File size: 15,685 Bytes
3978e51 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 |
# https://github.com/Human9000/nd-Mamba2-torch
from __future__ import print_function
import torch
import torch.nn as nn
import numpy as np
from torch.utils.checkpoint import checkpoint_sequential
try:
from mamba_ssm.modules.mamba2 import Mamba2
except Exception as e:
print('Exception during load Mamba2 modules: {}'.format(str(e)))
print('Load local torch implementation!')
from .ex_bi_mamba2 import Mamba2
class MambaBlock(nn.Module):
def __init__(self, in_channels):
super(MambaBlock, self).__init__()
self.forward_mamba2 = Mamba2(
d_model=in_channels,
d_state=128,
d_conv=4,
expand=4,
headdim=64,
)
self.backward_mamba2 = Mamba2(
d_model=in_channels,
d_state=128,
d_conv=4,
expand=4,
headdim=64,
)
def forward(self, input):
forward_f = input
forward_f_output = self.forward_mamba2(forward_f)
backward_f = torch.flip(input, [1])
backward_f_output = self.backward_mamba2(backward_f)
backward_f_output2 = torch.flip(backward_f_output, [1])
output = torch.cat([forward_f_output + input, backward_f_output2+input], -1)
return output
class TAC(nn.Module):
"""
A transform-average-concatenate (TAC) module.
"""
def __init__(self, input_size, hidden_size):
super(TAC, self).__init__()
self.input_size = input_size
self.eps = torch.finfo(torch.float32).eps
self.input_norm = nn.GroupNorm(1, input_size, self.eps)
self.TAC_input = nn.Sequential(nn.Linear(input_size, hidden_size),
nn.Tanh()
)
self.TAC_mean = nn.Sequential(nn.Linear(hidden_size, hidden_size),
nn.Tanh()
)
self.TAC_output = nn.Sequential(nn.Linear(hidden_size*2, input_size),
nn.Tanh()
)
def forward(self, input):
# input shape: batch, group, N, *
batch_size, G, N = input.shape[:3]
output = self.input_norm(input.view(batch_size*G, N, -1)).view(batch_size, G, N, -1)
T = output.shape[-1]
# transform
group_input = output # B, G, N, T
group_input = group_input.permute(0,3,1,2).contiguous().view(-1, N) # B*T*G, N
group_output = self.TAC_input(group_input).view(batch_size, T, G, -1) # B, T, G, H
# mean pooling
group_mean = group_output.mean(2).view(batch_size*T, -1) # B*T, H
group_mean = self.TAC_mean(group_mean).unsqueeze(1).expand(batch_size*T, G, group_mean.shape[-1]).contiguous() # B*T, G, H
# concate
group_output = group_output.view(batch_size*T, G, -1) # B*T, G, H
group_output = torch.cat([group_output, group_mean], 2) # B*T, G, 2H
group_output = self.TAC_output(group_output.view(-1, group_output.shape[-1])) # B*T*G, N
group_output = group_output.view(batch_size, T, G, -1).permute(0,2,3,1).contiguous() # B, G, N, T
output = input + group_output.view(input.shape)
return output
class ResMamba(nn.Module):
def __init__(self, input_size, hidden_size, dropout=0., bidirectional=True):
super(ResMamba, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.eps = torch.finfo(torch.float32).eps
self.norm = nn.GroupNorm(1, input_size, self.eps)
self.dropout = nn.Dropout(p=dropout)
self.rnn = MambaBlock(input_size)
self.proj = nn.Linear(input_size*2 ,input_size)
# linear projection layer
def forward(self, input):
# input shape: batch, dim, seq
rnn_output = self.rnn(self.dropout(self.norm(input)).transpose(1, 2).contiguous())
rnn_output = self.proj(rnn_output.contiguous().view(-1, rnn_output.shape[2])).view(input.shape[0],
input.shape[2],
input.shape[1])
return input + rnn_output.transpose(1, 2).contiguous()
class BSNet(nn.Module):
def __init__(self, in_channel, nband=7):
super(BSNet, self).__init__()
self.nband = nband
self.feature_dim = in_channel // nband
self.band_rnn = ResMamba(self.feature_dim, self.feature_dim*2)
self.band_comm = ResMamba(self.feature_dim, self.feature_dim*2)
self.channel_comm = TAC(self.feature_dim, self.feature_dim*3)
def forward(self, input):
# input shape: B, nch, nband*N, T
B, nch, N, T = input.shape
band_output = self.band_rnn(input.view(B*nch*self.nband, self.feature_dim, -1)).view(B*nch, self.nband, -1, T)
# band comm
band_output = band_output.permute(0,3,2,1).contiguous().view(B*nch*T, -1, self.nband)
output = self.band_comm(band_output).view(B*nch, T, -1, self.nband).permute(0,3,2,1).contiguous()
# channel comm
output = output.view(B, nch, self.nband, -1, T).transpose(1,2).contiguous().view(B*self.nband, nch, -1, T)
output = self.channel_comm(output).view(B, self.nband, nch, -1, T).transpose(1,2).contiguous()
return output.view(B, nch, N, T)
class Separator(nn.Module):
def __init__(self, sr=44100, win=2048, stride=512, feature_dim=128, num_repeat_mask=8, num_repeat_map=4, num_output=4):
super(Separator, self).__init__()
self.sr = sr
self.win = win
self.stride = stride
self.group = self.win // 2
self.enc_dim = self.win // 2 + 1
self.feature_dim = feature_dim
self.num_output = num_output
self.eps = torch.finfo(torch.float32).eps
# 0-1k (50 hop), 1k-2k (100 hop), 2k-4k (250 hop), 4k-8k (500 hop), 8k-16k (1k hop), 16k-20k (2k hop), 20k-inf
bandwidth_50 = int(np.floor(50 / (sr / 2.) * self.enc_dim))
bandwidth_100 = int(np.floor(100 / (sr / 2.) * self.enc_dim))
bandwidth_250 = int(np.floor(250 / (sr / 2.) * self.enc_dim))
bandwidth_500 = int(np.floor(500 / (sr / 2.) * self.enc_dim))
bandwidth_1k = int(np.floor(1000 / (sr / 2.) * self.enc_dim))
bandwidth_2k = int(np.floor(2000 / (sr / 2.) * self.enc_dim))
self.band_width = [bandwidth_50]*20
self.band_width += [bandwidth_100]*10
self.band_width += [bandwidth_250]*8
self.band_width += [bandwidth_500]*8
self.band_width += [bandwidth_1k]*8
self.band_width += [bandwidth_2k]*2
self.band_width.append(self.enc_dim - np.sum(self.band_width))
self.nband = len(self.band_width)
print(self.band_width)
self.BN_mask = nn.ModuleList([])
for i in range(self.nband):
self.BN_mask.append(nn.Sequential(nn.GroupNorm(1, self.band_width[i]*2, self.eps),
nn.Conv1d(self.band_width[i]*2, self.feature_dim, 1)
)
)
self.BN_map = nn.ModuleList([])
for i in range(self.nband):
self.BN_map.append(nn.Sequential(nn.GroupNorm(1, self.band_width[i] * 2, self.eps),
nn.Conv1d(self.band_width[i] * 2, self.feature_dim, 1)
)
)
self.separator_mask = []
for i in range(num_repeat_mask):
self.separator_mask.append(BSNet(self.nband*self.feature_dim, self.nband))
self.separator_mask = nn.Sequential(*self.separator_mask)
self.separator_map = []
for i in range(num_repeat_map):
self.separator_map.append(BSNet(self.nband * self.feature_dim, self.nband))
self.separator_map = nn.Sequential(*self.separator_map)
self.in_conv = nn.Conv1d(self.feature_dim*2, self.feature_dim, 1)
self.Tanh = nn.Tanh()
self.mask = nn.ModuleList([])
self.map = nn.ModuleList([])
for i in range(self.nband):
self.mask.append(nn.Sequential(nn.GroupNorm(1, self.feature_dim, torch.finfo(torch.float32).eps),
nn.Conv1d(self.feature_dim, self.feature_dim*1*self.num_output, 1),
nn.Tanh(),
nn.Conv1d(self.feature_dim*1*self.num_output, self.feature_dim*1*self.num_output, 1, groups=self.num_output),
nn.Tanh(),
nn.Conv1d(self.feature_dim*1*self.num_output, self.band_width[i]*4*self.num_output, 1, groups=self.num_output)
)
)
self.map.append(nn.Sequential(nn.GroupNorm(1, self.feature_dim, torch.finfo(torch.float32).eps),
nn.Conv1d(self.feature_dim, self.feature_dim*1*self.num_output, 1),
nn.Tanh(),
nn.Conv1d(self.feature_dim*1*self.num_output, self.feature_dim*1*self.num_output, 1, groups=self.num_output),
nn.Tanh(),
nn.Conv1d(self.feature_dim*1*self.num_output, self.band_width[i]*4*self.num_output, 1, groups=self.num_output)
)
)
def pad_input(self, input, window, stride):
"""
Zero-padding input according to window/stride size.
"""
batch_size, nsample = input.shape
# pad the signals at the end for matching the window/stride size
rest = window - (stride + nsample % window) % window
if rest > 0:
pad = torch.zeros(batch_size, rest).type(input.type())
input = torch.cat([input, pad], 1)
pad_aux = torch.zeros(batch_size, stride).type(input.type())
input = torch.cat([pad_aux, input, pad_aux], 1)
return input, rest
def forward(self, input):
# input shape: (B, C, T)
batch_size, nch, nsample = input.shape
input = input.view(batch_size*nch, -1)
# frequency-domain separation
spec = torch.stft(input, n_fft=self.win, hop_length=self.stride,
window=torch.hann_window(self.win).to(input.device).type(input.type()),
return_complex=True)
# concat real and imag, split to subbands
spec_RI = torch.stack([spec.real, spec.imag], 1) # B*nch, 2, F, T
subband_spec_RI = []
subband_spec = []
band_idx = 0
for i in range(len(self.band_width)):
subband_spec_RI.append(spec_RI[:,:,band_idx:band_idx+self.band_width[i]].contiguous())
subband_spec.append(spec[:,band_idx:band_idx+self.band_width[i]]) # B*nch, BW, T
band_idx += self.band_width[i]
# normalization and bottleneck
subband_feature_mask = []
for i in range(len(self.band_width)):
subband_feature_mask.append(self.BN_mask[i](subband_spec_RI[i].view(batch_size*nch, self.band_width[i]*2, -1)))
subband_feature_mask = torch.stack(subband_feature_mask, 1) # B, nband, N, T
subband_feature_map = []
for i in range(len(self.band_width)):
subband_feature_map.append(self.BN_map[i](subband_spec_RI[i].view(batch_size * nch, self.band_width[i] * 2, -1)))
subband_feature_map = torch.stack(subband_feature_map, 1) # B, nband, N, T
# separator
sep_output = checkpoint_sequential(self.separator_mask, 2, subband_feature_mask.view(batch_size, nch, self.nband*self.feature_dim, -1)) # B, nband*N, T
sep_output = sep_output.view(batch_size*nch, self.nband, self.feature_dim, -1)
combined = torch.cat((subband_feature_map,sep_output), dim=2)
combined1 = combined.reshape(batch_size * nch * self.nband,self.feature_dim*2,-1)
combined2 = self.Tanh(self.in_conv(combined1))
combined3 = combined2.reshape(batch_size * nch, self.nband,self.feature_dim,-1)
sep_output2 = checkpoint_sequential(self.separator_map, 2, combined3.view(batch_size, nch, self.nband*self.feature_dim, -1)) # 1B, nband*N, T
sep_output2 = sep_output2.view(batch_size * nch, self.nband, self.feature_dim, -1)
sep_subband_spec = []
sep_subband_spec_mask = []
for i in range(self.nband):
this_output = self.mask[i](sep_output[:,i]).view(batch_size*nch, 2, 2, self.num_output, self.band_width[i], -1)
this_mask = this_output[:,0] * torch.sigmoid(this_output[:,1]) # B*nch, 2, K, BW, T
this_mask_real = this_mask[:,0] # B*nch, K, BW, T
this_mask_imag = this_mask[:,1] # B*nch, K, BW, T
# force mask sum to 1
this_mask_real_sum = this_mask_real.sum(1).unsqueeze(1) # B*nch, 1, BW, T
this_mask_imag_sum = this_mask_imag.sum(1).unsqueeze(1) # B*nch, 1, BW, T
this_mask_real = this_mask_real - (this_mask_real_sum - 1) / self.num_output
this_mask_imag = this_mask_imag - this_mask_imag_sum / self.num_output
est_spec_real = subband_spec[i].real.unsqueeze(1) * this_mask_real - subband_spec[i].imag.unsqueeze(1) * this_mask_imag # B*nch, K, BW, T
est_spec_imag = subband_spec[i].real.unsqueeze(1) * this_mask_imag + subband_spec[i].imag.unsqueeze(1) * this_mask_real # B*nch, K, BW, T
##################################
this_output2 = self.map[i](sep_output2[:,i]).view(batch_size*nch, 2, 2, self.num_output, self.band_width[i], -1)
this_map = this_output2[:,0] * torch.sigmoid(this_output2[:,1]) # B*nch, 2, K, BW, T
this_map_real = this_map[:,0] # B*nch, K, BW, T
this_map_imag = this_map[:,1] # B*nch, K, BW, T
est_spec_real2 = est_spec_real+this_map_real
est_spec_imag2 = est_spec_imag+this_map_imag
sep_subband_spec.append(torch.complex(est_spec_real2, est_spec_imag2))
sep_subband_spec_mask.append(torch.complex(est_spec_real, est_spec_imag))
sep_subband_spec = torch.cat(sep_subband_spec, 2)
est_spec_mask = torch.cat(sep_subband_spec_mask, 2)
output = torch.istft(sep_subband_spec.view(batch_size*nch*self.num_output, self.enc_dim, -1),
n_fft=self.win, hop_length=self.stride,
window=torch.hann_window(self.win).to(input.device).type(input.type()), length=nsample)
output_mask = torch.istft(est_spec_mask.view(batch_size*nch*self.num_output, self.enc_dim, -1),
n_fft=self.win, hop_length=self.stride,
window=torch.hann_window(self.win).to(input.device).type(input.type()), length=nsample)
output = output.view(batch_size, nch, self.num_output, -1).transpose(1,2).contiguous()
output_mask = output_mask.view(batch_size, nch, self.num_output, -1).transpose(1,2).contiguous()
# return output, output_mask
return output
if __name__ == '__main__':
model = Separator().cuda()
arr = np.zeros((1, 2, 3*44100), dtype=np.float32)
x = torch.from_numpy(arr).cuda()
res = model(x)
|