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# -*- coding: utf-8 -*-
# Copyright 2019 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Parallel WaveGAN Modules."""
import logging
import math
import torch
from torch import nn
from modules.parallel_wavegan.layers import Conv1d
from modules.parallel_wavegan.layers import Conv1d1x1
from modules.parallel_wavegan.layers import ResidualBlock
from modules.parallel_wavegan.layers import upsample
from modules.parallel_wavegan import models
class ParallelWaveGANGenerator(torch.nn.Module):
"""Parallel WaveGAN Generator module."""
def __init__(self,
in_channels=1,
out_channels=1,
kernel_size=3,
layers=30,
stacks=3,
residual_channels=64,
gate_channels=128,
skip_channels=64,
aux_channels=80,
aux_context_window=2,
dropout=0.0,
bias=True,
use_weight_norm=True,
use_causal_conv=False,
upsample_conditional_features=True,
upsample_net="ConvInUpsampleNetwork",
upsample_params={"upsample_scales": [4, 4, 4, 4]},
use_pitch_embed=False,
):
"""Initialize Parallel WaveGAN Generator module.
Args:
in_channels (int): Number of input channels.
out_channels (int): Number of output channels.
kernel_size (int): Kernel size of dilated convolution.
layers (int): Number of residual block layers.
stacks (int): Number of stacks i.e., dilation cycles.
residual_channels (int): Number of channels in residual conv.
gate_channels (int): Number of channels in gated conv.
skip_channels (int): Number of channels in skip conv.
aux_channels (int): Number of channels for auxiliary feature conv.
aux_context_window (int): Context window size for auxiliary feature.
dropout (float): Dropout rate. 0.0 means no dropout applied.
bias (bool): Whether to use bias parameter in conv layer.
use_weight_norm (bool): Whether to use weight norm.
If set to true, it will be applied to all of the conv layers.
use_causal_conv (bool): Whether to use causal structure.
upsample_conditional_features (bool): Whether to use upsampling network.
upsample_net (str): Upsampling network architecture.
upsample_params (dict): Upsampling network parameters.
"""
super(ParallelWaveGANGenerator, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.aux_channels = aux_channels
self.layers = layers
self.stacks = stacks
self.kernel_size = kernel_size
# check the number of layers and stacks
assert layers % stacks == 0
layers_per_stack = layers // stacks
# define first convolution
self.first_conv = Conv1d1x1(in_channels, residual_channels, bias=True)
# define conv + upsampling network
if upsample_conditional_features:
upsample_params.update({
"use_causal_conv": use_causal_conv,
})
if upsample_net == "MelGANGenerator":
assert aux_context_window == 0
upsample_params.update({
"use_weight_norm": False, # not to apply twice
"use_final_nonlinear_activation": False,
})
self.upsample_net = getattr(models, upsample_net)(**upsample_params)
else:
if upsample_net == "ConvInUpsampleNetwork":
upsample_params.update({
"aux_channels": aux_channels,
"aux_context_window": aux_context_window,
})
self.upsample_net = getattr(upsample, upsample_net)(**upsample_params)
else:
self.upsample_net = None
# define residual blocks
self.conv_layers = torch.nn.ModuleList()
for layer in range(layers):
dilation = 2 ** (layer % layers_per_stack)
conv = ResidualBlock(
kernel_size=kernel_size,
residual_channels=residual_channels,
gate_channels=gate_channels,
skip_channels=skip_channels,
aux_channels=aux_channels,
dilation=dilation,
dropout=dropout,
bias=bias,
use_causal_conv=use_causal_conv,
)
self.conv_layers += [conv]
# define output layers
self.last_conv_layers = torch.nn.ModuleList([
torch.nn.ReLU(inplace=True),
Conv1d1x1(skip_channels, skip_channels, bias=True),
torch.nn.ReLU(inplace=True),
Conv1d1x1(skip_channels, out_channels, bias=True),
])
self.use_pitch_embed = use_pitch_embed
if use_pitch_embed:
self.pitch_embed = nn.Embedding(300, aux_channels, 0)
self.c_proj = nn.Linear(2 * aux_channels, aux_channels)
# apply weight norm
if use_weight_norm:
self.apply_weight_norm()
def forward(self, x, c=None, pitch=None, **kwargs):
"""Calculate forward propagation.
Args:
x (Tensor): Input noise signal (B, C_in, T).
c (Tensor): Local conditioning auxiliary features (B, C ,T').
pitch (Tensor): Local conditioning pitch (B, T').
Returns:
Tensor: Output tensor (B, C_out, T)
"""
# perform upsampling
if c is not None and self.upsample_net is not None:
if self.use_pitch_embed:
p = self.pitch_embed(pitch)
c = self.c_proj(torch.cat([c.transpose(1, 2), p], -1)).transpose(1, 2)
c = self.upsample_net(c)
assert c.size(-1) == x.size(-1), (c.size(-1), x.size(-1))
# encode to hidden representation
x = self.first_conv(x)
skips = 0
for f in self.conv_layers:
x, h = f(x, c)
skips += h
skips *= math.sqrt(1.0 / len(self.conv_layers))
# apply final layers
x = skips
for f in self.last_conv_layers:
x = f(x)
return x
def remove_weight_norm(self):
"""Remove weight normalization module from all of the layers."""
def _remove_weight_norm(m):
try:
logging.debug(f"Weight norm is removed from {m}.")
torch.nn.utils.remove_weight_norm(m)
except ValueError: # this module didn't have weight norm
return
self.apply(_remove_weight_norm)
def apply_weight_norm(self):
"""Apply weight normalization module from all of the layers."""
def _apply_weight_norm(m):
if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.Conv2d):
torch.nn.utils.weight_norm(m)
logging.debug(f"Weight norm is applied to {m}.")
self.apply(_apply_weight_norm)
@staticmethod
def _get_receptive_field_size(layers, stacks, kernel_size,
dilation=lambda x: 2 ** x):
assert layers % stacks == 0
layers_per_cycle = layers // stacks
dilations = [dilation(i % layers_per_cycle) for i in range(layers)]
return (kernel_size - 1) * sum(dilations) + 1
@property
def receptive_field_size(self):
"""Return receptive field size."""
return self._get_receptive_field_size(self.layers, self.stacks, self.kernel_size)
class ParallelWaveGANDiscriminator(torch.nn.Module):
"""Parallel WaveGAN Discriminator module."""
def __init__(self,
in_channels=1,
out_channels=1,
kernel_size=3,
layers=10,
conv_channels=64,
dilation_factor=1,
nonlinear_activation="LeakyReLU",
nonlinear_activation_params={"negative_slope": 0.2},
bias=True,
use_weight_norm=True,
):
"""Initialize Parallel WaveGAN Discriminator module.
Args:
in_channels (int): Number of input channels.
out_channels (int): Number of output channels.
kernel_size (int): Number of output channels.
layers (int): Number of conv layers.
conv_channels (int): Number of chnn layers.
dilation_factor (int): Dilation factor. For example, if dilation_factor = 2,
the dilation will be 2, 4, 8, ..., and so on.
nonlinear_activation (str): Nonlinear function after each conv.
nonlinear_activation_params (dict): Nonlinear function parameters
bias (bool): Whether to use bias parameter in conv.
use_weight_norm (bool) Whether to use weight norm.
If set to true, it will be applied to all of the conv layers.
"""
super(ParallelWaveGANDiscriminator, self).__init__()
assert (kernel_size - 1) % 2 == 0, "Not support even number kernel size."
assert dilation_factor > 0, "Dilation factor must be > 0."
self.conv_layers = torch.nn.ModuleList()
conv_in_channels = in_channels
for i in range(layers - 1):
if i == 0:
dilation = 1
else:
dilation = i if dilation_factor == 1 else dilation_factor ** i
conv_in_channels = conv_channels
padding = (kernel_size - 1) // 2 * dilation
conv_layer = [
Conv1d(conv_in_channels, conv_channels,
kernel_size=kernel_size, padding=padding,
dilation=dilation, bias=bias),
getattr(torch.nn, nonlinear_activation)(inplace=True, **nonlinear_activation_params)
]
self.conv_layers += conv_layer
padding = (kernel_size - 1) // 2
last_conv_layer = Conv1d(
conv_in_channels, out_channels,
kernel_size=kernel_size, padding=padding, bias=bias)
self.conv_layers += [last_conv_layer]
# apply weight norm
if use_weight_norm:
self.apply_weight_norm()
def forward(self, x):
"""Calculate forward propagation.
Args:
x (Tensor): Input noise signal (B, 1, T).
Returns:
Tensor: Output tensor (B, 1, T)
"""
for f in self.conv_layers:
x = f(x)
return x
def apply_weight_norm(self):
"""Apply weight normalization module from all of the layers."""
def _apply_weight_norm(m):
if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.Conv2d):
torch.nn.utils.weight_norm(m)
logging.debug(f"Weight norm is applied to {m}.")
self.apply(_apply_weight_norm)
def remove_weight_norm(self):
"""Remove weight normalization module from all of the layers."""
def _remove_weight_norm(m):
try:
logging.debug(f"Weight norm is removed from {m}.")
torch.nn.utils.remove_weight_norm(m)
except ValueError: # this module didn't have weight norm
return
self.apply(_remove_weight_norm)
class ResidualParallelWaveGANDiscriminator(torch.nn.Module):
"""Parallel WaveGAN Discriminator module."""
def __init__(self,
in_channels=1,
out_channels=1,
kernel_size=3,
layers=30,
stacks=3,
residual_channels=64,
gate_channels=128,
skip_channels=64,
dropout=0.0,
bias=True,
use_weight_norm=True,
use_causal_conv=False,
nonlinear_activation="LeakyReLU",
nonlinear_activation_params={"negative_slope": 0.2},
):
"""Initialize Parallel WaveGAN Discriminator module.
Args:
in_channels (int): Number of input channels.
out_channels (int): Number of output channels.
kernel_size (int): Kernel size of dilated convolution.
layers (int): Number of residual block layers.
stacks (int): Number of stacks i.e., dilation cycles.
residual_channels (int): Number of channels in residual conv.
gate_channels (int): Number of channels in gated conv.
skip_channels (int): Number of channels in skip conv.
dropout (float): Dropout rate. 0.0 means no dropout applied.
bias (bool): Whether to use bias parameter in conv.
use_weight_norm (bool): Whether to use weight norm.
If set to true, it will be applied to all of the conv layers.
use_causal_conv (bool): Whether to use causal structure.
nonlinear_activation_params (dict): Nonlinear function parameters
"""
super(ResidualParallelWaveGANDiscriminator, self).__init__()
assert (kernel_size - 1) % 2 == 0, "Not support even number kernel size."
self.in_channels = in_channels
self.out_channels = out_channels
self.layers = layers
self.stacks = stacks
self.kernel_size = kernel_size
# check the number of layers and stacks
assert layers % stacks == 0
layers_per_stack = layers // stacks
# define first convolution
self.first_conv = torch.nn.Sequential(
Conv1d1x1(in_channels, residual_channels, bias=True),
getattr(torch.nn, nonlinear_activation)(
inplace=True, **nonlinear_activation_params),
)
# define residual blocks
self.conv_layers = torch.nn.ModuleList()
for layer in range(layers):
dilation = 2 ** (layer % layers_per_stack)
conv = ResidualBlock(
kernel_size=kernel_size,
residual_channels=residual_channels,
gate_channels=gate_channels,
skip_channels=skip_channels,
aux_channels=-1,
dilation=dilation,
dropout=dropout,
bias=bias,
use_causal_conv=use_causal_conv,
)
self.conv_layers += [conv]
# define output layers
self.last_conv_layers = torch.nn.ModuleList([
getattr(torch.nn, nonlinear_activation)(
inplace=True, **nonlinear_activation_params),
Conv1d1x1(skip_channels, skip_channels, bias=True),
getattr(torch.nn, nonlinear_activation)(
inplace=True, **nonlinear_activation_params),
Conv1d1x1(skip_channels, out_channels, bias=True),
])
# apply weight norm
if use_weight_norm:
self.apply_weight_norm()
def forward(self, x):
"""Calculate forward propagation.
Args:
x (Tensor): Input noise signal (B, 1, T).
Returns:
Tensor: Output tensor (B, 1, T)
"""
x = self.first_conv(x)
skips = 0
for f in self.conv_layers:
x, h = f(x, None)
skips += h
skips *= math.sqrt(1.0 / len(self.conv_layers))
# apply final layers
x = skips
for f in self.last_conv_layers:
x = f(x)
return x
def apply_weight_norm(self):
"""Apply weight normalization module from all of the layers."""
def _apply_weight_norm(m):
if isinstance(m, torch.nn.Conv1d) or isinstance(m, torch.nn.Conv2d):
torch.nn.utils.weight_norm(m)
logging.debug(f"Weight norm is applied to {m}.")
self.apply(_apply_weight_norm)
def remove_weight_norm(self):
"""Remove weight normalization module from all of the layers."""
def _remove_weight_norm(m):
try:
logging.debug(f"Weight norm is removed from {m}.")
torch.nn.utils.remove_weight_norm(m)
except ValueError: # this module didn't have weight norm
return
self.apply(_remove_weight_norm)
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