vocoder/fregan/discriminator.py

import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn import Conv1d, AvgPool1d, Conv2d
from torch.nn.utils import weight_norm, spectral_norm
from vocoder.fregan.utils import get_padding
from vocoder.fregan.stft_loss import stft
from vocoder.fregan.dwt import DWT_1D
LRELU_SLOPE = 0.1



class SpecDiscriminator(nn.Module):
    """docstring for Discriminator."""

    def __init__(self, fft_size=1024, shift_size=120, win_length=600, window="hann_window", use_spectral_norm=False):
        super(SpecDiscriminator, self).__init__()
        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
        self.fft_size = fft_size
        self.shift_size = shift_size
        self.win_length = win_length
        self.window = getattr(torch, window)(win_length)
        self.discriminators = nn.ModuleList([
            norm_f(nn.Conv2d(1, 32, kernel_size=(3, 9), padding=(1, 4))),
            norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1,2), padding=(1, 4))),
            norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1,2), padding=(1, 4))),
            norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1,2), padding=(1, 4))),
            norm_f(nn.Conv2d(32, 32, kernel_size=(3, 3), stride=(1,1), padding=(1, 1))),
        ])

        self.out = norm_f(nn.Conv2d(32, 1, 3, 1, 1))

    def forward(self, y):

        fmap = []
        with torch.no_grad():
            y = y.squeeze(1)
            y = stft(y, self.fft_size, self.shift_size, self.win_length, self.window.to(y.get_device()))
        y = y.unsqueeze(1)
        for i, d in enumerate(self.discriminators):
            y = d(y)
            y = F.leaky_relu(y, LRELU_SLOPE)
            fmap.append(y)

        y = self.out(y)
        fmap.append(y)

        return torch.flatten(y, 1, -1), fmap

class MultiResSpecDiscriminator(torch.nn.Module):

    def __init__(self,
                 fft_sizes=[1024, 2048, 512],
                 hop_sizes=[120, 240, 50],
                 win_lengths=[600, 1200, 240],
                 window="hann_window"):

        super(MultiResSpecDiscriminator, self).__init__()
        self.discriminators = nn.ModuleList([
            SpecDiscriminator(fft_sizes[0], hop_sizes[0], win_lengths[0], window),
            SpecDiscriminator(fft_sizes[1], hop_sizes[1], win_lengths[1], window),
            SpecDiscriminator(fft_sizes[2], hop_sizes[2], win_lengths[2], window)
            ])

    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 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.dwt1d = DWT_1D()
        self.dwt_conv1 = norm_f(Conv1d(2, 1, 1))
        self.dwt_proj1 = norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0)))
        self.dwt_conv2 = norm_f(Conv1d(4, 1, 1))
        self.dwt_proj2 = norm_f(Conv2d(1, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0)))
        self.dwt_conv3 = norm_f(Conv1d(8, 1, 1))
        self.dwt_proj3 = norm_f(Conv2d(1, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0)))
        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 = []

        # DWT 1
        x_d1_high1, x_d1_low1 = self.dwt1d(x)
        x_d1 = self.dwt_conv1(torch.cat([x_d1_high1, x_d1_low1], dim=1))
        # 1d to 2d
        b, c, t = x_d1.shape
        if t % self.period != 0:  # pad first
            n_pad = self.period - (t % self.period)
            x_d1 = F.pad(x_d1, (0, n_pad), "reflect")
            t = t + n_pad
        x_d1 = x_d1.view(b, c, t // self.period, self.period)

        x_d1 = self.dwt_proj1(x_d1)

        # DWT 2
        x_d2_high1, x_d2_low1 = self.dwt1d(x_d1_high1)
        x_d2_high2, x_d2_low2 = self.dwt1d(x_d1_low1)
        x_d2 = self.dwt_conv2(torch.cat([x_d2_high1, x_d2_low1, x_d2_high2, x_d2_low2], dim=1))
        # 1d to 2d
        b, c, t = x_d2.shape
        if t % self.period != 0:  # pad first
            n_pad = self.period - (t % self.period)
            x_d2 = F.pad(x_d2, (0, n_pad), "reflect")
            t = t + n_pad
        x_d2 = x_d2.view(b, c, t // self.period, self.period)

        x_d2 = self.dwt_proj2(x_d2)

        # DWT 3

        x_d3_high1, x_d3_low1 = self.dwt1d(x_d2_high1)
        x_d3_high2, x_d3_low2 = self.dwt1d(x_d2_low1)
        x_d3_high3, x_d3_low3 = self.dwt1d(x_d2_high2)
        x_d3_high4, x_d3_low4 = self.dwt1d(x_d2_low2)
        x_d3 = self.dwt_conv3(
            torch.cat([x_d3_high1, x_d3_low1, x_d3_high2, x_d3_low2, x_d3_high3, x_d3_low3, x_d3_high4, x_d3_low4],
                      dim=1))
        # 1d to 2d
        b, c, t = x_d3.shape
        if t % self.period != 0:  # pad first
            n_pad = self.period - (t % self.period)
            x_d3 = F.pad(x_d3, (0, n_pad), "reflect")
            t = t + n_pad
        x_d3 = x_d3.view(b, c, t // self.period, self.period)

        x_d3 = self.dwt_proj3(x_d3)

        # 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)
        i = 0
        for l in self.convs:
            x = l(x)
            x = F.leaky_relu(x, LRELU_SLOPE)

            fmap.append(x)
            if i == 0:
                x = torch.cat([x, x_d1], dim=2)
            elif i == 1:
                x = torch.cat([x, x_d2], dim=2)
            elif i == 2:
                x = torch.cat([x, x_d3], dim=2)
            else:
                x = x
            i = i + 1
        x = self.conv_post(x)
        fmap.append(x)
        x = torch.flatten(x, 1, -1)

        return x, fmap


class ResWiseMultiPeriodDiscriminator(torch.nn.Module):
    def __init__(self):
        super(ResWiseMultiPeriodDiscriminator, 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.dwt1d = DWT_1D()
        self.dwt_conv1 = norm_f(Conv1d(2, 128, 15, 1, padding=7))
        self.dwt_conv2 = norm_f(Conv1d(4, 128, 41, 2, padding=20))
        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 = []

        # DWT 1
        x_d1_high1, x_d1_low1 = self.dwt1d(x)
        x_d1 = self.dwt_conv1(torch.cat([x_d1_high1, x_d1_low1], dim=1))

        # DWT 2
        x_d2_high1, x_d2_low1 = self.dwt1d(x_d1_high1)
        x_d2_high2, x_d2_low2 = self.dwt1d(x_d1_low1)
        x_d2 = self.dwt_conv2(torch.cat([x_d2_high1, x_d2_low1, x_d2_high2, x_d2_low2], dim=1))

        i = 0
        for l in self.convs:
            x = l(x)
            x = F.leaky_relu(x, LRELU_SLOPE)
            fmap.append(x)
            if i == 0:
                x = torch.cat([x, x_d1], dim=2)
            if i == 1:
                x = torch.cat([x, x_d2], dim=2)
            i = i + 1
        x = self.conv_post(x)
        fmap.append(x)
        x = torch.flatten(x, 1, -1)

        return x, fmap


class ResWiseMultiScaleDiscriminator(torch.nn.Module):
    def __init__(self, use_spectral_norm=False):
        super(ResWiseMultiScaleDiscriminator, self).__init__()
        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
        self.dwt1d = DWT_1D()
        self.dwt_conv1 = norm_f(Conv1d(2, 1, 1))
        self.dwt_conv2 = norm_f(Conv1d(4, 1, 1))
        self.discriminators = nn.ModuleList([
            DiscriminatorS(use_spectral_norm=True),
            DiscriminatorS(),
            DiscriminatorS(),
        ])

    def forward(self, y, y_hat):
        y_d_rs = []
        y_d_gs = []
        fmap_rs = []
        fmap_gs = []
        # DWT 1
        y_hi, y_lo = self.dwt1d(y)
        y_1 = self.dwt_conv1(torch.cat([y_hi, y_lo], dim=1))
        x_d1_high1, x_d1_low1 = self.dwt1d(y_hat)
        y_hat_1 = self.dwt_conv1(torch.cat([x_d1_high1, x_d1_low1], dim=1))

        # DWT 2
        x_d2_high1, x_d2_low1 = self.dwt1d(y_hi)
        x_d2_high2, x_d2_low2 = self.dwt1d(y_lo)
        y_2 = self.dwt_conv2(torch.cat([x_d2_high1, x_d2_low1, x_d2_high2, x_d2_low2], dim=1))

        x_d2_high1, x_d2_low1 = self.dwt1d(x_d1_high1)
        x_d2_high2, x_d2_low2 = self.dwt1d(x_d1_low1)
        y_hat_2 = self.dwt_conv2(torch.cat([x_d2_high1, x_d2_low1, x_d2_high2, x_d2_low2], dim=1))

        for i, d in enumerate(self.discriminators):

            if i == 1:
                y = y_1
                y_hat = y_hat_1
            if i == 2:
                y = y_2
                y_hat = y_hat_2

            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