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edeb217 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 | """Lightweight alias-free waveform blocks derived from NVIDIA BigVGAN.
BigVGAN and alias-free-torch are MIT/Apache-2.0 licensed. The implementation
is kept local so Inflect can train without BigVGAN's optional CUDA extension.
"""
import math
import torch
from torch import nn
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, weight_norm
from commons import get_padding, init_weights
def kaiser_sinc_filter1d(cutoff: float, half_width: float, kernel_size: int):
even = kernel_size % 2 == 0
half_size = kernel_size // 2
delta_f = 4 * half_width
attenuation = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95
if attenuation > 50.0:
beta = 0.1102 * (attenuation - 8.7)
elif attenuation >= 21.0:
beta = 0.5842 * (attenuation - 21) ** 0.4 + 0.07886 * (attenuation - 21.0)
else:
beta = 0.0
window = torch.kaiser_window(kernel_size, beta=beta, periodic=False)
if even:
time = torch.arange(-half_size, half_size) + 0.5
else:
time = torch.arange(kernel_size) - half_size
values = 2 * cutoff * window * torch.sinc(2 * cutoff * time)
values /= values.sum()
return values.view(1, 1, kernel_size)
class UpSample1d(nn.Module):
def __init__(self, ratio=2, kernel_size=12):
super().__init__()
self.ratio = ratio
self.stride = ratio
self.kernel_size = kernel_size
self.pad = kernel_size // ratio - 1
self.pad_left = self.pad * ratio + (kernel_size - ratio) // 2
self.pad_right = self.pad * ratio + (kernel_size - ratio + 1) // 2
self.register_buffer(
"filter",
kaiser_sinc_filter1d(0.5 / ratio, 0.6 / ratio, kernel_size))
def forward(self, x):
channels = x.shape[1]
x = F.pad(x, (self.pad, self.pad), mode="replicate")
x = self.ratio * F.conv_transpose1d(
x, self.filter.expand(channels, -1, -1),
stride=self.stride, groups=channels)
return x[..., self.pad_left:-self.pad_right]
class DownSample1d(nn.Module):
def __init__(self, ratio=2, kernel_size=12):
super().__init__()
self.ratio = ratio
self.kernel_size = kernel_size
self.pad_left = kernel_size // 2 - int(kernel_size % 2 == 0)
self.pad_right = kernel_size // 2
self.register_buffer(
"filter",
kaiser_sinc_filter1d(0.5 / ratio, 0.6 / ratio, kernel_size))
def forward(self, x):
channels = x.shape[1]
x = F.pad(x, (self.pad_left, self.pad_right), mode="replicate")
return F.conv1d(
x, self.filter.expand(channels, -1, -1),
stride=self.ratio, groups=channels)
class SnakeBeta(nn.Module):
def __init__(self, channels: int, logscale: bool = True):
super().__init__()
initial = torch.zeros(channels) if logscale else torch.ones(channels)
self.alpha = nn.Parameter(initial.clone())
self.beta = nn.Parameter(initial.clone())
self.logscale = logscale
def forward(self, x):
alpha = self.alpha.view(1, -1, 1)
beta = self.beta.view(1, -1, 1)
if self.logscale:
alpha = alpha.exp()
beta = beta.exp()
return x + torch.sin(x * alpha).square() / (beta + 1e-9)
class AliasFreeActivation1d(nn.Module):
def __init__(self, activation: nn.Module):
super().__init__()
self.upsample = UpSample1d()
self.act = activation
self.downsample = DownSample1d()
def forward(self, x):
return self.downsample(self.act(self.upsample(x)))
class AliasFreeResBlock1(nn.Module):
"""Shape-compatible VITS ResBlock1 with filtered SnakeBeta activations."""
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5), logscale=True):
super().__init__()
self.convs1 = nn.ModuleList([
weight_norm(nn.Conv1d(
channels, channels, kernel_size, 1,
dilation=d, padding=get_padding(kernel_size, d)))
for d in dilation
])
self.convs2 = nn.ModuleList([
weight_norm(nn.Conv1d(
channels, channels, kernel_size, 1,
dilation=1, padding=get_padding(kernel_size, 1)))
for _ in dilation
])
self.convs1.apply(init_weights)
self.convs2.apply(init_weights)
self.activations = nn.ModuleList([
AliasFreeActivation1d(SnakeBeta(channels, logscale=logscale))
for _ in range(2 * len(dilation))
])
def forward(self, x, x_mask=None):
first = self.activations[::2]
second = self.activations[1::2]
for conv1, conv2, act1, act2 in zip(self.convs1, self.convs2, first, second):
residual = conv2(act2(conv1(act1(x))))
x = x + residual
return x
def remove_weight_norm(self):
for layer in self.convs1:
remove_weight_norm(layer)
for layer in self.convs2:
remove_weight_norm(layer)
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