Spaces:
Configuration error
Configuration error
File size: 9,462 Bytes
cfdc687 |
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 |
# -*- coding: utf-8 -*-
# Copyright 2020 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""MelGAN Modules."""
import logging
import numpy as np
import torch
from modules.base import BaseModule
class MelGANDiscriminator(BaseModule):
"""MelGAN discriminator module."""
def __init__(
self,
in_channels=1,
out_channels=1,
kernel_sizes=[5, 3],
channels=16,
max_downsample_channels=1024,
bias=True,
downsample_scales=[4, 4, 4, 4],
nonlinear_activation="LeakyReLU",
nonlinear_activation_params={"negative_slope": 0.2},
pad="ReflectionPad1d",
pad_params={},
):
"""Initilize MelGAN discriminator module.
Args:
in_channels (int): Number of input channels.
out_channels (int): Number of output channels.
kernel_sizes (list): List of two kernel sizes. The prod will be used for the first conv layer,
and the first and the second kernel sizes will be used for the last two layers.
For example if kernel_sizes = [5, 3], the first layer kernel size will be 5 * 3 = 15,
the last two layers' kernel size will be 5 and 3, respectively.
channels (int): Initial number of channels for conv layer.
max_downsample_channels (int): Maximum number of channels for downsampling layers.
bias (bool): Whether to add bias parameter in convolution layers.
downsample_scales (list): List of downsampling scales.
nonlinear_activation (str): Activation function module name.
nonlinear_activation_params (dict): Hyperparameters for activation function.
pad (str): Padding function module name before dilated convolution layer.
pad_params (dict): Hyperparameters for padding function.
"""
super(MelGANDiscriminator, self).__init__()
self.layers = torch.nn.ModuleList()
# check kernel size is valid
assert len(kernel_sizes) == 2
assert kernel_sizes[0] % 2 == 1
assert kernel_sizes[1] % 2 == 1
# add first layer
self.layers += [
torch.nn.Sequential(
getattr(torch.nn, pad)((np.prod(kernel_sizes) - 1) // 2, **pad_params),
torch.nn.Conv1d(
in_channels, channels, np.prod(kernel_sizes), bias=bias
),
getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params),
)
]
# add downsample layers
in_chs = channels
for downsample_scale in downsample_scales:
out_chs = min(in_chs * downsample_scale, max_downsample_channels)
self.layers += [
torch.nn.Sequential(
torch.nn.Conv1d(
in_chs,
out_chs,
kernel_size=downsample_scale * 10 + 1,
stride=downsample_scale,
padding=downsample_scale * 5,
groups=in_chs // 4,
bias=bias,
),
getattr(torch.nn, nonlinear_activation)(
**nonlinear_activation_params
),
)
]
in_chs = out_chs
# add final layers
out_chs = min(in_chs * 2, max_downsample_channels)
self.layers += [
torch.nn.Sequential(
torch.nn.Conv1d(
in_chs,
out_chs,
kernel_sizes[0],
padding=(kernel_sizes[0] - 1) // 2,
bias=bias,
),
getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params),
)
]
self.layers += [
torch.nn.Conv1d(
out_chs,
out_channels,
kernel_sizes[1],
padding=(kernel_sizes[1] - 1) // 2,
bias=bias,
),
]
def forward(self, x):
"""Calculate forward propagation.
Args:
x (Tensor): Input noise signal (B, 1, T).
Returns:
List: List of output tensors of each layer.
"""
outs = []
for f in self.layers:
x = f(x)
outs += [x]
return outs
class MelGANMultiScaleDiscriminator(BaseModule):
"""MelGAN multi-scale discriminator module."""
def __init__(
self,
in_channels=1,
out_channels=1,
scales=3,
downsample_pooling="AvgPool1d",
# follow the official implementation setting
downsample_pooling_params={
"kernel_size": 4,
"stride": 2,
"padding": 1,
"count_include_pad": False,
},
kernel_sizes=[5, 3],
channels=16,
max_downsample_channels=1024,
bias=True,
downsample_scales=[4, 4, 4, 4],
nonlinear_activation="LeakyReLU",
nonlinear_activation_params={"negative_slope": 0.2},
pad="ReflectionPad1d",
pad_params={},
use_weight_norm=True,
):
"""Initilize MelGAN multi-scale discriminator module.
Args:
in_channels (int): Number of input channels.
out_channels (int): Number of output channels.
scales (int): Number of multi-scales.
downsample_pooling (str): Pooling module name for downsampling of the inputs.
downsample_pooling_params (dict): Parameters for the above pooling module.
kernel_sizes (list): List of two kernel sizes. The sum will be used for the first conv layer,
and the first and the second kernel sizes will be used for the last two layers.
channels (int): Initial number of channels for conv layer.
max_downsample_channels (int): Maximum number of channels for downsampling layers.
bias (bool): Whether to add bias parameter in convolution layers.
downsample_scales (list): List of downsampling scales.
nonlinear_activation (str): Activation function module name.
nonlinear_activation_params (dict): Hyperparameters for activation function.
pad (str): Padding function module name before dilated convolution layer.
pad_params (dict): Hyperparameters for padding function.
use_causal_conv (bool): Whether to use causal convolution.
"""
super(MelGANMultiScaleDiscriminator, self).__init__()
self.discriminators = torch.nn.ModuleList()
# add discriminators
for _ in range(scales):
self.discriminators += [
MelGANDiscriminator(
in_channels=in_channels,
out_channels=out_channels,
kernel_sizes=kernel_sizes,
channels=channels,
max_downsample_channels=max_downsample_channels,
bias=bias,
downsample_scales=downsample_scales,
nonlinear_activation=nonlinear_activation,
nonlinear_activation_params=nonlinear_activation_params,
pad=pad,
pad_params=pad_params,
)
]
self.pooling = getattr(torch.nn, downsample_pooling)(
**downsample_pooling_params
)
# apply weight norm
if use_weight_norm:
self.apply_weight_norm()
# reset parameters
self.reset_parameters()
def forward(self, x):
"""Calculate forward propagation.
Args:
x (Tensor): Input noise signal (B, 1, T).
Returns:
List: List of list of each discriminator outputs, which consists of each layer output tensors.
"""
outs = []
for f in self.discriminators:
outs += [f(x)]
x = self.pooling(x)
return outs
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.ConvTranspose1d
):
torch.nn.utils.weight_norm(m)
logging.debug(f"Weight norm is applied to {m}.")
self.apply(_apply_weight_norm)
def reset_parameters(self):
"""Reset parameters.
This initialization follows official implementation manner.
https://github.com/descriptinc/melgan-neurips/blob/master/mel2wav/modules.py
"""
def _reset_parameters(m):
if isinstance(m, torch.nn.Conv1d) or isinstance(
m, torch.nn.ConvTranspose1d
):
m.weight.data.normal_(0.0, 0.02)
logging.debug(f"Reset parameters in {m}.")
self.apply(_reset_parameters)
|