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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 numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Conv1d, ConvTranspose1d
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
from modules.neural_source_filter import *
from modules.vocoder_blocks import *
LRELU_SLOPE = 0.1
class ResBlock1(nn.Module):
def __init__(self, cfg, channels, kernel_size=3, dilation=(1, 3, 5)):
super(ResBlock1, self).__init__()
self.cfg = cfg
self.convs1 = nn.ModuleList(
[
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation[0],
padding=get_padding(kernel_size, dilation[0]),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation[1],
padding=get_padding(kernel_size, dilation[1]),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation[2],
padding=get_padding(kernel_size, dilation[2]),
)
),
]
)
self.convs1.apply(init_weights)
self.convs2 = nn.ModuleList(
[
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=1,
padding=get_padding(kernel_size, 1),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=1,
padding=get_padding(kernel_size, 1),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=1,
padding=get_padding(kernel_size, 1),
)
),
]
)
self.convs2.apply(init_weights)
def forward(self, x):
for c1, c2 in zip(self.convs1, self.convs2):
xt = F.leaky_relu(x, LRELU_SLOPE)
xt = c1(xt)
xt = F.leaky_relu(xt, LRELU_SLOPE)
xt = c2(xt)
x = xt + x
return x
def remove_weight_norm(self):
for l in self.convs1:
remove_weight_norm(l)
for l in self.convs2:
remove_weight_norm(l)
class ResBlock2(nn.Module):
def __init__(self, cfg, channels, kernel_size=3, dilation=(1, 3)):
super(ResBlock1, self).__init__()
self.cfg = cfg
self.convs = nn.ModuleList(
[
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation[0],
padding=get_padding(kernel_size, dilation[0]),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation[1],
padding=get_padding(kernel_size, dilation[1]),
)
),
]
)
self.convs.apply(init_weights)
def forward(self, x):
for c in self.convs:
xt = F.leaky_relu(x, LRELU_SLOPE)
xt = c(xt)
x = xt + x
return x
def remove_weight_norm(self):
for l in self.convs:
remove_weight_norm(l)
# This NSF Module is adopted from Xin Wang's NSF under the MIT License
# https://github.com/nii-yamagishilab/project-NN-Pytorch-scripts
class SourceModuleHnNSF(nn.Module):
def __init__(
self, fs, harmonic_num=0, amp=0.1, noise_std=0.003, voiced_threshold=0
):
super(SourceModuleHnNSF, self).__init__()
self.amp = amp
self.noise_std = noise_std
self.l_sin_gen = SineGen(fs, harmonic_num, amp, noise_std, voiced_threshold)
self.l_linear = nn.Linear(harmonic_num + 1, 1)
self.l_tanh = nn.Tanh()
def forward(self, x, upp):
sine_wavs, uv, _ = self.l_sin_gen(x, upp)
sine_merge = self.l_tanh(self.l_linear(sine_wavs))
return sine_merge
class NSFHiFiGAN(nn.Module):
def __init__(self, cfg):
super(NSFHiFiGAN, self).__init__()
self.cfg = cfg
self.num_kernels = len(self.cfg.model.nsfhifigan.resblock_kernel_sizes)
self.num_upsamples = len(self.cfg.model.nsfhifigan.upsample_rates)
self.m_source = SourceModuleHnNSF(
fs=self.cfg.preprocess.sample_rate,
harmonic_num=self.cfg.model.nsfhifigan.harmonic_num,
)
self.noise_convs = nn.ModuleList()
self.conv_pre = weight_norm(
Conv1d(
self.cfg.preprocess.n_mel,
self.cfg.model.nsfhifigan.upsample_initial_channel,
7,
1,
padding=3,
)
)
resblock = ResBlock1 if self.cfg.model.nsfhifigan.resblock == "1" else ResBlock2
self.ups = nn.ModuleList()
for i, (u, k) in enumerate(
zip(
self.cfg.model.nsfhifigan.upsample_rates,
self.cfg.model.nsfhifigan.upsample_kernel_sizes,
)
):
c_cur = self.cfg.model.nsfhifigan.upsample_initial_channel // (2 ** (i + 1))
self.ups.append(
weight_norm(
ConvTranspose1d(
self.cfg.model.nsfhifigan.upsample_initial_channel // (2**i),
self.cfg.model.nsfhifigan.upsample_initial_channel
// (2 ** (i + 1)),
k,
u,
padding=(k - u) // 2,
)
)
)
if i + 1 < len(self.cfg.model.nsfhifigan.upsample_rates):
stride_f0 = int(
np.prod(self.cfg.model.nsfhifigan.upsample_rates[i + 1 :])
)
self.noise_convs.append(
Conv1d(
1,
c_cur,
kernel_size=stride_f0 * 2,
stride=stride_f0,
padding=stride_f0 // 2,
)
)
else:
self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
self.resblocks = nn.ModuleList()
ch = self.cfg.model.nsfhifigan.upsample_initial_channel
for i in range(len(self.ups)):
ch //= 2
for j, (k, d) in enumerate(
zip(
self.cfg.model.nsfhifigan.resblock_kernel_sizes,
self.cfg.model.nsfhifigan.resblock_dilation_sizes,
)
):
self.resblocks.append(resblock(cfg, ch, k, d))
self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
self.ups.apply(init_weights)
self.conv_post.apply(init_weights)
self.upp = int(np.prod(self.cfg.model.nsfhifigan.upsample_rates))
def forward(self, x, f0):
har_source = self.m_source(f0, self.upp).transpose(1, 2)
x = self.conv_pre(x)
for i in range(self.num_upsamples):
x = F.leaky_relu(x, LRELU_SLOPE)
x = self.ups[i](x)
x_source = self.noise_convs[i](har_source)
length = min(x.shape[-1], x_source.shape[-1])
x = x[:, :, :length]
x_source = x[:, :, :length]
x = x + x_source
xs = None
for j in range(self.num_kernels):
if xs is None:
xs = self.resblocks[i * self.num_kernels + j](x)
else:
xs += self.resblocks[i * self.num_kernels + j](x)
x = xs / self.num_kernels
x = F.leaky_relu(x)
x = self.conv_post(x)
x = torch.tanh(x)
return x