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