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from typing import List, Tuple
import torch
from torch import nn
from torch import Tensor
from torch.nn import Conv2d
from torch.nn.utils import weight_norm
from torchaudio.transforms import Spectrogram
class MultiPeriodDiscriminator(nn.Module):
def __init__(self, periods: Tuple[int, ...] = (2, 3, 5, 7, 11)):
super().__init__()
self.discriminators = nn.ModuleList([DiscriminatorP(period=p) for p in periods])
def forward(self, y: Tensor, y_hat: Tensor):
y_d_rs = []
y_d_gs = []
fmap_rs = []
fmap_gs = []
for d in 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(nn.Module):
def __init__(
self,
period: int,
in_channels: int = 1,
kernel_size: int = 5,
stride: int = 3,
lrelu_slope: float = 0.1,
):
super().__init__()
self.period = period
self.convs = nn.ModuleList(
[
weight_norm(Conv2d(in_channels, 32, (kernel_size, 1), (stride, 1), padding=(kernel_size // 2, 0))),
weight_norm(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(kernel_size // 2, 0))),
weight_norm(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(kernel_size // 2, 0))),
weight_norm(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(kernel_size // 2, 0))),
weight_norm(Conv2d(1024, 1024, (kernel_size, 1), (1, 1), padding=(kernel_size // 2, 0))),
]
)
self.conv_post = weight_norm(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
self.lrelu_slope = lrelu_slope
def forward(self, x: Tensor) -> Tuple[Tensor, List[Tensor]]:
fmap = []
# 1d to 2d
b, c, t = x.shape
if t % self.period != 0: # pad first
n_pad = self.period - (t % self.period)
x = torch.nn.functional.pad(x, (0, n_pad), "reflect")
t = t + n_pad
x = x.view(b, c, t // self.period, self.period)
for i, l in enumerate(self.convs):
x = l(x)
x = torch.nn.functional.leaky_relu(x, self.lrelu_slope)
if i > 0:
fmap.append(x)
x = self.conv_post(x)
fmap.append(x)
x = torch.flatten(x, 1, -1)
return x, fmap
class MultiResolutionDiscriminator(nn.Module):
def __init__(
self,
fft_sizes: Tuple[int, ...] = (2048, 1024, 512),
):
"""
Multi-Resolution Discriminator module adapted from https://github.com/descriptinc/descript-audio-codec.
Args:
fft_sizes (tuple[int]): Tuple of window lengths for FFT. Defaults to (2048, 1024, 512).
"""
super().__init__()
self.discriminators = nn.ModuleList(
[DiscriminatorR(window_length=w) for w in fft_sizes]
)
def forward(self, y: Tensor, y_hat: Tensor) -> Tuple[List[Tensor], List[Tensor], List[List[Tensor]], List[List[Tensor]]]:
y_d_rs = []
y_d_gs = []
fmap_rs = []
fmap_gs = []
for d in self.discriminators:
y_d_r, fmap_r = d(x=y)
y_d_g, fmap_g = d(x=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 DiscriminatorR(nn.Module):
def __init__(
self,
window_length: int,
channels: int = 32,
hop_factor: float = 0.25,
bands: Tuple[Tuple[float, float], ...] = ((0.0, 0.1), (0.1, 0.25), (0.25, 0.5), (0.5, 0.75), (0.75, 1.0)),
):
super().__init__()
self.window_length = window_length
self.hop_factor = hop_factor
self.spec_fn = Spectrogram(
n_fft=window_length, hop_length=int(window_length * hop_factor), win_length=window_length, power=None
)
n_fft = window_length // 2 + 1
bands = [(int(b[0] * n_fft), int(b[1] * n_fft)) for b in bands]
self.bands = bands
convs = lambda: nn.ModuleList(
[
weight_norm(nn.Conv2d(2, channels, (3, 9), (1, 1), padding=(1, 4))),
weight_norm(nn.Conv2d(channels, channels, (3, 9), (1, 2), padding=(1, 4))),
weight_norm(nn.Conv2d(channels, channels, (3, 9), (1, 2), padding=(1, 4))),
weight_norm(nn.Conv2d(channels, channels, (3, 9), (1, 2), padding=(1, 4))),
weight_norm(nn.Conv2d(channels, channels, (3, 3), (1, 1), padding=(1, 1))),
]
)
self.band_convs = nn.ModuleList([convs() for _ in range(len(self.bands))])
self.conv_post = weight_norm(nn.Conv2d(channels, 1, (3, 3), (1, 1), padding=(1, 1)))
def spectrogram(self, x):
x = x.squeeze(1)
# Remove DC offset
x = x - x.mean(dim=-1, keepdims=True)
# Peak normalize the volume of input audio
x = 0.8 * x / (x.abs().max(dim=-1, keepdim=True)[0] + 1e-9)
x = self.spec_fn(x)
x = torch.view_as_real(x)
x = x.permute(0, 3, 2, 1) # b f t c -> b c t f
# Split into bands
x_bands = [x[..., b[0] : b[1]] for b in self.bands]
return x_bands
def forward(self, x: Tensor):
x_bands = self.spectrogram(x)
fmap = []
x = []
for band, stack in zip(x_bands, self.band_convs):
for i, layer in enumerate(stack):
band = layer(band)
band = torch.nn.functional.leaky_relu(band, 0.1)
if i > 0:
fmap.append(band)
x.append(band)
x = torch.cat(x, dim=-1)
x = self.conv_post(x)
fmap.append(x)
return x, fmap |