models.py

# modified from https://github.com/jik876/hifi-gan
# modified from https://github.com/descriptinc/melgan-neurips
import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
from utils import init_weights, get_padding

LRELU_SLOPE = 0.1


class ResBlock1(torch.nn.Module):

    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
        super(ResBlock1, self).__init__()
        self.h = h
        self.convs1 = nn.ModuleList([
            weight_norm(
                Conv1d(channels,
                       channels,
                       kernel_size,
                       1,
                       dilation=dilation[0],
                       padding=get_padding(kernel_size, dilation[0]))),
            weight_norm(
                Conv1d(channels,
                       channels,
                       kernel_size,
                       1,
                       dilation=dilation[1],
                       padding=get_padding(kernel_size, dilation[1]))),
            weight_norm(
                Conv1d(channels,
                       channels,
                       kernel_size,
                       1,
                       dilation=dilation[2],
                       padding=get_padding(kernel_size, dilation[2])))
        ])
        self.convs1.apply(init_weights)

        self.convs2 = nn.ModuleList([
            weight_norm(
                Conv1d(channels,
                       channels,
                       kernel_size,
                       1,
                       dilation=1,
                       padding=get_padding(kernel_size, 1))),
            weight_norm(
                Conv1d(channels,
                       channels,
                       kernel_size,
                       1,
                       dilation=1,
                       padding=get_padding(kernel_size, 1))),
            weight_norm(
                Conv1d(channels,
                       channels,
                       kernel_size,
                       1,
                       dilation=1,
                       padding=get_padding(kernel_size, 1)))
        ])
        self.convs2.apply(init_weights)

    def forward(self, x):
        for c1, c2 in zip(self.convs1, self.convs2):
            xt = F.leaky_relu(x, LRELU_SLOPE)
            xt = c1(xt)
            xt = F.leaky_relu(xt, LRELU_SLOPE)
            xt = c2(xt)
            x = xt + x
        return x

    def remove_weight_norm(self):
        for l in self.convs1:
            remove_weight_norm(l)
        for l in self.convs2:
            remove_weight_norm(l)


class ResBlock2(torch.nn.Module):

    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)):
        super(ResBlock2, self).__init__()
        self.h = h
        self.convs = nn.ModuleList([
            weight_norm(
                Conv1d(channels,
                       channels,
                       kernel_size,
                       1,
                       dilation=dilation[0],
                       padding=get_padding(kernel_size, dilation[0]))),
            weight_norm(
                Conv1d(channels,
                       channels,
                       kernel_size,
                       1,
                       dilation=dilation[1],
                       padding=get_padding(kernel_size, dilation[1])))
        ])
        self.convs.apply(init_weights)

    def forward(self, x):
        for c in self.convs:
            xt = F.leaky_relu(x, LRELU_SLOPE)
            xt = c(xt)
            x = xt + x
        return x

    def remove_weight_norm(self):
        for l in self.convs:
            remove_weight_norm(l)


#HiFi-GAN generator
class Generator(torch.nn.Module):

    def __init__(self, h):
        super(Generator, self).__init__()
        self.h = h
        self.num_kernels = len(h.resblock_kernel_sizes)
        self.num_upsamples = len(h.upsample_rates)
        self.conv_pre = weight_norm(
            Conv1d(80, h.upsample_initial_channel, 7, 1, padding=3))
        resblock = ResBlock1 if h.resblock == '1' else ResBlock2

        self.ups = nn.ModuleList()
        for i, (u,
                k) in enumerate(zip(h.upsample_rates,
                                    h.upsample_kernel_sizes)):
            self.ups.append(
                weight_norm(
                    ConvTranspose1d(h.upsample_initial_channel // (2**i),
                                    h.upsample_initial_channel // (2**(i + 1)),
                                    k,
                                    u,
                                    padding=(k - u) // 2)))

        self.resblocks = nn.ModuleList()
        for i in range(len(self.ups)):
            ch = h.upsample_initial_channel // (2**(i + 1))
            for j, (k, d) in enumerate(
                    zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)):
                self.resblocks.append(resblock(h, ch, k, d))

        # we remove bias from original implementation
        self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3,
                                            bias=False))
        self.ups.apply(init_weights)
        self.conv_post.apply(init_weights)

    def forward(self, x):
        x = self.conv_pre(x)
        for i in range(self.num_upsamples):
            x = F.leaky_relu(x, LRELU_SLOPE)
            x = self.ups[i](x)
            xs = None
            for j in range(self.num_kernels):
                if xs is None:
                    xs = self.resblocks[i * self.num_kernels + j](x)
                else:
                    xs += self.resblocks[i * self.num_kernels + j](x)
            x = xs / self.num_kernels
        x = F.leaky_relu(x)
        x = self.conv_post(x)
        x = torch.tanh(x)

        return x

    def remove_weight_norm(self):
        print('Removing weight norm...')
        for l in self.ups:
            remove_weight_norm(l)
        for l in self.resblocks:
            l.remove_weight_norm()
        remove_weight_norm(self.conv_pre)
        remove_weight_norm(self.conv_post)


class DiscriminatorP(torch.nn.Module):

    def __init__(self,
                 period,
                 kernel_size=5,
                 stride=3,
                 use_spectral_norm=False):
        super(DiscriminatorP, self).__init__()
        self.period = period
        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
        self.convs = nn.ModuleList([
            norm_f(
                Conv2d(1,
                       32, (kernel_size, 1), (stride, 1),
                       padding=(get_padding(5, 1), 0))),
            norm_f(
                Conv2d(32,
                       128, (kernel_size, 1), (stride, 1),
                       padding=(get_padding(5, 1), 0))),
            norm_f(
                Conv2d(128,
                       512, (kernel_size, 1), (stride, 1),
                       padding=(get_padding(5, 1), 0))),
            norm_f(
                Conv2d(512,
                       1024, (kernel_size, 1), (stride, 1),
                       padding=(get_padding(5, 1), 0))),
            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))),
        ])
        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))

    def forward(self, x):
        fmap = []

        # 1d to 2d
        b, c, t = x.shape
        if t % self.period != 0:  # pad first
            n_pad = self.period - (t % self.period)
            x = F.pad(x, (0, n_pad), "reflect")
            t = t + n_pad
        x = x.view(b, c, t // self.period, self.period)

        for l in self.convs:
            x = l(x)
            x = F.leaky_relu(x, LRELU_SLOPE)
            fmap.append(x)
        x = self.conv_post(x)
        fmap.append(x)
        x = torch.flatten(x, 1, -1)

        return x, fmap


class MultiPeriodDiscriminator(torch.nn.Module):

    def __init__(self):
        super(MultiPeriodDiscriminator, self).__init__()
        self.discriminators = nn.ModuleList([
            DiscriminatorP(2),
            DiscriminatorP(3),
            DiscriminatorP(5),
            DiscriminatorP(7),
            DiscriminatorP(11),
        ])

    def forward(self, y, y_hat):
        y_d_rs = []
        y_d_gs = []
        fmap_rs = []
        fmap_gs = []
        for i, d in enumerate(self.discriminators):
            y_d_r, fmap_r = d(y)
            y_d_g, fmap_g = d(y_hat)
            y_d_rs.append(y_d_r)
            fmap_rs.append(fmap_r)
            y_d_gs.append(y_d_g)
            fmap_gs.append(fmap_g)

        return y_d_rs, y_d_gs, fmap_rs, fmap_gs


class DiscriminatorS(torch.nn.Module):

    def __init__(self, use_spectral_norm=False):
        super(DiscriminatorS, self).__init__()
        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
        self.convs = nn.ModuleList([
            norm_f(Conv1d(1, 128, 15, 1, padding=7)),
            norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)),
            norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)),
            norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)),
            norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)),
            norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)),
            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
        ])
        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))

    def forward(self, x):
        fmap = []
        for l in self.convs:
            x = l(x)
            x = F.leaky_relu(x, LRELU_SLOPE)
            fmap.append(x)
        x = self.conv_post(x)
        fmap.append(x)
        x = torch.flatten(x, 1, -1)

        return x, fmap


class MultiScaleDiscriminator(torch.nn.Module):

    def __init__(self):
        super(MultiScaleDiscriminator, self).__init__()
        self.discriminators = nn.ModuleList([
            DiscriminatorS(use_spectral_norm=True),
            DiscriminatorS(),
            DiscriminatorS(),
        ])
        self.meanpools = nn.ModuleList(
            [AvgPool1d(4, 2, padding=2),
             AvgPool1d(4, 2, padding=2)])

    def forward(self, y, y_hat):
        y_d_rs = []
        y_d_gs = []
        fmap_rs = []
        fmap_gs = []
        for i, d in enumerate(self.discriminators):
            if i != 0:
                y = self.meanpools[i - 1](y)
                y_hat = self.meanpools[i - 1](y_hat)
            y_d_r, fmap_r = d(y)
            y_d_g, fmap_g = d(y_hat)
            y_d_rs.append(y_d_r)
            fmap_rs.append(fmap_r)
            y_d_gs.append(y_d_g)
            fmap_gs.append(fmap_g)

        return y_d_rs, y_d_gs, fmap_rs, fmap_gs


def feature_loss(fmap_r, fmap_g):
    loss = 0
    for dr, dg in zip(fmap_r, fmap_g):
        for rl, gl in zip(dr, dg):
            loss += torch.mean(torch.abs(rl - gl))

    return loss * 2


def discriminator_loss(disc_real_outputs, disc_generated_outputs):
    loss = 0
    r_losses = []
    g_losses = []
    r_accs = []
    g_accs = []
    dr_stats = []
    dg_stats = []
    r_acc_mean = 0.
    g_acc_mean = 0.
    dr_mean = 0.
    dg_mean = 0.
    for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
        r_loss = torch.mean((1 - dr)**2)
        g_loss = torch.mean(dg**2)
        loss += (r_loss + g_loss)
        r_losses.append(r_loss.item())
        g_losses.append(g_loss.item())
        #acc added
        r_acc = (dr >= 0.5).float().mean().item()
        r_acc_mean += r_acc
        r_accs.append(r_acc)
        g_acc = (dg < 0.5).float().mean().item()
        g_acc_mean += g_acc
        g_accs.append(g_acc)
        dr_stats.append([dr.mean().item(), dr.std().item()])
        dg_stats.append([dg.mean().item(), dg.std().item()])
        dr_mean += dr.mean().item()
        dg_mean += dg.mean().item()
    r_accs.append(r_acc_mean / len(r_accs))
    g_accs.append(g_acc_mean / len(g_accs))
    dr_stats.append([dr_mean / len(dr_stats), 0.])
    dg_stats.append([dg_mean / len(dg_stats), 0.])

    return loss, r_losses, g_losses, r_accs, g_accs, dr_stats, dg_stats


def generator_loss(disc_outputs):
    loss = 0
    gen_losses = []
    for dg in disc_outputs:
        l = torch.mean((1 - dg)**2)
        gen_losses.append(l)
        loss += l

    return loss, gen_losses


### MelGAN Generator
import numpy as np


def weights_init(m):
    classname = m.__class__.__name__
    if classname.find("Conv") != -1:
        m.weight.data.normal_(0.0, 0.02)
    elif classname.find("BatchNorm2d") != -1:
        m.weight.data.normal_(1.0, 0.02)
        m.bias.data.fill_(0)


def WNConv1d(*args, **kwargs):
    return weight_norm(nn.Conv1d(*args, **kwargs))


def WNConvTranspose1d(*args, **kwargs):
    return weight_norm(nn.ConvTranspose1d(*args, **kwargs))


class ResnetBlock(nn.Module):

    def __init__(self, dim, dilation=1):
        super().__init__()
        self.block = nn.Sequential(
            nn.LeakyReLU(0.2),
            nn.ReflectionPad1d(dilation),
            WNConv1d(dim, dim, kernel_size=3, dilation=dilation),
            nn.LeakyReLU(0.2),
            WNConv1d(dim, dim, kernel_size=1),
        )
        self.shortcut = WNConv1d(dim, dim, kernel_size=1)

    def forward(self, x):
        return self.shortcut(x) + self.block(x)

    def remove_weight_norm(self):
        remove_weight_norm(self.shortcut)
        remove_weight_norm(self.block[-1])
        remove_weight_norm(self.block[-3])


class MelGANGenerator(nn.Module):

    def __init__(self, h):
        super().__init__()
        self.h = h
        input_size = h.num_mels
        ngf = h.ngf
        n_residual_layers = h.n_residual_layers

        ratios = [8, 8, 2, 2]
        self.hop_length = np.prod(ratios)
        mult = int(2**len(ratios))

        model = [
            nn.ReflectionPad1d(3),
            WNConv1d(input_size, mult * ngf, kernel_size=7, padding=0),
        ]

        # Upsample to raw audio scale
        for i, r in enumerate(ratios):
            model += [
                nn.LeakyReLU(0.2),
                WNConvTranspose1d(
                    mult * ngf,
                    mult * ngf // 2,
                    kernel_size=r * 2,
                    stride=r,
                    padding=r // 2 + r % 2,
                    output_padding=r % 2,
                ),
            ]

            for j in range(n_residual_layers):
                model += [ResnetBlock(mult * ngf // 2, dilation=3**j)]

            mult //= 2

        model += [
            nn.LeakyReLU(0.2),
            nn.ReflectionPad1d(3),
            WNConv1d(ngf, 1, kernel_size=7, padding=0),
            nn.Tanh(),
        ]

        self.model = nn.Sequential(*model)
        self.apply(weights_init)

    def forward(self, x):
        return self.model(x)

    def remove_weight_norm(self):
        for l in self.model:
            classname = l.__class__.__name__
            if classname.find("Conv") != -1:
                remove_weight_norm(l)
            elif classname.find("ResnetBlock") != -1:
                l.remove_weight_norm()