fish-speech/fish_speech/models/vqgan/modules/discriminator.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils import spectral_norm, weight_norm

from fish_speech.models.vqgan.modules.modules import LRELU_SLOPE
from fish_speech.models.vqgan.utils import get_padding


class DiscriminatorP(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(
                    nn.Conv2d(
                        1,
                        32,
                        (kernel_size, 1),
                        (stride, 1),
                        padding=(get_padding(kernel_size, 1), 0),
                    )
                ),
                norm_f(
                    nn.Conv2d(
                        32,
                        128,
                        (kernel_size, 1),
                        (stride, 1),
                        padding=(get_padding(kernel_size, 1), 0),
                    )
                ),
                norm_f(
                    nn.Conv2d(
                        128,
                        512,
                        (kernel_size, 1),
                        (stride, 1),
                        padding=(get_padding(kernel_size, 1), 0),
                    )
                ),
                norm_f(
                    nn.Conv2d(
                        512,
                        1024,
                        (kernel_size, 1),
                        (stride, 1),
                        padding=(get_padding(kernel_size, 1), 0),
                    )
                ),
                norm_f(
                    nn.Conv2d(
                        1024,
                        1024,
                        (kernel_size, 1),
                        1,
                        padding=(get_padding(kernel_size, 1), 0),
                    )
                ),
            ]
        )
        self.conv_post = norm_f(nn.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 DiscriminatorS(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(nn.Conv1d(1, 128, 15, 1, padding=7)),
                norm_f(nn.Conv1d(128, 128, 41, 2, groups=4, padding=20)),
                norm_f(nn.Conv1d(128, 256, 41, 2, groups=16, padding=20)),
                norm_f(nn.Conv1d(256, 512, 41, 4, groups=16, padding=20)),
                norm_f(nn.Conv1d(512, 1024, 41, 4, groups=16, padding=20)),
                norm_f(nn.Conv1d(1024, 1024, 41, 1, groups=16, padding=20)),
                norm_f(nn.Conv1d(1024, 1024, 5, 1, padding=2)),
            ]
        )
        self.conv_post = norm_f(nn.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 EnsembleDiscriminator(nn.Module):
    def __init__(self, ckpt_path=None):
        super(EnsembleDiscriminator, self).__init__()
        periods = [2, 3, 5, 7, 11]  # [1, 2, 3, 5, 7, 11]

        discs = [DiscriminatorS(use_spectral_norm=True)]
        discs = discs + [DiscriminatorP(i, use_spectral_norm=False) for i in periods]
        self.discriminators = nn.ModuleList(discs)

        if ckpt_path is not None:
            self.restore_from_ckpt(ckpt_path)

    def restore_from_ckpt(self, ckpt_path):
        ckpt = torch.load(ckpt_path, map_location="cpu")
        mpd, msd = ckpt["mpd"], ckpt["msd"]

        all_keys = {}
        for k, v in mpd.items():
            keys = k.split(".")
            keys[1] = str(int(keys[1]) + 1)
            all_keys[".".join(keys)] = v

        for k, v in msd.items():
            if not k.startswith("discriminators.0"):
                continue
            all_keys[k] = v

        self.load_state_dict(all_keys, strict=True)

    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)
            y_d_gs.append(y_d_g)
            fmap_rs.append(fmap_r)
            fmap_gs.append(fmap_g)

        return y_d_rs, y_d_gs, fmap_rs, fmap_gs


if __name__ == "__main__":
    m = EnsembleDiscriminator(
        ckpt_path="checkpoints/hifigan-v1-universal-22050/do_02500000"
    )