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# Copyright (c) 2024 NVIDIA CORPORATION.
# Licensed under the MIT license.
# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
from .dac_activations import SnakeBeta
import torch
import torch.nn as nn
from torch.nn import Conv1d, ConvTranspose1d
from torch.nn.utils.parametrizations import weight_norm
from .dac_utils import init_weights, get_padding
from .dac_alias_free_act import Activation1d
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
self.__dict__ = self
class AMPBlock1(torch.nn.Module):
"""
AMPBlock applies SnakeBeta activation functions with trainable parameters that control periodicity, defined for each layer.
AMPBlock1 has additional self.convs2 that contains additional Conv1d layers with a fixed dilation=1 followed by each layer in self.convs1
Args:
h (AttrDict): Hyperparameters.
channels (int): Number of convolution channels.
kernel_size (int): Size of the convolution kernel. Default is 3.
dilation (tuple): Dilation rates for the convolutions. Each dilation layer has two convolutions. Default is (1, 3, 5).
activation (str): Activation function type. Must be 'snakebeta'.
"""
def __init__(
self,
h: AttrDict,
channels: int,
kernel_size: int = 3,
dilation: tuple = (1, 3, 5),
activation: str = None,
):
super().__init__()
self.h = h
self.convs1 = nn.ModuleList(
[
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
stride=1,
dilation=d,
padding=get_padding(kernel_size, d),
)
)
for d in dilation
]
)
self.convs1.apply(init_weights)
self.convs2 = nn.ModuleList(
[
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
stride=1,
dilation=1,
padding=get_padding(kernel_size, 1),
)
)
for _ in range(len(dilation))
]
)
self.convs2.apply(init_weights)
self.num_layers = len(self.convs1) + len(self.convs2) # Total number of conv layers
if activation == "snakebeta":
self.activations = nn.ModuleList(
[
Activation1d(activation=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
class BigVGAN(torch.nn.Module):
"""
BigVGAN is a neural vocoder model that applies anti-aliased periodic activation for residual blocks (resblocks).
Args:
h (AttrDict): Hyperparameters.
"""
def __init__(self, h: AttrDict):
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
if h.resblock == "1":
resblock_class = AMPBlock1
else:
raise ValueError(f"Incorrect resblock class specified in hyperparameters. Got {h.resblock}")
# 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_class(h, ch, k, d, activation=h.activation))
# Post-conv
if h.activation != "snakebeta":
raise NotImplementedError(
"activation incorrectly specified. check the config file and look for 'activation'."
)
activation_post = SnakeBeta(ch, alpha_logscale=h.snake_logscale)
self.activation_post = Activation1d(activation=activation_post)
# Whether to use bias for the final conv_post. Default to True for backward compatibility
self.use_bias_at_final = h.get("use_bias_at_final", True)
self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3, bias=self.use_bias_at_final))
# Weight initialization
for i in range(len(self.ups)):
self.ups[i].apply(init_weights)
self.conv_post.apply(init_weights)
# Final tanh activation. Defaults to True for backward compatibility
self.use_tanh_at_final = h.get("use_tanh_at_final", True)
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)
# Final tanh activation
if self.use_tanh_at_final:
x = torch.tanh(x)
else:
x = torch.clamp(x, min=-1.0, max=1.0) # Bound the output to [-1, 1]
return x
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