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from typing import Optional, List
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
)
resblock = 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()
for i in range(len(self.ups)):
ch: int = upsample_initial_channel // (2 ** (i + 1))
for j, (k, d) in enumerate(
zip(resblock_kernel_sizes, resblock_dilation_sizes)
):
self.resblocks.append(resblock(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
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