You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
114 lines
4.2 KiB
114 lines
4.2 KiB
# 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
|
|
|
|
|
|
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.
|
|
Parameters
|
|
----------
|
|
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
|
|
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)
|
|
|
|
def forward(self, c):
|
|
"""Calculate forward propagation.
|
|
Parameters
|
|
----------
|
|
c : Tensor
|
|
Input tensor (B, channels, T).
|
|
Returns
|
|
----------
|
|
Tensor
|
|
Output tensor (B, chennels, T).
|
|
"""
|
|
return self.stack(c) + self.skip_layer(c)
|