ChazzyG/Retrieval-based-Voice-Conversion-WebUI

1import torch2from torch.nn import functional as F3 4 5def feature_loss(fmap_r, fmap_g):6    loss = 07    for dr, dg in zip(fmap_r, fmap_g):8        for rl, gl in zip(dr, dg):9            rl = rl.float().detach()10            gl = gl.float()11            loss += torch.mean(torch.abs(rl - gl))12 13    return loss * 214 15 16def discriminator_loss(disc_real_outputs, disc_generated_outputs):17    loss = 018    r_losses = []19    g_losses = []20    for dr, dg in zip(disc_real_outputs, disc_generated_outputs):21        dr = dr.float()22        dg = dg.float()23        r_loss = torch.mean((1 - dr) ** 2)24        g_loss = torch.mean(dg**2)25        loss += r_loss + g_loss26        r_losses.append(r_loss.item())27        g_losses.append(g_loss.item())28 29    return loss, r_losses, g_losses30 31 32def generator_loss(disc_outputs):33    loss = 034    gen_losses = []35    for dg in disc_outputs:36        dg = dg.float()37        l = torch.mean((1 - dg) ** 2)38        gen_losses.append(l)39        loss += l40 41    return loss, gen_losses42 43 44def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):45    """46    z_p, logs_q: [b, h, t_t]47    m_p, logs_p: [b, h, t_t]48    """49    z_p = z_p.float()50    logs_q = logs_q.float()51    m_p = m_p.float()52    logs_p = logs_p.float()53    z_mask = z_mask.float()54 55    kl = logs_p - logs_q - 0.556    kl += 0.5 * ((z_p - m_p) ** 2) * torch.exp(-2.0 * logs_p)57    kl = torch.sum(kl * z_mask)58    l = kl / torch.sum(z_mask)59    return l60