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10d809f | 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 | # coding: utf-8
# Author：WangTianRui
# Date ：2020/11/3 16:49
import torch.nn as nn
import torch
from utils.conv_stft import *
from utils.complexnn import *
class DCCRN(nn.Module):
def __init__(self,
rnn_layer=2, rnn_hidden=256,
win_len=400, hop_len=100, fft_len=512, win_type='hann',
use_clstm=True, use_cbn=False, masking_mode='E',
kernel_size=5, kernel_num=(32, 64, 128, 256, 256, 256)
):
super(DCCRN, self).__init__()
self.rnn_layer = rnn_layer
self.rnn_hidden = rnn_hidden
self.win_len = win_len
self.hop_len = hop_len
self.fft_len = fft_len
self.win_type = win_type
self.use_clstm = use_clstm
self.use_cbn = use_cbn
self.masking_mode = masking_mode
self.kernel_size = kernel_size
self.kernel_num = (2,) + kernel_num
self.stft = ConvSTFT(self.win_len, self.hop_len, self.fft_len, self.win_type, 'complex', fix=True)
self.istft = ConviSTFT(self.win_len, self.hop_len, self.fft_len, self.win_type, 'complex', fix=True)
self.encoder = nn.ModuleList()
self.decoder = nn.ModuleList()
for idx in range(len(self.kernel_num) - 1):
self.encoder.append(
nn.Sequential(
ComplexConv2d(
self.kernel_num[idx],
self.kernel_num[idx + 1],
kernel_size=(self.kernel_size, 2),
stride=(2, 1),
padding=(2, 1)
),
nn.BatchNorm2d(self.kernel_num[idx + 1]) if not use_cbn else ComplexBatchNorm(
self.kernel_num[idx + 1]),
nn.PReLU()
)
)
hidden_dim = self.fft_len // (2 ** (len(self.kernel_num)))
if self.use_clstm:
rnns = []
for idx in range(rnn_layer):
rnns.append(
NavieComplexLSTM(
input_size=hidden_dim * self.kernel_num[-1] if idx == 0 else self.rnn_hidden,
hidden_size=self.rnn_hidden,
batch_first=False,
projection_dim=hidden_dim * self.kernel_num[-1] if idx == rnn_layer - 1 else None
)
)
self.enhance = nn.Sequential(*rnns)
else:
self.enhance = nn.LSTM(
input_size=hidden_dim * self.kernel_num[-1],
hidden_size=self.rnn_hidden,
num_layers=2,
dropout=0.0,
batch_first=False
)
self.transform = nn.Linear(self.rnn_hidden, hidden_dim * self.kernel_num[-1])
for idx in range(len(self.kernel_num) - 1, 0, -1):
if idx != 1:
self.decoder.append(
nn.Sequential(
ComplexConvTranspose2d(
self.kernel_num[idx] * 2,
self.kernel_num[idx - 1],
kernel_size=(self.kernel_size, 2),
stride=(2, 1),
padding=(2, 0),
output_padding=(1, 0)
),
nn.BatchNorm2d(self.kernel_num[idx - 1]) if not use_cbn else ComplexBatchNorm(
self.kernel_num[idx - 1]),
nn.PReLU()
)
)
else:
self.decoder.append(
nn.Sequential(
ComplexConvTranspose2d(
self.kernel_num[idx] * 2,
self.kernel_num[idx - 1],
kernel_size=(self.kernel_size, 2),
stride=(2, 1),
padding=(2, 0),
output_padding=(1, 0)
)
)
)
if isinstance(self.enhance, nn.LSTM):
self.enhance.flatten_parameters()
def forward(self, x):
stft = self.stft(x)
# print("stft:", stft.size())
real = stft[:, :self.fft_len // 2 + 1]
imag = stft[:, self.fft_len // 2 + 1:]
# print("real imag:", real.size(), imag.size())
spec_mags = torch.sqrt(real ** 2 + imag ** 2 + 1e-8)
spec_phase = torch.atan2(imag, real)
spec_complex = torch.stack([real, imag], dim=1)[:, :, 1:] # B,2,256
# print("spec", spec_mags.size(), spec_phase.size(), spec_complex.size())
out = spec_complex
encoder_out = []
for idx, encoder in enumerate(self.encoder):
out = encoder(out)
# print("encoder out:", out.size())
encoder_out.append(out)
B, C, D, T = out.size()
out = out.permute(3, 0, 1, 2)
if self.use_clstm:
r_rnn_in = out[:, :, :C // 2]
i_rnn_in = out[:, :, C // 2:]
r_rnn_in = torch.reshape(r_rnn_in, [T, B, C // 2 * D])
i_rnn_in = torch.reshape(i_rnn_in, [T, B, C // 2 * D])
r_rnn_in, i_rnn_in = self.enhance([r_rnn_in, i_rnn_in])
r_rnn_in = torch.reshape(r_rnn_in, [T, B, C // 2, D])
i_rnn_in = torch.reshape(i_rnn_in, [T, B, C // 2, D])
out = torch.cat([r_rnn_in, i_rnn_in], 2)
else:
out = torch.reshape(out, [T, B, C * D])
out, _ = self.enhance(out)
out = self.transform(out)
out = torch.reshape(out, [T, B, C, D])
out = out.permute(1, 2, 3, 0)
for idx in range(len(self.decoder)):
out = complex_cat([out, encoder_out[-1 - idx]], 1)
out = self.decoder[idx](out)
out = out[..., 1:]
mask_real = out[:, 0]
mask_imag = out[:, 1]
mask_real = F.pad(mask_real, [0, 0, 1, 0])
mask_imag = F.pad(mask_imag, [0, 0, 1, 0])
if self.masking_mode == 'E':
mask_mags = (mask_real ** 2 + mask_imag ** 2) ** 0.5
real_phase = mask_real / (mask_mags + 1e-8)
imag_phase = mask_imag / (mask_mags + 1e-8)
mask_phase = torch.atan2(
imag_phase,
real_phase
)
mask_mags = torch.tanh(mask_mags)
est_mags = mask_mags * spec_mags
est_phase = spec_phase + mask_phase
real = est_mags * torch.cos(est_phase)
imag = est_mags * torch.sin(est_phase)
elif self.masking_mode == 'C':
real = real * mask_real - imag * mask_imag
imag = real * mask_imag + imag * mask_real
elif self.masking_mode == 'R':
real = real * mask_real
imag = imag * mask_imag
out_spec = torch.cat([real, imag], 1)
out_wav = self.istft(out_spec)
out_wav = torch.squeeze(out_wav, 1)
out_wav = out_wav.clamp_(-1, 1)
return out_wav
def l2_norm(s1, s2):
norm = torch.sum(s1 * s2, -1, keepdim=True)
return norm
def si_snr(s1, s2, eps=1e-8):
s1_s2_norm = l2_norm(s1, s2)
s2_s2_norm = l2_norm(s2, s2)
s_target = s1_s2_norm / (s2_s2_norm + eps) * s2
e_nosie = s1 - s_target
target_norm = l2_norm(s_target, s_target)
noise_norm = l2_norm(e_nosie, e_nosie)
snr = 10 * torch.log10(target_norm / (noise_norm + eps) + eps)
return torch.mean(snr)
def loss(inputs, label):
return -(si_snr(inputs, label))
if __name__ == '__main__':
test_model = DCCRN(rnn_hidden=256, masking_mode='E', use_clstm=True, kernel_num=(32, 64, 128, 256, 256, 256))
model_test_timer(test_model, (1, 16000 * 30))
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