Linter fixes and docstrings for HiFiGAN

TTS/vocoder/models/hifigan_generator.py

@@ -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)
+            o = F.leaky_relu(x, LRELU_SLOPE)
-            xt = c1(xt)
+            o = c1(o)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
+            o = F.leaky_relu(o, LRELU_SLOPE)
-            xt = c2(xt)
+            o = c2(o)
-            x = xt + x
+            o = o + x
-        return 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)
+            o = F.leaky_relu(x, LRELU_SLOPE)
-            xt = c(xt)
+            o = c(o)
-            x = xt + x
+            o = o + x
-        return 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,
+    def __init__(self, in_channels, out_channels, resblock_type,
-                 resblock_kernel_sizes, upsample_kernel_sizes,
+                 resblock_dilation_sizes, resblock_kernel_sizes,
-                 upsample_initial_channel, upsample_factors):
+                 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)
+            o = F.leaky_relu(o, LRELU_SLOPE)
-            x = self.ups[i](x)
+            o = self.ups[i](o)
-            xs = None
+            z_sum = None
             for j in range(self.num_kernels):
-                if xs is None:
+                if z_sum is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
+                    z_sum = self.resblocks[i * self.num_kernels + j](o)
                 else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
+                    z_sum += self.resblocks[i * self.num_kernels + j](o)
-            x = xs / self.num_kernels
+            o = z_sum / self.num_kernels
-        x = F.leaky_relu(x)
+        o = F.leaky_relu(o)
-        x = self.conv_post(x)
+        o = self.conv_post(o)
-        x = torch.tanh(x)
+        o = torch.tanh(o)
-        return x
+        return o
 
     @torch.no_grad()
     def inference(self, c):