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import math
import torch
from torch.nn.utils import remove_weight_norm
from torch.nn.utils.parametrizations import weight_norm
from typing import Optional
from rvc.lib.algorithm.generators import SineGen
from rvc.lib.algorithm.residuals import LRELU_SLOPE, ResBlock1, ResBlock2
from rvc.lib.algorithm.commons import init_weights
class SourceModuleHnNSF(torch.nn.Module):
"""
Source Module for harmonic-plus-noise excitation.
Args:
sample_rate (int): Sampling rate in Hz.
harmonic_num (int, optional): Number of harmonics above F0. Defaults to 0.
sine_amp (float, optional): Amplitude of sine source signal. Defaults to 0.1.
add_noise_std (float, optional): Standard deviation of additive Gaussian noise. Defaults to 0.003.
voiced_threshod (float, optional): Threshold to set voiced/unvoiced given F0. Defaults to 0.
is_half (bool, optional): Whether to use half precision. Defaults to True.
"""
def __init__(
self,
sample_rate,
harmonic_num=0,
sine_amp=0.1,
add_noise_std=0.003,
voiced_threshod=0,
is_half=True,
):
super(SourceModuleHnNSF, self).__init__()
self.sine_amp = sine_amp
self.noise_std = add_noise_std
self.is_half = is_half
self.l_sin_gen = SineGen(
sample_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
)
self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
self.l_tanh = torch.nn.Tanh()
def forward(self, x: torch.Tensor, upp: int = 1):
sine_wavs, uv, _ = self.l_sin_gen(x, upp)
sine_wavs = sine_wavs.to(dtype=self.l_linear.weight.dtype)
sine_merge = self.l_tanh(self.l_linear(sine_wavs))
return sine_merge, None, None
class GeneratorNSF(torch.nn.Module):
"""
Generator for synthesizing audio using the NSF (Neural Source Filter) approach.
Args:
initial_channel (int): Number of channels in the initial convolutional layer.
resblock (str): Type of residual block to use (1 or 2).
resblock_kernel_sizes (list): Kernel sizes of the residual blocks.
resblock_dilation_sizes (list): Dilation rates of the residual blocks.
upsample_rates (list): Upsampling rates.
upsample_initial_channel (int): Number of channels in the initial upsampling layer.
upsample_kernel_sizes (list): Kernel sizes of the upsampling layers.
gin_channels (int): Number of channels for the global conditioning input.
sr (int): Sampling rate.
is_half (bool, optional): Whether to use half precision. Defaults to False.
"""
def __init__(
self,
initial_channel,
resblock,
resblock_kernel_sizes,
resblock_dilation_sizes,
upsample_rates,
upsample_initial_channel,
upsample_kernel_sizes,
gin_channels,
sr,
is_half=False,
):
super(GeneratorNSF, self).__init__()
self.num_kernels = len(resblock_kernel_sizes)
self.num_upsamples = len(upsample_rates)
self.f0_upsamp = torch.nn.Upsample(scale_factor=math.prod(upsample_rates))
self.m_source = SourceModuleHnNSF(
sample_rate=sr, harmonic_num=0, is_half=is_half
)
self.conv_pre = torch.nn.Conv1d(
initial_channel, upsample_initial_channel, 7, 1, padding=3
)
resblock_cls = ResBlock1 if resblock == "1" else ResBlock2
self.ups = torch.nn.ModuleList()
self.noise_convs = torch.nn.ModuleList()
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
current_channel = upsample_initial_channel // (2 ** (i + 1))
self.ups.append(
weight_norm(
torch.nn.ConvTranspose1d(
upsample_initial_channel // (2**i),
current_channel,
k,
u,
padding=(k - u) // 2,
)
)
)
stride_f0 = (
math.prod(upsample_rates[i + 1 :]) if i + 1 < len(upsample_rates) else 1
)
self.noise_convs.append(
torch.nn.Conv1d(
1,
current_channel,
kernel_size=stride_f0 * 2 if stride_f0 > 1 else 1,
stride=stride_f0,
padding=(stride_f0 // 2 if stride_f0 > 1 else 0),
)
)
self.resblocks = torch.nn.ModuleList(
[
resblock_cls(upsample_initial_channel // (2 ** (i + 1)), k, d)
for i in range(len(self.ups))
for k, d in zip(resblock_kernel_sizes, resblock_dilation_sizes)
]
)
self.conv_post = torch.nn.Conv1d(
current_channel, 1, 7, 1, padding=3, bias=False
)
self.ups.apply(init_weights)
if gin_channels != 0:
self.cond = torch.nn.Conv1d(gin_channels, upsample_initial_channel, 1)
self.upp = math.prod(upsample_rates)
self.lrelu_slope = LRELU_SLOPE
def forward(self, x, f0, g: Optional[torch.Tensor] = None):
har_source, _, _ = self.m_source(f0, self.upp)
har_source = har_source.transpose(1, 2)
x = self.conv_pre(x)
if g is not None:
x = x + self.cond(g)
for i, (ups, noise_convs) in enumerate(zip(self.ups, self.noise_convs)):
x = torch.nn.functional.leaky_relu(x, self.lrelu_slope)
x = ups(x)
x = x + noise_convs(har_source)
xs = sum(
[
resblock(x)
for j, resblock in enumerate(self.resblocks)
if j in range(i * self.num_kernels, (i + 1) * self.num_kernels)
]
)
x = xs / self.num_kernels
x = torch.nn.functional.leaky_relu(x)
x = torch.tanh(self.conv_post(x))
return x
def remove_weight_norm(self):
for l in self.ups:
remove_weight_norm(l)
for l in self.resblocks:
l.remove_weight_norm()
def __prepare_scriptable__(self):
for l in self.ups:
for hook in l._forward_pre_hooks.values():
if (
hook.__module__ == "torch.nn.utils.parametrizations.weight_norm"
and hook.__class__.__name__ == "WeightNorm"
):
remove_weight_norm(l)
for l in self.resblocks:
for hook in l._forward_pre_hooks.values():
if (
hook.__module__ == "torch.nn.utils.parametrizations.weight_norm"
and hook.__class__.__name__ == "WeightNorm"
):
remove_weight_norm(l)
return self
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