Refactor: Format Code with Ruff and Update Deprecated G2PW Link (#2255)

* ruff check --fix

* ruff format --line-length 120 --target-version py39

* Change the link for G2PW Model

* update pytorch version and colab

GPT_SoVITS/module/quantize.py

@@ -7,7 +7,6 @@
 """Residual vector quantizer implementation."""
 
 from dataclasses import dataclass, field
-import math
 import typing as tp
 
 import torch
@@ -88,14 +87,10 @@ class ResidualVectorQuantizer(nn.Module):
             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
-        )
+        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.