import torch
import torch.nn as nn
import torch.nn.functional as F
from audiotools import AudioSignal
from audiotools import ml
from audiotools import STFTParams
from einops import rearrange
from torch.nn.utils import weight_norm
def WNConv1d(*args, **kwargs):
act = kwargs.pop("act", True)
conv = weight_norm(nn.Conv1d(*args, **kwargs))
if not act:
return conv
return nn.Sequential(conv, nn.LeakyReLU(0.1))
def WNConv2d(*args, **kwargs):
act = kwargs.pop("act", True)
conv = weight_norm(nn.Conv2d(*args, **kwargs))
if not act:
return conv
return nn.Sequential(conv, nn.LeakyReLU(0.1))
class MPD(nn.Module):
def __init__(self, period):
super().__init__()
self.period = period
self.convs = nn.ModuleList(
[
WNConv2d(1, 32, (5, 1), (3, 1), padding=(2, 0)),
WNConv2d(32, 128, (5, 1), (3, 1), padding=(2, 0)),
WNConv2d(128, 512, (5, 1), (3, 1), padding=(2, 0)),
WNConv2d(512, 1024, (5, 1), (3, 1), padding=(2, 0)),
WNConv2d(1024, 1024, (5, 1), 1, padding=(2, 0)),
]
)
self.conv_post = WNConv2d(
1024, 1, kernel_size=(3, 1), padding=(1, 0), act=False
)
def pad_to_period(self, x):
t = x.shape[-1]
x = F.pad(x, (0, self.period - t % self.period), mode="reflect")
return x
def forward(self, x):
fmap = []
x = self.pad_to_period(x)
x = rearrange(x, "b c (l p) -> b c l p", p=self.period)
for layer in self.convs:
x = layer(x)
fmap.append(x)
x = self.conv_post(x)
fmap.append(x)
return fmap
class MSD(nn.Module):
def __init__(self, rate: int = 1, sample_rate: int = 44100):
super().__init__()
self.convs = nn.ModuleList(
[
WNConv1d(1, 16, 15, 1, padding=7),
WNConv1d(16, 64, 41, 4, groups=4, padding=20),
WNConv1d(64, 256, 41, 4, groups=16, padding=20),
WNConv1d(256, 1024, 41, 4, groups=64, padding=20),
WNConv1d(1024, 1024, 41, 4, groups=256, padding=20),
WNConv1d(1024, 1024, 5, 1, padding=2),
]
)
self.conv_post = WNConv1d(1024, 1, 3, 1, padding=1, act=False)
self.sample_rate = sample_rate
self.rate = rate
def forward(self, x):
x = AudioSignal(x, self.sample_rate)
x.resample(self.sample_rate // self.rate)
x = x.audio_data
fmap = []
for l in self.convs:
x = l(x)
fmap.append(x)
x = self.conv_post(x)
fmap.append(x)
return fmap
BANDS = [(0.0, 0.1), (0.1, 0.25), (0.25, 0.5), (0.5, 0.75), (0.75, 1.0)]
class MRD(nn.Module):
def __init__(
self,
window_length: int,
hop_factor: float = 0.25,
sample_rate: int = 44100,
bands: list = BANDS,
):
"""Complex multi-band spectrogram discriminator.
Parameters
----------
window_length : int
Window length of STFT.
hop_factor : float, optional
Hop factor of the STFT, defaults to ``0.25 * window_length``.
sample_rate : int, optional
Sampling rate of audio in Hz, by default 44100
bands : list, optional
Bands to run discriminator over.
"""
super().__init__()
self.window_length = window_length
self.hop_factor = hop_factor
self.sample_rate = sample_rate
self.stft_params = STFTParams(
window_length=window_length,
hop_length=int(window_length * hop_factor),
match_stride=True,
)
n_fft = window_length // 2 + 1
bands = [(int(b[0] * n_fft), int(b[1] * n_fft)) for b in bands]
self.bands = bands
ch = 32
convs = lambda: nn.ModuleList(
[
WNConv2d(2, ch, (3, 9), (1, 1), padding=(1, 4)),
WNConv2d(ch, ch, (3, 9), (1, 2), padding=(1, 4)),
WNConv2d(ch, ch, (3, 9), (1, 2), padding=(1, 4)),
WNConv2d(ch, ch, (3, 9), (1, 2), padding=(1, 4)),
WNConv2d(ch, ch, (3, 3), (1, 1), padding=(1, 1)),
]
)
self.band_convs = nn.ModuleList([convs() for _ in range(len(self.bands))])
self.conv_post = WNConv2d(ch, 1, (3, 3), (1, 1), padding=(1, 1), act=False)
def spectrogram(self, x):
x = AudioSignal(x, self.sample_rate, stft_params=self.stft_params)
x = torch.view_as_real(x.stft())
x = rearrange(x, "b 1 f t c -> (b 1) 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):
x_bands = self.spectrogram(x)
fmap = []
x = []
for band, stack in zip(x_bands, self.band_convs):
for layer in stack:
band = layer(band)
fmap.append(band)
x.append(band)
x = torch.cat(x, dim=-1)
x = self.conv_post(x)
fmap.append(x)
return fmap
class Discriminator(ml.BaseModel):
def __init__(
self,
rates: list = [],
periods: list = [2, 3, 5, 7, 11],
fft_sizes: list = [2048, 1024, 512],
sample_rate: int = 44100,
bands: list = BANDS,
):
"""Discriminator that combines multiple discriminators.
Parameters
----------
rates : list, optional
sampling rates (in Hz) to run MSD at, by default []
If empty, MSD is not used.
periods : list, optional
periods (of samples) to run MPD at, by default [2, 3, 5, 7, 11]
fft_sizes : list, optional
Window sizes of the FFT to run MRD at, by default [2048, 1024, 512]
sample_rate : int, optional
Sampling rate of audio in Hz, by default 44100
bands : list, optional
Bands to run MRD at, by default `BANDS`
"""
super().__init__()
discs = []
discs += [MPD(p) for p in periods]
discs += [MSD(r, sample_rate=sample_rate) for r in rates]
discs += [MRD(f, sample_rate=sample_rate, bands=bands) for f in fft_sizes]
self.discriminators = nn.ModuleList(discs)
def preprocess(self, y):
# Remove DC offset
y = y - y.mean(dim=-1, keepdims=True)
# Peak normalize the volume of input audio
y = 0.8 * y / (y.abs().max(dim=-1, keepdim=True)[0] + 1e-9)
return y
def forward(self, x):
x = self.preprocess(x)
fmaps = [d(x) for d in self.discriminators]
return fmaps
if __name__ == "__main__":
disc = Discriminator()
x = torch.zeros(1, 1, 44100)
results = disc(x)
for i, result in enumerate(results):
print(f"disc{i}")
for i, r in enumerate(result):
print(r.shape, r.mean(), r.min(), r.max())
print()