models.py

# Copyright (c) 2022 NVIDIA CORPORATION. 
#   Licensed under the MIT license.

# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
#   LICENSE is in incl_licenses directory.


import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn import Conv1d, ConvTranspose1d, Conv2d
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm

import activations
from san_modules import SANConv2d
from utils import init_weights, get_padding
from alias_free_torch import *

LRELU_SLOPE = 0.1


class AMPBlock1(torch.nn.Module):
    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5), activation=None):
        super(AMPBlock1, 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)

        self.num_layers = len(self.convs1) + len(self.convs2) # total number of conv layers

        if activation == 'snake': # periodic nonlinearity with snake function and anti-aliasing
            self.activations = nn.ModuleList([
                Activation1d(
                    activation=activations.Snake(channels, alpha_logscale=h.snake_logscale))
                for _ in range(self.num_layers)
            ])
        elif activation == 'snakebeta': # periodic nonlinearity with snakebeta function and anti-aliasing
            self.activations = nn.ModuleList([
                Activation1d(
                    activation=activations.SnakeBeta(channels, alpha_logscale=h.snake_logscale))
                 for _ in range(self.num_layers)
            ])
        else:
            raise NotImplementedError("activation incorrectly specified. check the config file and look for 'activation'.")

    def forward(self, x):
        acts1, acts2 = self.activations[::2], self.activations[1::2]
        for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2):
            xt = a1(x)
            xt = c1(xt)
            xt = a2(xt)
            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 AMPBlock2(torch.nn.Module):
    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3), activation=None):
        super(AMPBlock2, 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)

        self.num_layers = len(self.convs) # total number of conv layers

        if activation == 'snake': # periodic nonlinearity with snake function and anti-aliasing
            self.activations = nn.ModuleList([
                Activation1d(
                    activation=activations.Snake(channels, alpha_logscale=h.snake_logscale))
                for _ in range(self.num_layers)
            ])
        elif activation == 'snakebeta': # periodic nonlinearity with snakebeta function and anti-aliasing
            self.activations = nn.ModuleList([
                Activation1d(
                    activation=activations.SnakeBeta(channels, alpha_logscale=h.snake_logscale))
                 for _ in range(self.num_layers)
            ])
        else:
            raise NotImplementedError("activation incorrectly specified. check the config file and look for 'activation'.")

    def forward(self, x):
        for c, a in zip (self.convs, self.activations):
            xt = a(x)
            xt = c(xt)
            x = xt + x

        return x

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


class BigVSAN(torch.nn.Module):
    # this is our main BigVSAN model. Applies anti-aliased periodic activation for resblocks.
    def __init__(self, h):
        super(BigVSAN, self).__init__()
        self.h = h

        self.num_kernels = len(h.resblock_kernel_sizes)
        self.num_upsamples = len(h.upsample_rates)

        # pre conv
        self.conv_pre = weight_norm(Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3))

        # define which AMPBlock to use. BigVSAN uses AMPBlock1 as default
        resblock = AMPBlock1 if h.resblock == '1' else AMPBlock2

        # transposed conv-based upsamplers. does not apply anti-aliasing
        self.ups = nn.ModuleList()
        for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
            self.ups.append(nn.ModuleList([
                weight_norm(ConvTranspose1d(h.upsample_initial_channel // (2 ** i),
                                            h.upsample_initial_channel // (2 ** (i + 1)),
                                            k, u, padding=(k - u) // 2))
            ]))

        # residual blocks using anti-aliased multi-periodicity composition modules (AMP)
        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, activation=h.activation))

        # post conv
        if h.activation == "snake": # periodic nonlinearity with snake function and anti-aliasing
            activation_post = activations.Snake(ch, alpha_logscale=h.snake_logscale)
            self.activation_post = Activation1d(activation=activation_post)
        elif h.activation == "snakebeta": # periodic nonlinearity with snakebeta function and anti-aliasing
            activation_post = activations.SnakeBeta(ch, alpha_logscale=h.snake_logscale)
            self.activation_post = Activation1d(activation=activation_post)
        else:
            raise NotImplementedError("activation incorrectly specified. check the config file and look for 'activation'.")

        self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))

        # weight initialization
        for i in range(len(self.ups)):
            self.ups[i].apply(init_weights)
        self.conv_post.apply(init_weights)

    def forward(self, x):
        # pre conv
        x = self.conv_pre(x)

        for i in range(self.num_upsamples):
            # upsampling
            for i_up in range(len(self.ups[i])):
                x = self.ups[i][i_up](x)
            # AMP blocks
            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

        # post conv
        x = self.activation_post(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:
            for l_i in l:
                remove_weight_norm(l_i)
        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, h, period, kernel_size=5, stride=3, use_spectral_norm=False):
        super(DiscriminatorP, self).__init__()
        self.period = period
        self.d_mult = h.discriminator_channel_mult
        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
        self.convs = nn.ModuleList([
            norm_f(Conv2d(1, int(32*self.d_mult), (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
            norm_f(Conv2d(int(32*self.d_mult), int(128*self.d_mult), (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
            norm_f(Conv2d(int(128*self.d_mult), int(512*self.d_mult), (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
            norm_f(Conv2d(int(512*self.d_mult), int(1024*self.d_mult), (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
            norm_f(Conv2d(int(1024*self.d_mult), int(1024*self.d_mult), (kernel_size, 1), 1, padding=(2, 0))),
        ])
        self.conv_post = SANConv2d(int(1024*self.d_mult), 1, (3, 1), 1, padding=(1, 0))

    def forward(self, x, flg_train=False):
        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, flg_train=flg_train)
        if flg_train:
            x_fun, x_dir = x
            fmap.append(x_fun)
            x_fun = torch.flatten(x_fun, 1, -1)
            x_dir = torch.flatten(x_dir, 1, -1)
            x = [x_fun, x_dir]
        else:
            fmap.append(x)
            x = torch.flatten(x, 1, -1)

        return x, fmap


class MultiPeriodDiscriminator(torch.nn.Module):
    def __init__(self, h):
        super(MultiPeriodDiscriminator, self).__init__()
        self.mpd_reshapes = h.mpd_reshapes
        print("mpd_reshapes: {}".format(self.mpd_reshapes))
        discriminators = [DiscriminatorP(h, rs, use_spectral_norm=h.use_spectral_norm) for rs in self.mpd_reshapes]
        self.discriminators = nn.ModuleList(discriminators)

    def forward(self, y, y_hat, flg_train=False):
        y_d_rs = []
        y_d_gs = []
        fmap_rs = []
        fmap_gs = []
        for i, d in enumerate(self.discriminators):
            y_d_r, fmap_r = d(y, flg_train=flg_train)
            y_d_g, fmap_g = d(y_hat, flg_train=flg_train)
            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 DiscriminatorR(nn.Module):
    def __init__(self, cfg, resolution):
        super().__init__()

        self.resolution = resolution
        assert len(self.resolution) == 3, \
            "MRD layer requires list with len=3, got {}".format(self.resolution)
        self.lrelu_slope = LRELU_SLOPE

        norm_f = weight_norm if cfg.use_spectral_norm == False else spectral_norm
        if hasattr(cfg, "mrd_use_spectral_norm"):
            print("INFO: overriding MRD use_spectral_norm as {}".format(cfg.mrd_use_spectral_norm))
            norm_f = weight_norm if cfg.mrd_use_spectral_norm == False else spectral_norm
        self.d_mult = cfg.discriminator_channel_mult
        if hasattr(cfg, "mrd_channel_mult"):
            print("INFO: overriding mrd channel multiplier as {}".format(cfg.mrd_channel_mult))
            self.d_mult = cfg.mrd_channel_mult

        self.convs = nn.ModuleList([
            norm_f(nn.Conv2d(1, int(32*self.d_mult), (3, 9), padding=(1, 4))),
            norm_f(nn.Conv2d(int(32*self.d_mult), int(32*self.d_mult), (3, 9), stride=(1, 2), padding=(1, 4))),
            norm_f(nn.Conv2d(int(32*self.d_mult), int(32*self.d_mult), (3, 9), stride=(1, 2), padding=(1, 4))),
            norm_f(nn.Conv2d(int(32*self.d_mult), int(32*self.d_mult), (3, 9), stride=(1, 2), padding=(1, 4))),
            norm_f(nn.Conv2d(int(32*self.d_mult), int(32*self.d_mult), (3, 3), padding=(1, 1))),
        ])
        self.conv_post = SANConv2d(int(32 * self.d_mult), 1, (3, 3), padding=(1, 1))

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

        x = self.spectrogram(x)
        x = x.unsqueeze(1)
        for l in self.convs:
            x = l(x)
            x = F.leaky_relu(x, self.lrelu_slope)
            fmap.append(x)
        x = self.conv_post(x, flg_train=flg_train)
        if flg_train:
            x_fun, x_dir = x
            fmap.append(x_fun)
            x_fun = torch.flatten(x_fun, 1, -1)
            x_dir = torch.flatten(x_dir, 1, -1)
            x = [x_fun, x_dir]
        else:
            fmap.append(x)
            x = torch.flatten(x, 1, -1)

        return x, fmap

    def spectrogram(self, x):
        n_fft, hop_length, win_length = self.resolution
        x = F.pad(x, (int((n_fft - hop_length) / 2), int((n_fft - hop_length) / 2)), mode='reflect')
        x = x.squeeze(1)
        x = torch.stft(x, n_fft=n_fft, hop_length=hop_length, win_length=win_length, center=False, return_complex=True)
        x = torch.view_as_real(x)  # [B, F, TT, 2]
        mag = torch.norm(x, p=2, dim =-1) #[B, F, TT]

        return mag


class MultiResolutionDiscriminator(nn.Module):
    def __init__(self, cfg, debug=False):
        super().__init__()
        self.resolutions = cfg.resolutions
        assert len(self.resolutions) == 3,\
            "MRD requires list of list with len=3, each element having a list with len=3. got {}".\
                format(self.resolutions)
        self.discriminators = nn.ModuleList(
            [DiscriminatorR(cfg, resolution) for resolution in self.resolutions]
        )

    def forward(self, y, y_hat, flg_train=False):
        y_d_rs = []
        y_d_gs = []
        fmap_rs = []
        fmap_gs = []

        for i, d in enumerate(self.discriminators):
            y_d_r, fmap_r = d(x=y, flg_train=flg_train)
            y_d_g, fmap_g = d(x=y_hat, flg_train=flg_train)
            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 = []
    for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
        dr_fun, dr_dir = dr
        dg_fun, dg_dir = dg
        r_loss_fun = torch.mean(F.softplus(1-dr_fun)**2)
        g_loss_fun = torch.mean(F.softplus(dg_fun)**2)
        r_loss_dir = torch.mean(F.softplus(1-dr_dir)**2)
        g_loss_dir = torch.mean(-F.softplus(1-dg_dir)**2)
        r_loss = r_loss_fun + r_loss_dir
        g_loss = g_loss_fun + g_loss_dir
        loss += (r_loss + g_loss)
        r_losses.append(r_loss.item())
        g_losses.append(g_loss.item())

    return loss, r_losses, g_losses


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

    return loss, gen_losses