NeuralSeq/modules/hifigan/hifigan.py

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 modules.parallel_wavegan.layers import UpsampleNetwork, ConvInUpsampleNetwork
from modules.parallel_wavegan.models.source import SourceModuleHnNSF
import numpy as np

LRELU_SLOPE = 0.1


def init_weights(m, mean=0.0, std=0.01):
    classname = m.__class__.__name__
    if classname.find("Conv") != -1:
        m.weight.data.normal_(mean, std)


def apply_weight_norm(m):
    classname = m.__class__.__name__
    if classname.find("Conv") != -1:
        weight_norm(m)


def get_padding(kernel_size, dilation=1):
    return int((kernel_size * dilation - dilation) / 2)


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)


class Conv1d1x1(Conv1d):
    """1x1 Conv1d with customized initialization."""

    def __init__(self, in_channels, out_channels, bias):
        """Initialize 1x1 Conv1d module."""
        super(Conv1d1x1, self).__init__(in_channels, out_channels,
                                        kernel_size=1, padding=0,
                                        dilation=1, bias=bias)


class HifiGanGenerator(torch.nn.Module):
    def __init__(self, h, c_out=1):
        super(HifiGanGenerator, self).__init__()
        self.h = h
        self.num_kernels = len(h['resblock_kernel_sizes'])
        self.num_upsamples = len(h['upsample_rates'])

        if h['use_pitch_embed']:
            self.harmonic_num = 8
            self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(h['upsample_rates']))
            self.m_source = SourceModuleHnNSF(
                sampling_rate=h['audio_sample_rate'],
                harmonic_num=self.harmonic_num)
            self.noise_convs = nn.ModuleList()
        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'])):
            c_cur = h['upsample_initial_channel'] // (2 ** (i + 1))
            self.ups.append(weight_norm(
                ConvTranspose1d(c_cur * 2, c_cur, k, u, padding=(k - u) // 2)))
            if h['use_pitch_embed']:
                if i + 1 < len(h['upsample_rates']):
                    stride_f0 = np.prod(h['upsample_rates'][i + 1:])
                    self.noise_convs.append(Conv1d(
                        1, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=stride_f0 // 2))
                else:
                    self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))

        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))

        self.conv_post = weight_norm(Conv1d(ch, c_out, 7, 1, padding=3))
        self.ups.apply(init_weights)
        self.conv_post.apply(init_weights)

    def forward(self, x, f0=None):
        if f0 is not None:
            # harmonic-source signal, noise-source signal, uv flag
            f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2)
            har_source, noi_source, uv = self.m_source(f0)
            har_source = har_source.transpose(1, 2)

        x = self.conv_pre(x)
        for i in range(self.num_upsamples):
            x = F.leaky_relu(x, LRELU_SLOPE)
            x = self.ups[i](x)
            if f0 is not None:
                x_source = self.noise_convs[i](har_source)
                x = x + x_source
            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, use_cond=False, c_in=1):
        super(DiscriminatorP, self).__init__()
        self.use_cond = use_cond
        if use_cond:
            from utils.hparams import hparams
            t = hparams['hop_size']
            self.cond_net = torch.nn.ConvTranspose1d(80, 1, t * 2, stride=t, padding=t // 2)
            c_in = 2

        self.period = period
        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
        self.convs = nn.ModuleList([
            norm_f(Conv2d(c_in, 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, mel):
        fmap = []
        if self.use_cond:
            x_mel = self.cond_net(mel)
            x = torch.cat([x_mel, x], 1)
        # 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, use_cond=False, c_in=1):
        super(MultiPeriodDiscriminator, self).__init__()
        self.discriminators = nn.ModuleList([
            DiscriminatorP(2, use_cond=use_cond, c_in=c_in),
            DiscriminatorP(3, use_cond=use_cond, c_in=c_in),
            DiscriminatorP(5, use_cond=use_cond, c_in=c_in),
            DiscriminatorP(7, use_cond=use_cond, c_in=c_in),
            DiscriminatorP(11, use_cond=use_cond, c_in=c_in),
        ])

    def forward(self, y, y_hat, mel=None):
        y_d_rs = []
        y_d_gs = []
        fmap_rs = []
        fmap_gs = []
        for i, d in enumerate(self.discriminators):
            y_d_r, fmap_r = d(y, mel)
            y_d_g, fmap_g = d(y_hat, mel)
            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, use_cond=False, upsample_rates=None, c_in=1):
        super(DiscriminatorS, self).__init__()
        self.use_cond = use_cond
        if use_cond:
            t = np.prod(upsample_rates)
            self.cond_net = torch.nn.ConvTranspose1d(80, 1, t * 2, stride=t, padding=t // 2)
            c_in = 2
        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
        self.convs = nn.ModuleList([
            norm_f(Conv1d(c_in, 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, mel):
        if self.use_cond:
            x_mel = self.cond_net(mel)
            x = torch.cat([x_mel, x], 1)
        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, use_cond=False, c_in=1):
        super(MultiScaleDiscriminator, self).__init__()
        from utils.hparams import hparams
        self.discriminators = nn.ModuleList([
            DiscriminatorS(use_spectral_norm=True, use_cond=use_cond,
                           upsample_rates=[4, 4, hparams['hop_size'] // 16],
                           c_in=c_in),
            DiscriminatorS(use_cond=use_cond,
                           upsample_rates=[4, 4, hparams['hop_size'] // 32],
                           c_in=c_in),
            DiscriminatorS(use_cond=use_cond,
                           upsample_rates=[4, 4, hparams['hop_size'] // 64],
                           c_in=c_in),
        ])
        self.meanpools = nn.ModuleList([
            AvgPool1d(4, 2, padding=1),
            AvgPool1d(4, 2, padding=1)
        ])

    def forward(self, y, y_hat, mel=None):
        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, mel)
            y_d_g, fmap_g = d(y_hat, mel)
            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):
    r_losses = 0
    g_losses = 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)
        r_losses += r_loss
        g_losses += g_loss
    r_losses = r_losses / len(disc_real_outputs)
    g_losses = g_losses / len(disc_real_outputs)
    return r_losses, g_losses


def cond_discriminator_loss(outputs):
    loss = 0
    for dg in outputs:
        g_loss = torch.mean(dg ** 2)
        loss += g_loss
    loss = loss / len(outputs)
    return loss


def generator_loss(disc_outputs):
    loss = 0
    for dg in disc_outputs:
        l = torch.mean((1 - dg) ** 2)
        loss += l
    loss = loss / len(disc_outputs)
    return loss