Spaces:
Runtime error
Runtime error
File size: 10,175 Bytes
3a478bf |
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 |
from typing import Optional
import torch
from torch.nn.utils import remove_weight_norm
from torch.nn.utils.parametrizations import weight_norm
from rvc.lib.algorithm.modules import WaveNet
from rvc.lib.algorithm.commons import get_padding, init_weights
LRELU_SLOPE = 0.1
# Helper functions
def create_conv1d_layer(channels, kernel_size, dilation):
return weight_norm(
torch.nn.Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation,
padding=get_padding(kernel_size, dilation),
)
)
def apply_mask(tensor, mask):
return tensor * mask if mask is not None else tensor
class ResBlockBase(torch.nn.Module):
def __init__(self, channels, kernel_size, dilations):
super(ResBlockBase, self).__init__()
self.convs1 = torch.nn.ModuleList(
[create_conv1d_layer(channels, kernel_size, d) for d in dilations]
)
self.convs1.apply(init_weights)
self.convs2 = torch.nn.ModuleList(
[create_conv1d_layer(channels, kernel_size, 1) for _ in dilations]
)
self.convs2.apply(init_weights)
def forward(self, x, x_mask=None):
for c1, c2 in zip(self.convs1, self.convs2):
xt = torch.nn.functional.leaky_relu(x, LRELU_SLOPE)
xt = apply_mask(xt, x_mask)
xt = torch.nn.functional.leaky_relu(c1(xt), LRELU_SLOPE)
xt = apply_mask(xt, x_mask)
xt = c2(xt)
x = xt + x
return apply_mask(x, x_mask)
def remove_weight_norm(self):
for conv in self.convs1 + self.convs2:
remove_weight_norm(conv)
class ResBlock1(ResBlockBase):
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
super(ResBlock1, self).__init__(channels, kernel_size, dilation)
class ResBlock2(ResBlockBase):
def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
super(ResBlock2, self).__init__(channels, kernel_size, dilation)
class Log(torch.nn.Module):
"""Logarithm module for flow-based models.
This module computes the logarithm of the input and its log determinant.
During reverse, it computes the exponential of the input.
"""
def forward(self, x, x_mask, reverse=False, **kwargs):
"""Forward pass.
Args:
x (torch.Tensor): Input tensor.
x_mask (torch.Tensor): Mask tensor.
reverse (bool, optional): Whether to reverse the operation. Defaults to False.
"""
if not reverse:
y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
logdet = torch.sum(-y, [1, 2])
return y, logdet
else:
x = torch.exp(x) * x_mask
return x
class Flip(torch.nn.Module):
"""Flip module for flow-based models.
This module flips the input along the time dimension.
"""
def forward(self, x, *args, reverse=False, **kwargs):
"""Forward pass.
Args:
x (torch.Tensor): Input tensor.
reverse (bool, optional): Whether to reverse the operation. Defaults to False.
"""
x = torch.flip(x, [1])
if not reverse:
logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
return x, logdet
else:
return x
class ElementwiseAffine(torch.nn.Module):
"""Elementwise affine transformation module for flow-based models.
This module performs an elementwise affine transformation on the input.
Args:
channels (int): Number of channels.
"""
def __init__(self, channels):
super().__init__()
self.channels = channels
self.m = torch.nn.Parameter(torch.zeros(channels, 1))
self.logs = torch.nn.Parameter(torch.zeros(channels, 1))
def forward(self, x, x_mask, reverse=False, **kwargs):
"""Forward pass.
Args:
x (torch.Tensor): Input tensor.
x_mask (torch.Tensor): Mask tensor.
reverse (bool, optional): Whether to reverse the operation. Defaults to False.
"""
if not reverse:
y = self.m + torch.exp(self.logs) * x
y = y * x_mask
logdet = torch.sum(self.logs * x_mask, [1, 2])
return y, logdet
else:
x = (x - self.m) * torch.exp(-self.logs) * x_mask
return x
class ResidualCouplingBlock(torch.nn.Module):
"""Residual Coupling Block for normalizing flow.
Args:
channels (int): Number of channels in the input.
hidden_channels (int): Number of hidden channels in the coupling layer.
kernel_size (int): Kernel size of the convolutional layers.
dilation_rate (int): Dilation rate of the convolutional layers.
n_layers (int): Number of layers in the coupling layer.
n_flows (int, optional): Number of coupling layers in the block. Defaults to 4.
gin_channels (int, optional): Number of channels for the global conditioning input. Defaults to 0.
"""
def __init__(
self,
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
n_flows=4,
gin_channels=0,
):
super(ResidualCouplingBlock, self).__init__()
self.channels = channels
self.hidden_channels = hidden_channels
self.kernel_size = kernel_size
self.dilation_rate = dilation_rate
self.n_layers = n_layers
self.n_flows = n_flows
self.gin_channels = gin_channels
self.flows = torch.nn.ModuleList()
for i in range(n_flows):
self.flows.append(
ResidualCouplingLayer(
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=gin_channels,
mean_only=True,
)
)
self.flows.append(Flip())
def forward(
self,
x: torch.Tensor,
x_mask: torch.Tensor,
g: Optional[torch.Tensor] = None,
reverse: bool = False,
):
if not reverse:
for flow in self.flows:
x, _ = flow(x, x_mask, g=g, reverse=reverse)
else:
for flow in reversed(self.flows):
x = flow.forward(x, x_mask, g=g, reverse=reverse)
return x
def remove_weight_norm(self):
"""Removes weight normalization from the coupling layers."""
for i in range(self.n_flows):
self.flows[i * 2].remove_weight_norm()
def __prepare_scriptable__(self):
"""Prepares the module for scripting."""
for i in range(self.n_flows):
for hook in self.flows[i * 2]._forward_pre_hooks.values():
if (
hook.__module__ == "torch.nn.utils.parametrizations.weight_norm"
and hook.__class__.__name__ == "WeightNorm"
):
torch.nn.utils.remove_weight_norm(self.flows[i * 2])
return self
class ResidualCouplingLayer(torch.nn.Module):
"""Residual coupling layer for flow-based models.
Args:
channels (int): Number of channels.
hidden_channels (int): Number of hidden channels.
kernel_size (int): Size of the convolutional kernel.
dilation_rate (int): Dilation rate of the convolution.
n_layers (int): Number of convolutional layers.
p_dropout (float, optional): Dropout probability. Defaults to 0.
gin_channels (int, optional): Number of conditioning channels. Defaults to 0.
mean_only (bool, optional): Whether to use mean-only coupling. Defaults to False.
"""
def __init__(
self,
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
p_dropout=0,
gin_channels=0,
mean_only=False,
):
assert channels % 2 == 0, "channels should be divisible by 2"
super().__init__()
self.channels = channels
self.hidden_channels = hidden_channels
self.kernel_size = kernel_size
self.dilation_rate = dilation_rate
self.n_layers = n_layers
self.half_channels = channels // 2
self.mean_only = mean_only
self.pre = torch.nn.Conv1d(self.half_channels, hidden_channels, 1)
self.enc = WaveNet(
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
p_dropout=p_dropout,
gin_channels=gin_channels,
)
self.post = torch.nn.Conv1d(
hidden_channels, self.half_channels * (2 - mean_only), 1
)
self.post.weight.data.zero_()
self.post.bias.data.zero_()
def forward(self, x, x_mask, g=None, reverse=False):
"""Forward pass.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, channels, time_steps).
x_mask (torch.Tensor): Mask tensor of shape (batch_size, 1, time_steps).
g (torch.Tensor, optional): Conditioning tensor of shape (batch_size, gin_channels, time_steps).
Defaults to None.
reverse (bool, optional): Whether to reverse the operation. Defaults to False.
"""
x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
h = self.pre(x0) * x_mask
h = self.enc(h, x_mask, g=g)
stats = self.post(h) * x_mask
if not self.mean_only:
m, logs = torch.split(stats, [self.half_channels] * 2, 1)
else:
m = stats
logs = torch.zeros_like(m)
if not reverse:
x1 = m + x1 * torch.exp(logs) * x_mask
x = torch.cat([x0, x1], 1)
logdet = torch.sum(logs, [1, 2])
return x, logdet
else:
x1 = (x1 - m) * torch.exp(-logs) * x_mask
x = torch.cat([x0, x1], 1)
return x
def remove_weight_norm(self):
"""Remove weight normalization from the module."""
self.enc.remove_weight_norm()
|