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model.py
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# coding: utf-8
# Author：WangTianRui
# Date ：2020/11/3 16:49
# from base.BaseModel import *
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='hanning',
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))
from BaseModel import *
model_test_timer(test_model, (1, 16000 * 30))