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import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from denoiser.conformer import ConformerBlock
from denoiser.utils import get_padding_2d, LearnableSigmoid_2d
from pesq import pesq
from joblib import Parallel, delayed
class DenseBlock(nn.Module):
def __init__(self, h, kernel_size=(3, 3), depth=4):
super(DenseBlock, self).__init__()
self.h = h
self.depth = depth
self.dense_block = nn.ModuleList([])
for i in range(depth):
dil = 2 ** i
dense_conv = nn.Sequential(
nn.Conv2d(h.dense_channel*(i+1), h.dense_channel, kernel_size, dilation=(dil, 1),
padding=get_padding_2d(kernel_size, (dil, 1))),
nn.InstanceNorm2d(h.dense_channel, affine=True),
nn.PReLU(h.dense_channel)
)
self.dense_block.append(dense_conv)
def forward(self, x):
skip = x
for i in range(self.depth):
x = self.dense_block[i](skip)
skip = torch.cat([x, skip], dim=1)
return x
class DenseEncoder(nn.Module):
def __init__(self, h, in_channel):
super(DenseEncoder, self).__init__()
self.h = h
self.dense_conv_1 = nn.Sequential(
nn.Conv2d(in_channel, h.dense_channel, (1, 1)),
nn.InstanceNorm2d(h.dense_channel, affine=True),
nn.PReLU(h.dense_channel))
self.dense_block = DenseBlock(h, depth=4) # [b, h.dense_channel, ndim_time, h.n_fft//2+1]
self.dense_conv_2 = nn.Sequential(
nn.Conv2d(h.dense_channel, h.dense_channel, (1, 3), (1, 2)),
nn.InstanceNorm2d(h.dense_channel, affine=True),
nn.PReLU(h.dense_channel))
def forward(self, x):
x = self.dense_conv_1(x) # [b, 64, T, F]
x = self.dense_block(x) # [b, 64, T, F]
x = self.dense_conv_2(x) # [b, 64, T, F//2]
return x
class MaskDecoder(nn.Module):
def __init__(self, h, out_channel=1):
super(MaskDecoder, self).__init__()
self.dense_block = DenseBlock(h, depth=4)
self.mask_conv = nn.Sequential(
nn.ConvTranspose2d(h.dense_channel, h.dense_channel, (1, 3), (1, 2)),
nn.Conv2d(h.dense_channel, out_channel, (1, 1)),
nn.InstanceNorm2d(out_channel, affine=True),
nn.PReLU(out_channel),
nn.Conv2d(out_channel, out_channel, (1, 1))
)
self.lsigmoid = LearnableSigmoid_2d(h.n_fft//2+1, beta=h.beta)
def forward(self, x):
x = self.dense_block(x)
x = self.mask_conv(x)
x = x.permute(0, 3, 2, 1).squeeze(-1)
x = self.lsigmoid(x).permute(0, 2, 1).unsqueeze(1)
return x
class PhaseDecoder(nn.Module):
def __init__(self, h, out_channel=1):
super(PhaseDecoder, self).__init__()
self.dense_block = DenseBlock(h, depth=4)
self.phase_conv = nn.Sequential(
nn.ConvTranspose2d(h.dense_channel, h.dense_channel, (1, 3), (1, 2)),
nn.InstanceNorm2d(h.dense_channel, affine=True),
nn.PReLU(h.dense_channel)
)
self.phase_conv_r = nn.Conv2d(h.dense_channel, out_channel, (1, 1))
self.phase_conv_i = nn.Conv2d(h.dense_channel, out_channel, (1, 1))
def forward(self, x):
x = self.dense_block(x)
x = self.phase_conv(x)
x_r = self.phase_conv_r(x)
x_i = self.phase_conv_i(x)
x = torch.atan2(x_i, x_r)
return x
class TSConformerBlock(nn.Module):
def __init__(self, h):
super(TSConformerBlock, self).__init__()
self.h = h
self.time_conformer = ConformerBlock(dim=h.dense_channel, n_head=4, ccm_kernel_size=31,
ffm_dropout=0.2, attn_dropout=0.2)
self.freq_conformer = ConformerBlock(dim=h.dense_channel, n_head=4, ccm_kernel_size=31,
ffm_dropout=0.2, attn_dropout=0.2)
def forward(self, x):
b, c, t, f = x.size()
x = x.permute(0, 3, 2, 1).contiguous().view(b*f, t, c)
x = self.time_conformer(x) + x
x = x.view(b, f, t, c).permute(0, 2, 1, 3).contiguous().view(b*t, f, c)
x = self.freq_conformer(x) + x
x = x.view(b, t, f, c).permute(0, 3, 1, 2)
return x
class MPNet(nn.Module):
def __init__(self, h, num_tscblocks=4):
super(MPNet, self).__init__()
self.h = h
self.num_tscblocks = num_tscblocks
self.dense_encoder = DenseEncoder(h, in_channel=2)
self.TSConformer = nn.ModuleList([])
for i in range(num_tscblocks):
self.TSConformer.append(TSConformerBlock(h))
self.mask_decoder = MaskDecoder(h, out_channel=1)
self.phase_decoder = PhaseDecoder(h, out_channel=1)
def forward(self, noisy_mag, noisy_pha): # [B, F, T]
noisy_mag = noisy_mag.unsqueeze(-1).permute(0, 3, 2, 1) # [B, 1, T, F]
noisy_pha = noisy_pha.unsqueeze(-1).permute(0, 3, 2, 1) # [B, 1, T, F]
x = torch.cat((noisy_mag, noisy_pha), dim=1) # [B, 2, T, F]
x = self.dense_encoder(x)
for i in range(self.num_tscblocks):
x = self.TSConformer[i](x)
denoised_mag = (noisy_mag * self.mask_decoder(x)).permute(0, 3, 2, 1).squeeze(-1)
denoised_pha = self.phase_decoder(x).permute(0, 3, 2, 1).squeeze(-1)
denoised_com = torch.stack((denoised_mag*torch.cos(denoised_pha),
denoised_mag*torch.sin(denoised_pha)), dim=-1)
return denoised_mag, denoised_pha, denoised_com
def phase_losses(phase_r, phase_g, h):
dim_freq = h.n_fft // 2 + 1
dim_time = phase_r.size(-1)
gd_matrix = (torch.triu(torch.ones(dim_freq, dim_freq), diagonal=1) - torch.triu(torch.ones(dim_freq, dim_freq), diagonal=2) - torch.eye(dim_freq)).to(phase_g.device)
gd_r = torch.matmul(phase_r.permute(0, 2, 1), gd_matrix)
gd_g = torch.matmul(phase_g.permute(0, 2, 1), gd_matrix)
iaf_matrix = (torch.triu(torch.ones(dim_time, dim_time), diagonal=1) - torch.triu(torch.ones(dim_time, dim_time), diagonal=2) - torch.eye(dim_time)).to(phase_g.device)
iaf_r = torch.matmul(phase_r, iaf_matrix)
iaf_g = torch.matmul(phase_g, iaf_matrix)
ip_loss = torch.mean(anti_wrapping_function(phase_r-phase_g))
gd_loss = torch.mean(anti_wrapping_function(gd_r-gd_g))
iaf_loss = torch.mean(anti_wrapping_function(iaf_r-iaf_g))
return ip_loss, gd_loss, iaf_loss
def anti_wrapping_function(x):
return torch.abs(x - torch.round(x / (2 * np.pi)) * 2 * np.pi)
def pesq_score(utts_r, utts_g, h):
pesq_score = Parallel(n_jobs=30)(delayed(eval_pesq)(
utts_r[i].squeeze().cpu().numpy(),
utts_g[i].squeeze().cpu().numpy(),
h.sampling_rate)
for i in range(len(utts_r)))
pesq_score = np.mean(pesq_score)
return pesq_score
def eval_pesq(clean_utt, esti_utt, sr):
try:
pesq_score = pesq(sr, clean_utt, esti_utt)
except:
# error can happen due to silent period
pesq_score = -1
return pesq_score
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