Spaces:
Running
on
Zero
Running
on
Zero
File size: 4,399 Bytes
66982e9 |
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 |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Conv1d, ConvTranspose1d
from torch.nn.utils.parametrizations import weight_norm
from torch.nn.utils.parametrize import remove_parametrizations
LRELU_SLOPE = 0.1
def init_weights(m, mean=0.0, std=0.01):
classname = m.__class__.__name__
if classname.find("Conv") != -1:
m.weight.data.normal_(mean, std)
def get_padding(kernel_size, dilation=1):
return int((kernel_size * dilation - dilation) / 2)
class ResBlock(torch.nn.Module):
def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
super(ResBlock, self).__init__()
self.h = h
self.convs1 = nn.ModuleList([
weight_norm(Conv1d(
channels, channels, kernel_size, 1, dilation=dilation[0],
padding=get_padding(kernel_size, dilation[0]),
)),
weight_norm(Conv1d(
channels, channels, kernel_size, 1, dilation=dilation[1],
padding=get_padding(kernel_size, dilation[1]),
)),
weight_norm(Conv1d(
channels, channels, kernel_size, 1, dilation=dilation[2],
padding=get_padding(kernel_size, dilation[2]),
)),
])
self.convs1.apply(init_weights)
self.convs2 = nn.ModuleList([
weight_norm(Conv1d(
channels, channels, kernel_size, 1, dilation=1,
padding=get_padding(kernel_size, 1),
)),
weight_norm(Conv1d(
channels, channels, kernel_size, 1, dilation=1,
padding=get_padding(kernel_size, 1),
)),
weight_norm(Conv1d(
channels, channels, kernel_size, 1, dilation=1,
padding=get_padding(kernel_size, 1),
)),
])
self.convs2.apply(init_weights)
def forward(self, x):
for c1, c2 in zip(self.convs1, self.convs2):
xt = F.leaky_relu(x, LRELU_SLOPE)
xt = c1(xt)
xt = F.leaky_relu(xt, LRELU_SLOPE)
xt = c2(xt)
x = xt + x
return x
def remove_weight_norm(self):
for l in self.convs1:
remove_parametrizations(l, 'weight')
for l in self.convs2:
remove_parametrizations(l, 'weight')
class Generator(torch.nn.Module):
def __init__(self, h):
super(Generator, self).__init__()
self.h = h
self.num_kernels = len(h.resblock_kernel_sizes)
self.num_upsamples = len(h.upsample_rates)
self.conv_pre = weight_norm(
Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3)
)
resblock = ResBlock
self.ups = nn.ModuleList()
for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
self.ups.append(weight_norm(ConvTranspose1d(
h.upsample_initial_channel // (2**i),
h.upsample_initial_channel // (2 ** (i + 1)),
k, u, padding=(k - u) // 2,
)))
self.resblocks = nn.ModuleList()
for i in range(len(self.ups)):
ch = h.upsample_initial_channel // (2 ** (i + 1))
for k, d in zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes):
self.resblocks.append(resblock(h, ch, k, d))
self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
self.ups.apply(init_weights)
self.conv_post.apply(init_weights)
def forward(self, x):
x = self.conv_pre(x)
for i in range(self.num_upsamples):
x = F.leaky_relu(x, LRELU_SLOPE)
x = self.ups[i](x)
xs = None
for j in range(self.num_kernels):
if xs is None:
xs = self.resblocks[i * self.num_kernels + j](x)
else:
xs += self.resblocks[i * self.num_kernels + j](x)
x = xs / self.num_kernels
x = F.leaky_relu(x)
x = self.conv_post(x)
x = torch.tanh(x)
return x
def remove_weight_norm(self):
for l in self.ups:
remove_parametrizations(l, 'weight')
for l in self.resblocks:
l.remove_weight_norm()
remove_parametrizations(self.conv_pre, 'weight')
remove_parametrizations(self.conv_post, 'weight')
|