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