chore(format): run black on dev (#9)

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

rvc/attentions.py

@@ -173,7 +173,10 @@ class MultiHeadAttention(nn.Module):
         """
         batch, heads, length, _ = x.size()
         # Concat columns of pad to shift from relative to absolute indexing.
-        x = F.pad(x, [0, 1, 0, 0, 0, 0, 0, 0], )
+        x = F.pad(
+            x,
+            [0, 1, 0, 0, 0, 0, 0, 0],
+        )
 
         # Concat extra elements so to add up to shape (len+1, 2*len-1).
         x_flat = x.view([batch, heads, length * 2 * length])

rvc/encoders.py

@@ -57,7 +57,7 @@ class Encoder(nn.Module):
                 )
             )
             self.norm_layers_2.append(LayerNorm(hidden_channels))
-    
+
     def __call__(self, x: torch.Tensor, x_mask: torch.Tensor) -> torch.Tensor:
         return super().__call__(x, x_mask)
 
@@ -146,7 +146,8 @@ class TextEncoder(nn.Module):
         x = self.lrelu(x)
         x = torch.transpose(x, 1, -1)  # [b, h, t]
         x_mask = torch.unsqueeze(
-            sequence_mask(lengths, x.size(2)), 1,
+            sequence_mask(lengths, x.size(2)),
+            1,
         ).to(x.dtype)
         x = self.encoder(x * x_mask, x_mask)
         """

rvc/utils.py

@@ -66,13 +66,15 @@ def sequence_mask(
 
 
 def total_grad_norm(
-    parameters: Iterator[torch.nn.Parameter], norm_type: float=2.0,
+    parameters: Iterator[torch.nn.Parameter],
+    norm_type: float = 2.0,
 ) -> float:
     norm_type = float(norm_type)
     total_norm = 0.0
 
     for p in parameters:
-        if p.grad is None: continue
+        if p.grad is None:
+            continue
         param_norm = p.grad.data.norm(norm_type)
         total_norm += float(param_norm.item()) ** norm_type
     total_norm = total_norm ** (1.0 / norm_type)