mirror of
https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI.git
synced 2026-06-05 01:10:22 +08:00
218 lines
8.0 KiB
Python
218 lines
8.0 KiB
Python
from typing import Optional, List, Union
|
|
import math
|
|
|
|
import torch
|
|
from torch import nn
|
|
from torch.nn import Conv1d, ConvTranspose1d
|
|
from torch.nn import functional as F
|
|
from torch.nn.utils import remove_weight_norm, weight_norm
|
|
|
|
from .generators import SineGenerator
|
|
from .residuals import ResBlock1, ResBlock2, LRELU_SLOPE
|
|
from .utils import call_weight_data_normal_if_Conv
|
|
|
|
|
|
class SourceModuleHnNSF(torch.nn.Module):
|
|
"""SourceModule for hn-nsf
|
|
SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
|
|
add_noise_std=0.003, voiced_threshod=0)
|
|
sampling_rate: sampling_rate in Hz
|
|
harmonic_num: number of harmonic above F0 (default: 0)
|
|
sine_amp: amplitude of sine source signal (default: 0.1)
|
|
add_noise_std: std of additive Gaussian noise (default: 0.003)
|
|
note that amplitude of noise in unvoiced is decided
|
|
by sine_amp
|
|
voiced_threshold: threhold to set U/V given F0 (default: 0)
|
|
Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
|
|
F0_sampled (batchsize, length, 1)
|
|
Sine_source (batchsize, length, 1)
|
|
noise_source (batchsize, length 1)
|
|
uv (batchsize, length, 1)
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
sampling_rate: int,
|
|
harmonic_num: int = 0,
|
|
sine_amp: float = 0.1,
|
|
add_noise_std: float = 0.003,
|
|
voiced_threshod: int = 0,
|
|
):
|
|
super(SourceModuleHnNSF, self).__init__()
|
|
|
|
self.sine_amp = sine_amp
|
|
self.noise_std = add_noise_std
|
|
# to produce sine waveforms
|
|
self.l_sin_gen = SineGenerator(
|
|
sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
|
|
)
|
|
# to merge source harmonics into a single excitation
|
|
self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
|
|
self.l_tanh = torch.nn.Tanh()
|
|
|
|
def __call__(self, x: torch.Tensor, upp: int = 1) -> torch.Tensor:
|
|
return super().__call__(x, upp=upp)
|
|
|
|
def forward(self, x: torch.Tensor, upp: int = 1) -> torch.Tensor:
|
|
sine_wavs, _, _ = self.l_sin_gen(x, upp)
|
|
sine_wavs = sine_wavs.to(dtype=self.l_linear.weight.dtype)
|
|
sine_merge: torch.Tensor = self.l_tanh(self.l_linear(sine_wavs))
|
|
return sine_merge # , None, None # noise, uv
|
|
|
|
|
|
class NSFGenerator(torch.nn.Module):
|
|
def __init__(
|
|
self,
|
|
initial_channel: int,
|
|
resblock: str,
|
|
resblock_kernel_sizes: List[int],
|
|
resblock_dilation_sizes: List[List[int]],
|
|
upsample_rates: List[int],
|
|
upsample_initial_channel: int,
|
|
upsample_kernel_sizes: List[int],
|
|
gin_channels: int,
|
|
sr: int,
|
|
):
|
|
super(NSFGenerator, 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(sampling_rate=sr, harmonic_num=0)
|
|
self.noise_convs = nn.ModuleList()
|
|
self.conv_pre = Conv1d(
|
|
initial_channel, upsample_initial_channel, 7, 1, padding=3
|
|
)
|
|
resblockcls = ResBlock1 if resblock == "1" else ResBlock2
|
|
|
|
self.ups = nn.ModuleList()
|
|
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
|
|
c_cur = upsample_initial_channel // (2 ** (i + 1))
|
|
self.ups.append(
|
|
weight_norm(
|
|
ConvTranspose1d(
|
|
upsample_initial_channel // (2**i),
|
|
upsample_initial_channel // (2 ** (i + 1)),
|
|
k,
|
|
u,
|
|
padding=(k - u) // 2,
|
|
)
|
|
)
|
|
)
|
|
if i + 1 < len(upsample_rates):
|
|
stride_f0 = math.prod(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 = 0
|
|
for i in range(len(self.ups)):
|
|
ch = upsample_initial_channel // (2 ** (i + 1))
|
|
for j, (k, d) in enumerate(
|
|
zip(resblock_kernel_sizes, resblock_dilation_sizes)
|
|
):
|
|
self.resblocks.append(resblockcls(ch, k, d))
|
|
|
|
self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
|
|
self.ups.apply(call_weight_data_normal_if_Conv)
|
|
|
|
if gin_channels != 0:
|
|
self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
|
|
|
|
self.upp = math.prod(upsample_rates)
|
|
|
|
self.lrelu_slope = LRELU_SLOPE
|
|
|
|
def __call__(
|
|
self,
|
|
x: torch.Tensor,
|
|
f0: torch.Tensor,
|
|
g: Optional[torch.Tensor] = None,
|
|
n_res: Optional[int] = None,
|
|
) -> torch.Tensor:
|
|
return super().__call__(x, f0, g=g, n_res=n_res)
|
|
|
|
def forward(
|
|
self,
|
|
x: torch.Tensor,
|
|
f0: torch.Tensor,
|
|
g: Optional[torch.Tensor] = None,
|
|
n_res: Optional[int] = None,
|
|
) -> torch.Tensor:
|
|
har_source = self.m_source(f0, self.upp)
|
|
har_source = har_source.transpose(1, 2)
|
|
|
|
if n_res is not None:
|
|
n_res = int(n_res)
|
|
if n_res * self.upp != har_source.shape[-1]:
|
|
har_source = F.interpolate(
|
|
har_source, size=n_res * self.upp, mode="linear"
|
|
)
|
|
if n_res != x.shape[-1]:
|
|
x = F.interpolate(x, size=n_res, mode="linear")
|
|
|
|
x = self.conv_pre(x)
|
|
if g is not None:
|
|
x = x + self.cond(g)
|
|
# torch.jit.script() does not support direct indexing of torch modules
|
|
# That's why I wrote this
|
|
for i, (ups, noise_convs) in enumerate(zip(self.ups, self.noise_convs)):
|
|
if i < self.num_upsamples:
|
|
x = F.leaky_relu(x, self.lrelu_slope)
|
|
x = ups(x)
|
|
x_source = noise_convs(har_source)
|
|
x = x + x_source
|
|
xs: Optional[torch.Tensor] = None
|
|
l = [i * self.num_kernels + j for j in range(self.num_kernels)]
|
|
for j, resblock in enumerate(self.resblocks):
|
|
if j in l:
|
|
if xs is None:
|
|
xs = resblock(x)
|
|
else:
|
|
xs += resblock(x)
|
|
# This assertion cannot be ignored! \
|
|
# If ignored, it will cause torch.jit.script() compilation errors
|
|
assert isinstance(xs, torch.Tensor)
|
|
x = xs / self.num_kernels
|
|
x = F.leaky_relu(x)
|
|
x = self.conv_post(x)
|
|
x = torch.tanh(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():
|
|
# The hook we want to remove is an instance of WeightNorm class, so
|
|
# normally we would do `if isinstance(...)` but this class is not accessible
|
|
# because of shadowing, so we check the module name directly.
|
|
# https://github.com/pytorch/pytorch/blob/be0ca00c5ce260eb5bcec3237357f7a30cc08983/torch/nn/utils/__init__.py#L3
|
|
if (
|
|
hook.__module__ == "torch.nn.utils.weight_norm"
|
|
and hook.__class__.__name__ == "WeightNorm"
|
|
):
|
|
torch.nn.utils.remove_weight_norm(l)
|
|
for l in self.resblocks:
|
|
for hook in self.resblocks._forward_pre_hooks.values():
|
|
if (
|
|
hook.__module__ == "torch.nn.utils.weight_norm"
|
|
and hook.__class__.__name__ == "WeightNorm"
|
|
):
|
|
torch.nn.utils.remove_weight_norm(l)
|
|
return self
|