward(
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self,
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x: paddle.Tensor,
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x_lengths: paddle.Tensor,
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g: Optional[paddle.Tensor]=None
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) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor, paddle.Tensor]:
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"""Calculate forward propagation.
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Args:
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x (Tensor):
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Input tensor (B, in_channels, T_feats).
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x_lengths (Tensor):
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Length tensor (B,).
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g (Optional[Tensor]):
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Global conditioning tensor (B, global_channels, 1).
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Returns:
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Tensor:
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Encoded hidden representation tensor (B, out_channels, T_feats).
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Tensor:
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Projected mean tensor (B, out_channels, T_feats).
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Tensor:
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Projected scale tensor (B, out_channels, T_feats).
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Tensor:
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Mask tensor for input tensor (B, 1, T_feats).
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"""
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x_mask = make_non_pad_mask(x_lengths).unsqueeze(1)
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x = self.input_conv(x) * x_mask
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x = self.encoder(x, x_mask, g=g)
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stats = self.proj(x) * x_mask
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m, logs = paddle.split(stats, 2, axis=1)
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z = (m + paddle.randn(paddle.shape(m)) * paddle.exp(logs)) * x_mask
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return z, m, logs, x_mask
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