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# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn.functional as F
import torch.nn as nn
from torch import nn
from modules.vocoder_blocks import *
from einops import rearrange
import torchaudio.transforms as T
from nnAudio import features
LRELU_SLOPE = 0.1
class DiscriminatorCQT(nn.Module):
def __init__(self, cfg, hop_length, n_octaves, bins_per_octave):
super(DiscriminatorCQT, self).__init__()
self.cfg = cfg
self.filters = cfg.model.mssbcqtd.filters
self.max_filters = cfg.model.mssbcqtd.max_filters
self.filters_scale = cfg.model.mssbcqtd.filters_scale
self.kernel_size = (3, 9)
self.dilations = cfg.model.mssbcqtd.dilations
self.stride = (1, 2)
self.in_channels = cfg.model.mssbcqtd.in_channels
self.out_channels = cfg.model.mssbcqtd.out_channels
self.fs = cfg.preprocess.sample_rate
self.hop_length = hop_length
self.n_octaves = n_octaves
self.bins_per_octave = bins_per_octave
self.cqt_transform = features.cqt.CQT2010v2(
sr=self.fs * 2,
hop_length=self.hop_length,
n_bins=self.bins_per_octave * self.n_octaves,
bins_per_octave=self.bins_per_octave,
output_format="Complex",
pad_mode="constant",
)
self.conv_pres = nn.ModuleList()
for i in range(self.n_octaves):
self.conv_pres.append(
NormConv2d(
self.in_channels * 2,
self.in_channels * 2,
kernel_size=self.kernel_size,
padding=get_2d_padding(self.kernel_size),
)
)
self.convs = nn.ModuleList()
self.convs.append(
NormConv2d(
self.in_channels * 2,
self.filters,
kernel_size=self.kernel_size,
padding=get_2d_padding(self.kernel_size),
)
)
in_chs = min(self.filters_scale * self.filters, self.max_filters)
for i, dilation in enumerate(self.dilations):
out_chs = min(
(self.filters_scale ** (i + 1)) * self.filters, self.max_filters
)
self.convs.append(
NormConv2d(
in_chs,
out_chs,
kernel_size=self.kernel_size,
stride=self.stride,
dilation=(dilation, 1),
padding=get_2d_padding(self.kernel_size, (dilation, 1)),
norm="weight_norm",
)
)
in_chs = out_chs
out_chs = min(
(self.filters_scale ** (len(self.dilations) + 1)) * self.filters,
self.max_filters,
)
self.convs.append(
NormConv2d(
in_chs,
out_chs,
kernel_size=(self.kernel_size[0], self.kernel_size[0]),
padding=get_2d_padding((self.kernel_size[0], self.kernel_size[0])),
norm="weight_norm",
)
)
self.conv_post = NormConv2d(
out_chs,
self.out_channels,
kernel_size=(self.kernel_size[0], self.kernel_size[0]),
padding=get_2d_padding((self.kernel_size[0], self.kernel_size[0])),
norm="weight_norm",
)
self.activation = torch.nn.LeakyReLU(negative_slope=LRELU_SLOPE)
self.resample = T.Resample(orig_freq=self.fs, new_freq=self.fs * 2)
def forward(self, x):
fmap = []
x = self.resample(x)
z = self.cqt_transform(x)
z_amplitude = z[:, :, :, 0].unsqueeze(1)
z_phase = z[:, :, :, 1].unsqueeze(1)
z = torch.cat([z_amplitude, z_phase], dim=1)
z = rearrange(z, "b c w t -> b c t w")
latent_z = []
for i in range(self.n_octaves):
latent_z.append(
self.conv_pres[i](
z[
:,
:,
:,
i * self.bins_per_octave : (i + 1) * self.bins_per_octave,
]
)
)
latent_z = torch.cat(latent_z, dim=-1)
for i, l in enumerate(self.convs):
latent_z = l(latent_z)
latent_z = self.activation(latent_z)
fmap.append(latent_z)
latent_z = self.conv_post(latent_z)
return latent_z, fmap
class MultiScaleSubbandCQTDiscriminator(nn.Module):
def __init__(self, cfg):
super(MultiScaleSubbandCQTDiscriminator, self).__init__()
self.cfg = cfg
self.discriminators = nn.ModuleList(
[
DiscriminatorCQT(
cfg,
hop_length=cfg.model.mssbcqtd.hop_lengths[i],
n_octaves=cfg.model.mssbcqtd.n_octaves[i],
bins_per_octave=cfg.model.mssbcqtd.bins_per_octaves[i],
)
for i in range(len(cfg.model.mssbcqtd.hop_lengths))
]
)
def forward(self, y, y_hat):
y_d_rs = []
y_d_gs = []
fmap_rs = []
fmap_gs = []
for disc in self.discriminators:
y_d_r, fmap_r = disc(y)
y_d_g, fmap_g = disc(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
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