Merge pull request #899 from IAHispano/formatter/main

chore(format): run black on main

rvc/lib/algorithm/attentions.py

@@ -201,10 +201,10 @@ class FFN(torch.nn.Module):
 
     def __init__(
         self,
-        in_channels = int,
-        out_channels = int,
-        filter_channels = int,
-        kernel_size = int,
+        in_channels=int,
+        out_channels=int,
+        filter_channels=int,
+        kernel_size=int,
         p_dropout: float = 0.0,
         activation: str = None,
         causal: bool = False,

rvc/lib/algorithm/discriminators.py

@@ -59,7 +59,7 @@ class DiscriminatorS(torch.nn.Module):
     convolutional layers that are applied to the input signal.
     """
 
-    def __init__(self, use_spectral_norm: bool =False):
+    def __init__(self, use_spectral_norm: bool = False):
         super(DiscriminatorS, self).__init__()
         norm_f = spectral_norm if use_spectral_norm else weight_norm
         self.convs = torch.nn.ModuleList(
@@ -108,7 +108,13 @@ class DiscriminatorP(torch.nn.Module):
         use_spectral_norm (bool): Whether to use spectral normalization. Defaults to False.
     """
 
-    def __init__(self, period: int, kernel_size: int = 5, stride: int = 3, use_spectral_norm: bool = False):
+    def __init__(
+        self,
+        period: int,
+        kernel_size: int = 5,
+        stride: int = 3,
+        use_spectral_norm: bool = False,
+    ):
         super(DiscriminatorP, self).__init__()
         self.period = period
         norm_f = spectral_norm if use_spectral_norm else weight_norm

rvc/lib/algorithm/nsf.py

@@ -69,7 +69,7 @@ class GeneratorNSF(torch.nn.Module):
 
     def __init__(
         self,
-        initial_channel : int,
+        initial_channel: int,
         resblock_kernel_sizes: list,
         resblock_dilation_sizes: list,
         upsample_rates: list,

rvc/lib/algorithm/residuals.py

@@ -55,7 +55,9 @@ def remove_weight_norm(self):
 
 
 class ResBlock(ResBlockBase):
-    def __init__(self, channels: int, kernel_size: int = 3, dilation: Tuple[int] = (1, 3, 5)):
+    def __init__(
+        self, channels: int, kernel_size: int = 3, dilation: Tuple[int] = (1, 3, 5)
+    ):
         super(ResBlock, self).__init__(channels, kernel_size, dilation)
 
 
@@ -210,7 +212,13 @@ def __init__(
         self.post.weight.data.zero_()
         self.post.bias.data.zero_()
 
-    def forward(self, x: torch.Tensor, x_mask: torch.Tensor, g: Optional[torch.Tensor] = None, reverse: bool = False):
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_mask: torch.Tensor,
+        g: Optional[torch.Tensor] = None,
+        reverse: bool = False,
+    ):
         """Forward pass.
 
         Args:

rvc/train/losses.py

@@ -1,5 +1,6 @@
 import torch
 
+
 def feature_loss(fmap_r, fmap_g):
     """
     Compute the feature loss between reference and generated feature maps.

rvc/train/process/change_info.py

@@ -1,6 +1,7 @@
 import os
 import torch
 
+
 def change_info(path, info, name):
     try:
         ckpt = torch.load(path, map_location="cpu")
@@ -15,8 +16,7 @@ def change_info(path, info, name):
         torch.save(ckpt, os.path.join(target_dir, f"{name}.pth"))
 
         return "Success."
-    
+
     except Exception as error:
         print(f"An error occurred while changing the info: {error}")
         return f"Error: {error}"
-