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113 lines
4.2 KiB
113 lines
4.2 KiB
# 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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# Modified from espnet(https://github.com/espnet/espnet)
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"""Residual stack module in MelGAN."""
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from typing import Any
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from typing import Dict
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from paddle import nn
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from paddlespeech.t2s.modules.activation import get_activation
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from paddlespeech.t2s.modules.causal_conv import CausalConv1D
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class ResidualStack(nn.Layer):
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"""Residual stack module introduced in MelGAN."""
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def __init__(
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self,
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kernel_size: int=3,
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channels: int=32,
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dilation: int=1,
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bias: bool=True,
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nonlinear_activation: str="leakyrelu",
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nonlinear_activation_params: Dict[str, Any]={"negative_slope": 0.2},
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pad: str="Pad1D",
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pad_params: Dict[str, Any]={"mode": "reflect"},
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use_causal_conv: bool=False, ):
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"""Initialize ResidualStack module.
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Args:
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kernel_size (int):
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Kernel size of dilation convolution layer.
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channels (int):
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Number of channels of convolution layers.
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dilation (int):
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Dilation factor.
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bias (bool):
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Whether to add bias parameter in convolution layers.
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nonlinear_activation (str):
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Activation function module name.
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nonlinear_activation_params (Dict[str,Any]):
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Hyperparameters for activation function.
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pad (str):
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Padding function module name before dilated convolution layer.
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pad_params (Dict[str, Any]):
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Hyperparameters for padding function.
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use_causal_conv (bool):
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Whether to use causal convolution.
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"""
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super().__init__()
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# for compatibility
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if nonlinear_activation:
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nonlinear_activation = nonlinear_activation.lower()
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# defile residual stack part
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if not use_causal_conv:
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assert (kernel_size - 1
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) % 2 == 0, "Not support even number kernel size."
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self.stack = nn.Sequential(
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get_activation(nonlinear_activation,
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**nonlinear_activation_params),
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getattr(nn, pad)((kernel_size - 1) // 2 * dilation,
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**pad_params),
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nn.Conv1D(
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channels,
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channels,
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kernel_size,
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dilation=dilation,
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bias_attr=bias),
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get_activation(nonlinear_activation,
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**nonlinear_activation_params),
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nn.Conv1D(channels, channels, 1, bias_attr=bias), )
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else:
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self.stack = nn.Sequential(
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get_activation(nonlinear_activation,
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**nonlinear_activation_params),
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CausalConv1D(
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channels,
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channels,
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kernel_size,
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dilation=dilation,
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bias=bias,
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pad=pad,
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pad_params=pad_params, ),
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get_activation(nonlinear_activation,
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**nonlinear_activation_params),
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nn.Conv1D(channels, channels, 1, bias_attr=bias), )
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# defile extra layer for skip connection
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self.skip_layer = nn.Conv1D(channels, channels, 1, bias_attr=bias)
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def forward(self, c):
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"""Calculate forward propagation.
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Args:
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c (Tensor):
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Input tensor (B, channels, T).
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Returns:
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Tensor: Output tensor (B, chennels, T).
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"""
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return self.stack(c) + self.skip_layer(c)
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