models/discriminator.py

# References: https://github.com/yxlu-0102/MP-SENet/blob/main/models/discriminator.py

import torch
import torch.nn as nn
import numpy as np
from pesq import pesq
from joblib import Parallel, delayed
from models.lsigmoid import LearnableSigmoid1D

def pesq_loss(clean, noisy, sr=16000):
    try:
        pesq_score = pesq(sr, clean, noisy, 'wb')
    except:
        # error can happen due to silent period
        pesq_score = -1
    return pesq_score


def batch_pesq(clean, noisy, cfg):
    num_worker = cfg['env_setting']['num_workers']
    pesq_score = Parallel(n_jobs=num_worker)(delayed(pesq_loss)(c, n) for c, n in zip(clean, noisy))
    pesq_score = np.array(pesq_score)
    if -1 in pesq_score:
        return None
    pesq_score = (pesq_score - 1) / 3.5
    return torch.FloatTensor(pesq_score)


class MetricDiscriminator(nn.Module):
    def __init__(self, dim=16, in_channel=2):
        super(MetricDiscriminator, self).__init__()
        self.layers = nn.Sequential(
            nn.utils.spectral_norm(nn.Conv2d(in_channel, dim, (4,4), (2,2), (1,1), bias=False)),
            nn.InstanceNorm2d(dim, affine=True),
            nn.PReLU(dim),
            nn.utils.spectral_norm(nn.Conv2d(dim, dim*2, (4,4), (2,2), (1,1), bias=False)),
            nn.InstanceNorm2d(dim*2, affine=True),
            nn.PReLU(dim*2),
            nn.utils.spectral_norm(nn.Conv2d(dim*2, dim*4, (4,4), (2,2), (1,1), bias=False)),
            nn.InstanceNorm2d(dim*4, affine=True),
            nn.PReLU(dim*4),
            nn.utils.spectral_norm(nn.Conv2d(dim*4, dim*8, (4,4), (2,2), (1,1), bias=False)),
            nn.InstanceNorm2d(dim*8, affine=True),
            nn.PReLU(dim*8),
            nn.AdaptiveMaxPool2d(1),
            nn.Flatten(),
            nn.utils.spectral_norm(nn.Linear(dim*8, dim*4)),
            nn.Dropout(0.3),
            nn.PReLU(dim*4),
            nn.utils.spectral_norm(nn.Linear(dim*4, 1)),
            LearnableSigmoid1D(1)
        )

    def forward(self, x, y):
        xy = torch.stack((x, y), dim=1)
        return self.layers(xy)