Spaces:
Runtime error
Runtime error
File size: 8,360 Bytes
9b2107c |
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 |
import torch
from packaging.version import Version
from torch import nn
from torch.nn import functional as F
from TTS.tts.layers.generic.wavenet import WN
from ..generic.normalization import LayerNorm
class ResidualConv1dLayerNormBlock(nn.Module):
"""Conv1d with Layer Normalization and residual connection as in GlowTTS paper.
https://arxiv.org/pdf/1811.00002.pdf
::
x |-> conv1d -> layer_norm -> relu -> dropout -> + -> o
|---------------> conv1d_1x1 ------------------|
Args:
in_channels (int): number of input tensor channels.
hidden_channels (int): number of inner layer channels.
out_channels (int): number of output tensor channels.
kernel_size (int): kernel size of conv1d filter.
num_layers (int): number of blocks.
dropout_p (float): dropout rate for each block.
"""
def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, num_layers, dropout_p):
super().__init__()
self.in_channels = in_channels
self.hidden_channels = hidden_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.num_layers = num_layers
self.dropout_p = dropout_p
assert num_layers > 1, " [!] number of layers should be > 0."
assert kernel_size % 2 == 1, " [!] kernel size should be odd number."
self.conv_layers = nn.ModuleList()
self.norm_layers = nn.ModuleList()
for idx in range(num_layers):
self.conv_layers.append(
nn.Conv1d(
in_channels if idx == 0 else hidden_channels, hidden_channels, kernel_size, padding=kernel_size // 2
)
)
self.norm_layers.append(LayerNorm(hidden_channels))
self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
self.proj.weight.data.zero_()
self.proj.bias.data.zero_()
def forward(self, x, x_mask):
"""
Shapes:
- x: :math:`[B, C, T]`
- x_mask: :math:`[B, 1, T]`
"""
x_res = x
for i in range(self.num_layers):
x = self.conv_layers[i](x * x_mask)
x = self.norm_layers[i](x * x_mask)
x = F.dropout(F.relu(x), self.dropout_p, training=self.training)
x = x_res + self.proj(x)
return x * x_mask
class InvConvNear(nn.Module):
"""Invertible Convolution with input splitting as in GlowTTS paper.
https://arxiv.org/pdf/1811.00002.pdf
Args:
channels (int): input and output channels.
num_splits (int): number of splits, also H and W of conv layer.
no_jacobian (bool): enable/disable jacobian computations.
Note:
Split the input into groups of size self.num_splits and
perform 1x1 convolution separately. Cast 1x1 conv operation
to 2d by reshaping the input for efficiency.
"""
def __init__(self, channels, num_splits=4, no_jacobian=False, **kwargs): # pylint: disable=unused-argument
super().__init__()
assert num_splits % 2 == 0
self.channels = channels
self.num_splits = num_splits
self.no_jacobian = no_jacobian
self.weight_inv = None
if Version(torch.__version__) < Version("1.9"):
w_init = torch.qr(torch.FloatTensor(self.num_splits, self.num_splits).normal_())[0]
else:
w_init = torch.linalg.qr(torch.FloatTensor(self.num_splits, self.num_splits).normal_(), "complete")[0]
if torch.det(w_init) < 0:
w_init[:, 0] = -1 * w_init[:, 0]
self.weight = nn.Parameter(w_init)
def forward(self, x, x_mask=None, reverse=False, **kwargs): # pylint: disable=unused-argument
"""
Shapes:
- x: :math:`[B, C, T]`
- x_mask: :math:`[B, 1, T]`
"""
b, c, t = x.size()
assert c % self.num_splits == 0
if x_mask is None:
x_mask = 1
x_len = torch.ones((b,), dtype=x.dtype, device=x.device) * t
else:
x_len = torch.sum(x_mask, [1, 2])
x = x.view(b, 2, c // self.num_splits, self.num_splits // 2, t)
x = x.permute(0, 1, 3, 2, 4).contiguous().view(b, self.num_splits, c // self.num_splits, t)
if reverse:
if self.weight_inv is not None:
weight = self.weight_inv
else:
weight = torch.inverse(self.weight.float()).to(dtype=self.weight.dtype)
logdet = None
else:
weight = self.weight
if self.no_jacobian:
logdet = 0
else:
logdet = torch.logdet(self.weight) * (c / self.num_splits) * x_len # [b]
weight = weight.view(self.num_splits, self.num_splits, 1, 1)
z = F.conv2d(x, weight)
z = z.view(b, 2, self.num_splits // 2, c // self.num_splits, t)
z = z.permute(0, 1, 3, 2, 4).contiguous().view(b, c, t) * x_mask
return z, logdet
def store_inverse(self):
weight_inv = torch.inverse(self.weight.float()).to(dtype=self.weight.dtype)
self.weight_inv = nn.Parameter(weight_inv, requires_grad=False)
class CouplingBlock(nn.Module):
"""Glow Affine Coupling block as in GlowTTS paper.
https://arxiv.org/pdf/1811.00002.pdf
::
x --> x0 -> conv1d -> wavenet -> conv1d --> t, s -> concat(s*x1 + t, x0) -> o
'-> x1 - - - - - - - - - - - - - - - - - - - - - - - - - ^
Args:
in_channels (int): number of input tensor channels.
hidden_channels (int): number of hidden channels.
kernel_size (int): WaveNet filter kernel size.
dilation_rate (int): rate to increase dilation by each layer in a decoder block.
num_layers (int): number of WaveNet layers.
c_in_channels (int): number of conditioning input channels.
dropout_p (int): wavenet dropout rate.
sigmoid_scale (bool): enable/disable sigmoid scaling for output scale.
Note:
It does not use the conditional inputs differently from WaveGlow.
"""
def __init__(
self,
in_channels,
hidden_channels,
kernel_size,
dilation_rate,
num_layers,
c_in_channels=0,
dropout_p=0,
sigmoid_scale=False,
):
super().__init__()
self.in_channels = in_channels
self.hidden_channels = hidden_channels
self.kernel_size = kernel_size
self.dilation_rate = dilation_rate
self.num_layers = num_layers
self.c_in_channels = c_in_channels
self.dropout_p = dropout_p
self.sigmoid_scale = sigmoid_scale
# input layer
start = torch.nn.Conv1d(in_channels // 2, hidden_channels, 1)
start = torch.nn.utils.parametrizations.weight_norm(start)
self.start = start
# output layer
# Initializing last layer to 0 makes the affine coupling layers
# do nothing at first. This helps with training stability
end = torch.nn.Conv1d(hidden_channels, in_channels, 1)
end.weight.data.zero_()
end.bias.data.zero_()
self.end = end
# coupling layers
self.wn = WN(hidden_channels, hidden_channels, kernel_size, dilation_rate, num_layers, c_in_channels, dropout_p)
def forward(self, x, x_mask=None, reverse=False, g=None, **kwargs): # pylint: disable=unused-argument
"""
Shapes:
- x: :math:`[B, C, T]`
- x_mask: :math:`[B, 1, T]`
- g: :math:`[B, C, 1]`
"""
if x_mask is None:
x_mask = 1
x_0, x_1 = x[:, : self.in_channels // 2], x[:, self.in_channels // 2 :]
x = self.start(x_0) * x_mask
x = self.wn(x, x_mask, g)
out = self.end(x)
z_0 = x_0
t = out[:, : self.in_channels // 2, :]
s = out[:, self.in_channels // 2 :, :]
if self.sigmoid_scale:
s = torch.log(1e-6 + torch.sigmoid(s + 2))
if reverse:
z_1 = (x_1 - t) * torch.exp(-s) * x_mask
logdet = None
else:
z_1 = (t + torch.exp(s) * x_1) * x_mask
logdet = torch.sum(s * x_mask, [1, 2])
z = torch.cat([z_0, z_1], 1)
return z, logdet
def store_inverse(self):
self.wn.remove_weight_norm()
|