File size: 7,513 Bytes
54c22e4 |
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 |
import torch
import torch.nn as nn
from functools import partial
class STFT:
def __init__(self, n_fft, hop_length, dim_f, device):
self.n_fft = n_fft
self.hop_length = hop_length
self.window = torch.hann_window(window_length=self.n_fft, periodic=True)
self.dim_f = dim_f
self.device = device
def __call__(self, x):
x_is_mps = not x.device.type in ["cuda", "cpu"]
if x_is_mps:
x = x.cpu()
window = self.window.to(x.device)
batch_dims = x.shape[:-2]
c, t = x.shape[-2:]
x = x.reshape([-1, t])
x = torch.stft(x, n_fft=self.n_fft, hop_length=self.hop_length, window=window, center=True,return_complex=False)
x = x.permute([0, 3, 1, 2])
x = x.reshape([*batch_dims, c, 2, -1, x.shape[-1]]).reshape([*batch_dims, c * 2, -1, x.shape[-1]])
if x_is_mps:
x = x.to(self.device)
return x[..., :self.dim_f, :]
def inverse(self, x):
x_is_mps = not x.device.type in ["cuda", "cpu"]
if x_is_mps:
x = x.cpu()
window = self.window.to(x.device)
batch_dims = x.shape[:-3]
c, f, t = x.shape[-3:]
n = self.n_fft // 2 + 1
f_pad = torch.zeros([*batch_dims, c, n - f, t]).to(x.device)
x = torch.cat([x, f_pad], -2)
x = x.reshape([*batch_dims, c // 2, 2, n, t]).reshape([-1, 2, n, t])
x = x.permute([0, 2, 3, 1])
x = x[..., 0] + x[..., 1] * 1.j
x = torch.istft(x, n_fft=self.n_fft, hop_length=self.hop_length, window=window, center=True)
x = x.reshape([*batch_dims, 2, -1])
if x_is_mps:
x = x.to(self.device)
return x
def get_norm(norm_type):
def norm(c, norm_type):
if norm_type == 'BatchNorm':
return nn.BatchNorm2d(c)
elif norm_type == 'InstanceNorm':
return nn.InstanceNorm2d(c, affine=True)
elif 'GroupNorm' in norm_type:
g = int(norm_type.replace('GroupNorm', ''))
return nn.GroupNorm(num_groups=g, num_channels=c)
else:
return nn.Identity()
return partial(norm, norm_type=norm_type)
def get_act(act_type):
if act_type == 'gelu':
return nn.GELU()
elif act_type == 'relu':
return nn.ReLU()
elif act_type[:3] == 'elu':
alpha = float(act_type.replace('elu', ''))
return nn.ELU(alpha)
else:
raise Exception
class Upscale(nn.Module):
def __init__(self, in_c, out_c, scale, norm, act):
super().__init__()
self.conv = nn.Sequential(
norm(in_c),
act,
nn.ConvTranspose2d(in_channels=in_c, out_channels=out_c, kernel_size=scale, stride=scale, bias=False)
)
def forward(self, x):
return self.conv(x)
class Downscale(nn.Module):
def __init__(self, in_c, out_c, scale, norm, act):
super().__init__()
self.conv = nn.Sequential(
norm(in_c),
act,
nn.Conv2d(in_channels=in_c, out_channels=out_c, kernel_size=scale, stride=scale, bias=False)
)
def forward(self, x):
return self.conv(x)
class TFC_TDF(nn.Module):
def __init__(self, in_c, c, l, f, bn, norm, act):
super().__init__()
self.blocks = nn.ModuleList()
for i in range(l):
block = nn.Module()
block.tfc1 = nn.Sequential(
norm(in_c),
act,
nn.Conv2d(in_c, c, 3, 1, 1, bias=False),
)
block.tdf = nn.Sequential(
norm(c),
act,
nn.Linear(f, f // bn, bias=False),
norm(c),
act,
nn.Linear(f // bn, f, bias=False),
)
block.tfc2 = nn.Sequential(
norm(c),
act,
nn.Conv2d(c, c, 3, 1, 1, bias=False),
)
block.shortcut = nn.Conv2d(in_c, c, 1, 1, 0, bias=False)
self.blocks.append(block)
in_c = c
def forward(self, x):
for block in self.blocks:
s = block.shortcut(x)
x = block.tfc1(x)
x = x + block.tdf(x)
x = block.tfc2(x)
x = x + s
return x
class TFC_TDF_net(nn.Module):
def __init__(self, config, device):
super().__init__()
self.config = config
self.device = device
norm = get_norm(norm_type=config.model.norm)
act = get_act(act_type=config.model.act)
self.num_target_instruments = 1 if config.training.target_instrument else len(config.training.instruments)
self.num_subbands = config.model.num_subbands
dim_c = self.num_subbands * config.audio.num_channels * 2
n = config.model.num_scales
scale = config.model.scale
l = config.model.num_blocks_per_scale
c = config.model.num_channels
g = config.model.growth
bn = config.model.bottleneck_factor
f = config.audio.dim_f // self.num_subbands
self.first_conv = nn.Conv2d(dim_c, c, 1, 1, 0, bias=False)
self.encoder_blocks = nn.ModuleList()
for i in range(n):
block = nn.Module()
block.tfc_tdf = TFC_TDF(c, c, l, f, bn, norm, act)
block.downscale = Downscale(c, c + g, scale, norm, act)
f = f // scale[1]
c += g
self.encoder_blocks.append(block)
self.bottleneck_block = TFC_TDF(c, c, l, f, bn, norm, act)
self.decoder_blocks = nn.ModuleList()
for i in range(n):
block = nn.Module()
block.upscale = Upscale(c, c - g, scale, norm, act)
f = f * scale[1]
c -= g
block.tfc_tdf = TFC_TDF(2 * c, c, l, f, bn, norm, act)
self.decoder_blocks.append(block)
self.final_conv = nn.Sequential(
nn.Conv2d(c + dim_c, c, 1, 1, 0, bias=False),
act,
nn.Conv2d(c, self.num_target_instruments * dim_c, 1, 1, 0, bias=False)
)
self.stft = STFT(config.audio.n_fft, config.audio.hop_length, config.audio.dim_f, self.device)
def cac2cws(self, x):
k = self.num_subbands
b, c, f, t = x.shape
x = x.reshape(b, c, k, f // k, t)
x = x.reshape(b, c * k, f // k, t)
return x
def cws2cac(self, x):
k = self.num_subbands
b, c, f, t = x.shape
x = x.reshape(b, c // k, k, f, t)
x = x.reshape(b, c // k, f * k, t)
return x
def forward(self, x):
x = self.stft(x)
mix = x = self.cac2cws(x)
first_conv_out = x = self.first_conv(x)
x = x.transpose(-1, -2)
encoder_outputs = []
for block in self.encoder_blocks:
x = block.tfc_tdf(x)
encoder_outputs.append(x)
x = block.downscale(x)
x = self.bottleneck_block(x)
for block in self.decoder_blocks:
x = block.upscale(x)
x = torch.cat([x, encoder_outputs.pop()], 1)
x = block.tfc_tdf(x)
x = x.transpose(-1, -2)
x = x * first_conv_out # reduce artifacts
x = self.final_conv(torch.cat([mix, x], 1))
x = self.cws2cac(x)
if self.num_target_instruments > 1:
b, c, f, t = x.shape
x = x.reshape(b, self.num_target_instruments, -1, f, t)
x = self.stft.inverse(x)
return x
|