Spaces:
Configuration error
Configuration error
File size: 16,475 Bytes
ed1cdd1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 |
# -*- 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)
|