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# adopted from https://github.com/jik876/hifi-gan/blob/master/models.py
import torch
from torch import nn
from torch.nn import Conv1d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils.parametrizations import weight_norm
from torch.nn.utils.parametrize import remove_parametrizations
from TTS.utils.io import load_fsspec
LRELU_SLOPE = 0.1
def get_padding(k, d):
return int((k * d - d) / 2)
class ResBlock1(torch.nn.Module):
"""Residual Block Type 1. It has 3 convolutional layers in each convolutional block.
Network::
x -> lrelu -> conv1_1 -> conv1_2 -> conv1_3 -> z -> lrelu -> conv2_1 -> conv2_2 -> conv2_3 -> o -> + -> o
|--------------------------------------------------------------------------------------------------|
Args:
channels (int): number of hidden channels for the convolutional layers.
kernel_size (int): size of the convolution filter in each layer.
dilations (list): list of dilation value for each conv layer in a block.
"""
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
super().__init__()
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.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))
),
]
)
def forward(self, x):
"""
Args:
x (Tensor): input tensor.
Returns:
Tensor: output tensor.
Shapes:
x: [B, C, T]
"""
for c1, c2 in zip(self.convs1, self.convs2):
xt = F.leaky_relu(x, LRELU_SLOPE)
xt = c1(xt)
xt = F.leaky_relu(xt, LRELU_SLOPE)
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 ResBlock2(torch.nn.Module):
"""Residual Block Type 2. It has 1 convolutional layers in each convolutional block.
Network::
x -> lrelu -> conv1-> -> z -> lrelu -> conv2-> o -> + -> o
|---------------------------------------------------|
Args:
channels (int): number of hidden channels for the convolutional layers.
kernel_size (int): size of the convolution filter in each layer.
dilations (list): list of dilation value for each conv layer in a block.
"""
def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
super().__init__()
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]),
)
),
]
)
def forward(self, x):
for c in self.convs:
xt = F.leaky_relu(x, LRELU_SLOPE)
xt = c(xt)
x = xt + x
return x
def remove_weight_norm(self):
for l in self.convs:
remove_parametrizations(l, "weight")
class HifiganGenerator(torch.nn.Module):
def __init__(
self,
in_channels,
out_channels,
resblock_type,
resblock_dilation_sizes,
resblock_kernel_sizes,
upsample_kernel_sizes,
upsample_initial_channel,
upsample_factors,
inference_padding=5,
cond_channels=0,
conv_pre_weight_norm=True,
conv_post_weight_norm=True,
conv_post_bias=True,
):
r"""HiFiGAN Generator with Multi-Receptive Field Fusion (MRF)
Network:
x -> lrelu -> upsampling_layer -> resblock1_k1x1 -> z1 -> + -> z_sum / #resblocks -> lrelu -> conv_post_7x1 -> tanh -> o
.. -> zI ---|
resblockN_kNx1 -> zN ---'
Args:
in_channels (int): number of input tensor channels.
out_channels (int): number of output tensor channels.
resblock_type (str): type of the `ResBlock`. '1' or '2'.
resblock_dilation_sizes (List[List[int]]): list of dilation values in each layer of a `ResBlock`.
resblock_kernel_sizes (List[int]): list of kernel sizes for each `ResBlock`.
upsample_kernel_sizes (List[int]): list of kernel sizes for each transposed convolution.
upsample_initial_channel (int): number of channels for the first upsampling layer. This is divided by 2
for each consecutive upsampling layer.
upsample_factors (List[int]): upsampling factors (stride) for each upsampling layer.
inference_padding (int): constant padding applied to the input at inference time. Defaults to 5.
"""
super().__init__()
self.inference_padding = inference_padding
self.num_kernels = len(resblock_kernel_sizes)
self.num_upsamples = len(upsample_factors)
# initial upsampling layers
self.conv_pre = weight_norm(Conv1d(in_channels, upsample_initial_channel, 7, 1, padding=3))
resblock = ResBlock1 if resblock_type == "1" else ResBlock2
# upsampling layers
self.ups = nn.ModuleList()
for i, (u, k) in enumerate(zip(upsample_factors, upsample_kernel_sizes)):
self.ups.append(
weight_norm(
ConvTranspose1d(
upsample_initial_channel // (2**i),
upsample_initial_channel // (2 ** (i + 1)),
k,
u,
padding=(k - u) // 2,
)
)
)
# MRF blocks
self.resblocks = nn.ModuleList()
for i in range(len(self.ups)):
ch = upsample_initial_channel // (2 ** (i + 1))
for _, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
self.resblocks.append(resblock(ch, k, d))
# post convolution layer
self.conv_post = weight_norm(Conv1d(ch, out_channels, 7, 1, padding=3, bias=conv_post_bias))
if cond_channels > 0:
self.cond_layer = nn.Conv1d(cond_channels, upsample_initial_channel, 1)
if not conv_pre_weight_norm:
remove_parametrizations(self.conv_pre, "weight")
if not conv_post_weight_norm:
remove_parametrizations(self.conv_post, "weight")
def forward(self, x, g=None):
"""
Args:
x (Tensor): feature input tensor.
g (Tensor): global conditioning input tensor.
Returns:
Tensor: output waveform.
Shapes:
x: [B, C, T]
Tensor: [B, 1, T]
"""
o = self.conv_pre(x)
if hasattr(self, "cond_layer"):
o = o + self.cond_layer(g)
for i in range(self.num_upsamples):
o = F.leaky_relu(o, LRELU_SLOPE)
o = self.ups[i](o)
z_sum = None
for j in range(self.num_kernels):
if z_sum is None:
z_sum = self.resblocks[i * self.num_kernels + j](o)
else:
z_sum += self.resblocks[i * self.num_kernels + j](o)
o = z_sum / self.num_kernels
o = F.leaky_relu(o)
o = self.conv_post(o)
o = torch.tanh(o)
return o
@torch.no_grad()
def inference(self, c):
"""
Args:
x (Tensor): conditioning input tensor.
Returns:
Tensor: output waveform.
Shapes:
x: [B, C, T]
Tensor: [B, 1, T]
"""
c = c.to(self.conv_pre.weight.device)
c = torch.nn.functional.pad(c, (self.inference_padding, self.inference_padding), "replicate")
return self.forward(c)
def remove_weight_norm(self):
print("Removing weight norm...")
for l in self.ups:
remove_parametrizations(l, "weight")
for l in self.resblocks:
l.remove_weight_norm()
remove_parametrizations(self.conv_pre, "weight")
remove_parametrizations(self.conv_post, "weight")
def load_checkpoint(
self, config, checkpoint_path, eval=False, cache=False
): # pylint: disable=unused-argument, redefined-builtin
state = load_fsspec(checkpoint_path, map_location=torch.device("cpu"), cache=cache)
self.load_state_dict(state["model"])
if eval:
self.eval()
assert not self.training
self.remove_weight_norm()
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