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Linter fixes and docstrings for HiFiGAN

pull/422/head
Eren Gölge 2021-04-07 15:58:44 +02:00
parent bd7a1c177b
commit d95b1458e8
1 changed files with 84 additions and 29 deletions
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113
TTS/vocoder/models/hifigan_generator.py
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@ -12,6 +12,19 @@ def get_padding(k, d):
class ResBlock1(torch.nn.Module):
"""Residual Block Type 1. It has 3 convolutional layers in each convolutiona 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([
@ -63,13 +76,21 @@ class ResBlock1(torch.nn.Module):
])
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
o = F.leaky_relu(x, LRELU_SLOPE)
o = c1(o)
o = F.leaky_relu(o, LRELU_SLOPE)
o = c2(o)
o = o + x
return o
def remove_weight_norm(self):
for l in self.convs1:
@ -79,6 +100,19 @@ class ResBlock1(torch.nn.Module):
class ResBlock2(torch.nn.Module):
"""Residual Block Type 1. It has 3 convolutional layers in each convolutiona 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([
@ -100,10 +134,10 @@ class ResBlock2(torch.nn.Module):
def forward(self, x):
for c in self.convs:
xt = F.leaky_relu(x, LRELU_SLOPE)
xt = c(xt)
x = xt + x
return x
o = F.leaky_relu(x, LRELU_SLOPE)
o = c(o)
o = o + x
return o
def remove_weight_norm(self):
for l in self.convs:
@ -111,17 +145,38 @@ class ResBlock2(torch.nn.Module):
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):
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):
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 = 5
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)):
@ -132,32 +187,32 @@ class HifiganGenerator(torch.nn.Module):
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 j, (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))
def forward(self, x):
x = self.conv_pre(x)
o = self.conv_pre(x)
for i in range(self.num_upsamples):
x = F.leaky_relu(x, LRELU_SLOPE)
x = self.ups[i](x)
xs = None
o = F.leaky_relu(o, LRELU_SLOPE)
o = self.ups[i](o)
z_sum = None
for j in range(self.num_kernels):
if xs is None:
xs = self.resblocks[i * self.num_kernels + j](x)
if z_sum is None:
z_sum = self.resblocks[i * self.num_kernels + j](o)
else:
xs += self.resblocks[i * self.num_kernels + j](x)
x = xs / self.num_kernels
x = F.leaky_relu(x)
x = self.conv_post(x)
x = torch.tanh(x)
return x
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):