more code refactor

GPT_SoVITS/module/quantize.py

@@ -38,6 +38,7 @@ class ResidualVectorQuantizer(nn.Module):
             that have an exponential moving average cluster size less than the specified threshold with
             randomly selected vector from the current batch.
     """
+
     def __init__(
         self,
         dimension: int = 256,
@@ -66,7 +67,12 @@ class ResidualVectorQuantizer(nn.Module):
             threshold_ema_dead_code=self.threshold_ema_dead_code,
         )
 
-    def forward(self, x: torch.Tensor, n_q: tp.Optional[int] = None, layers: tp.Optional[list] = None) -> QuantizedResult:
+    def forward(
+        self,
+        x: torch.Tensor,
+        n_q: tp.Optional[int] = None,
+        layers: tp.Optional[list] = None,
+    ) -> QuantizedResult:
         """Residual vector quantization on the given input tensor.
         Args:
             x (torch.Tensor): Input tensor.
@@ -79,12 +85,17 @@ class ResidualVectorQuantizer(nn.Module):
         """
         n_q = n_q if n_q else self.n_q
         if layers and max(layers) >= n_q:
-            raise ValueError(f'Last layer index in layers: A {max(layers)}. Number of quantizers in RVQ: B {self.n_q}. A must less than B.')
-        quantized, codes, commit_loss, quantized_list = self.vq(x, n_q=n_q, layers=layers)
+            raise ValueError(
+                f"Last layer index in layers: A {max(layers)}. Number of quantizers in RVQ: B {self.n_q}. A must less than B."
+            )
+        quantized, codes, commit_loss, quantized_list = self.vq(
+            x, n_q=n_q, layers=layers
+        )
         return quantized, codes, torch.mean(commit_loss), quantized_list
 
-
-    def encode(self, x: torch.Tensor, n_q: tp.Optional[int] = None, st: tp.Optional[int] = None) -> torch.Tensor:
+    def encode(
+        self, x: torch.Tensor, n_q: tp.Optional[int] = None, st: tp.Optional[int] = None
+    ) -> torch.Tensor:
         """Encode a given input tensor with the specified sample rate at the given bandwidth.
         The RVQ encode method sets the appropriate number of quantizer to use
         and returns indices for each quantizer.
@@ -105,4 +116,4 @@ class ResidualVectorQuantizer(nn.Module):
             st (int): Start to decode input codes from which layers. Default: 0.
         """
         quantized = self.vq.decode(codes, st=st)
-        return quantized
+        return quantized