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# Copyright (c) 2022 NVIDIA CORPORATION.
# Licensed under the MIT license.
# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
# LICENSE is in incl_licenses directory.
import torch
import torch.nn as nn
from torch.nn import Conv1d, ConvTranspose1d
from torch.nn.utils.parametrizations import weight_norm
from torch.nn.utils.parametrize import remove_parametrizations
from mmaudio.ext.bigvgan import activations
from mmaudio.ext.bigvgan.alias_free_torch import *
from mmaudio.ext.bigvgan.utils import get_padding, init_weights
LRELU_SLOPE = 0.1
class AMPBlock1(torch.nn.Module):
def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5), activation=None):
super(AMPBlock1, self).__init__()
self.h = h
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)
self.num_layers = len(self.convs1) + len(self.convs2) # total number of conv layers
if activation == 'snake': # periodic nonlinearity with snake function and anti-aliasing
self.activations = nn.ModuleList([
Activation1d(
activation=activations.Snake(channels, alpha_logscale=h.snake_logscale))
for _ in range(self.num_layers)
])
elif activation == 'snakebeta': # periodic nonlinearity with snakebeta function and anti-aliasing
self.activations = nn.ModuleList([
Activation1d(
activation=activations.SnakeBeta(channels, alpha_logscale=h.snake_logscale))
for _ in range(self.num_layers)
])
else:
raise NotImplementedError(
"activation incorrectly specified. check the config file and look for 'activation'."
)
def forward(self, x):
acts1, acts2 = self.activations[::2], self.activations[1::2]
for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2):
xt = a1(x)
xt = c1(xt)
xt = a2(xt)
xt = c2(xt)
x = xt + x
return x
def remove_weight_norm(self):
for l in self.convs1:
remove_parametrizations(l, 'weight')
for l in self.convs2:
remove_parametrizations(l, 'weight')
class AMPBlock2(torch.nn.Module):
def __init__(self, h, channels, kernel_size=3, dilation=(1, 3), activation=None):
super(AMPBlock2, self).__init__()
self.h = h
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)
self.num_layers = len(self.convs) # total number of conv layers
if activation == 'snake': # periodic nonlinearity with snake function and anti-aliasing
self.activations = nn.ModuleList([
Activation1d(
activation=activations.Snake(channels, alpha_logscale=h.snake_logscale))
for _ in range(self.num_layers)
])
elif activation == 'snakebeta': # periodic nonlinearity with snakebeta function and anti-aliasing
self.activations = nn.ModuleList([
Activation1d(
activation=activations.SnakeBeta(channels, alpha_logscale=h.snake_logscale))
for _ in range(self.num_layers)
])
else:
raise NotImplementedError(
"activation incorrectly specified. check the config file and look for 'activation'."
)
def forward(self, x):
for c, a in zip(self.convs, self.activations):
xt = a(x)
xt = c(xt)
x = xt + x
return x
def remove_weight_norm(self):
for l in self.convs:
remove_parametrizations(l, 'weight')
class BigVGANVocoder(torch.nn.Module):
# this is our main BigVGAN model. Applies anti-aliased periodic activation for resblocks.
def __init__(self, h):
super().__init__()
self.h = h
self.num_kernels = len(h.resblock_kernel_sizes)
self.num_upsamples = len(h.upsample_rates)
# pre conv
self.conv_pre = weight_norm(Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3))
# define which AMPBlock to use. BigVGAN uses AMPBlock1 as default
resblock = AMPBlock1 if h.resblock == '1' else AMPBlock2
# transposed conv-based upsamplers. does not apply anti-aliasing
self.ups = nn.ModuleList()
for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
self.ups.append(
nn.ModuleList([
weight_norm(
ConvTranspose1d(h.upsample_initial_channel // (2**i),
h.upsample_initial_channel // (2**(i + 1)),
k,
u,
padding=(k - u) // 2))
]))
# residual blocks using anti-aliased multi-periodicity composition modules (AMP)
self.resblocks = nn.ModuleList()
for i in range(len(self.ups)):
ch = h.upsample_initial_channel // (2**(i + 1))
for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)):
self.resblocks.append(resblock(h, ch, k, d, activation=h.activation))
# post conv
if h.activation == "snake": # periodic nonlinearity with snake function and anti-aliasing
activation_post = activations.Snake(ch, alpha_logscale=h.snake_logscale)
self.activation_post = Activation1d(activation=activation_post)
elif h.activation == "snakebeta": # periodic nonlinearity with snakebeta function and anti-aliasing
activation_post = activations.SnakeBeta(ch, alpha_logscale=h.snake_logscale)
self.activation_post = Activation1d(activation=activation_post)
else:
raise NotImplementedError(
"activation incorrectly specified. check the config file and look for 'activation'."
)
self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
# weight initialization
for i in range(len(self.ups)):
self.ups[i].apply(init_weights)
self.conv_post.apply(init_weights)
def forward(self, x):
# pre conv
x = self.conv_pre(x)
for i in range(self.num_upsamples):
# upsampling
for i_up in range(len(self.ups[i])):
x = self.ups[i][i_up](x)
# AMP blocks
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
# post conv
x = self.activation_post(x)
x = self.conv_post(x)
x = torch.tanh(x)
return x
def remove_weight_norm(self):
print('Removing weight norm...')
for l in self.ups:
for l_i in l:
remove_parametrizations(l_i, 'weight')
for l in self.resblocks:
l.remove_weight_norm()
remove_parametrizations(self.conv_pre, 'weight')
remove_parametrizations(self.conv_post, 'weight')
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