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/BigVGAN/alias_free_activation/cuda/activation1d.py

@@ -20,9 +20,7 @@ class FusedAntiAliasActivation(torch.autograd.Function):
 
     @staticmethod
     def forward(ctx, inputs, up_ftr, down_ftr, alpha, beta):
-        activation_results = anti_alias_activation_cuda.forward(
-            inputs, up_ftr, down_ftr, alpha, beta
-        )
+        activation_results = anti_alias_activation_cuda.forward(inputs, up_ftr, down_ftr, alpha, beta)
 
         return activation_results
 
@@ -61,17 +59,11 @@ class Activation1d(nn.Module):
             if self.act.__class__.__name__ == "Snake":
                 beta = self.act.alpha.data  # Snake uses same params for alpha and beta
             else:
-                beta = (
-                    self.act.beta.data
-                )  # Snakebeta uses different params for alpha and beta
+                beta = self.act.beta.data  # Snakebeta uses different params for alpha and beta
             alpha = self.act.alpha.data
-            if (
-                not self.act.alpha_logscale
-            ):  # Exp baked into cuda kernel, cancel it out with a log
+            if not self.act.alpha_logscale:  # Exp baked into cuda kernel, cancel it out with a log
                 alpha = torch.log(alpha)
                 beta = torch.log(beta)
 
-            x = FusedAntiAliasActivation.apply(
-                x, self.upsample.filter, self.downsample.lowpass.filter, alpha, beta
-            )
+            x = FusedAntiAliasActivation.apply(x, self.upsample.filter, self.downsample.lowpass.filter, alpha, beta)
             return x

GPT_SoVITS/BigVGAN/alias_free_activation/cuda/load.py

@@ -58,17 +58,13 @@ def load():
         srcpath / "anti_alias_activation.cpp",
         srcpath / "anti_alias_activation_cuda.cu",
     ]
-    anti_alias_activation_cuda = _cpp_extention_load_helper(
-        "anti_alias_activation_cuda", sources, extra_cuda_flags
-    )
+    anti_alias_activation_cuda = _cpp_extention_load_helper("anti_alias_activation_cuda", sources, extra_cuda_flags)
 
     return anti_alias_activation_cuda
 
 
 def _get_cuda_bare_metal_version(cuda_dir):
-    raw_output = subprocess.check_output(
-        [cuda_dir + "/bin/nvcc", "-V"], universal_newlines=True
-    )
+    raw_output = subprocess.check_output([cuda_dir + "/bin/nvcc", "-V"], universal_newlines=True)
     output = raw_output.split()
     release_idx = output.index("release") + 1
     release = output[release_idx].split(".")

GPT_SoVITS/BigVGAN/alias_free_activation/torch/filter.py

@@ -27,9 +27,7 @@ else:
 # This code is adopted from adefossez's julius.lowpass.LowPassFilters under the MIT License
 # https://adefossez.github.io/julius/julius/lowpass.html
 #   LICENSE is in incl_licenses directory.
-def kaiser_sinc_filter1d(
-    cutoff, half_width, kernel_size
-):  # return filter [1,1,kernel_size]
+def kaiser_sinc_filter1d(cutoff, half_width, kernel_size):  # return filter [1,1,kernel_size]
     even = kernel_size % 2 == 0
     half_size = kernel_size // 2
 

GPT_SoVITS/BigVGAN/alias_free_activation/torch/resample.py

@@ -11,18 +11,12 @@ class UpSample1d(nn.Module):
     def __init__(self, ratio=2, kernel_size=None):
         super().__init__()
         self.ratio = ratio
-        self.kernel_size = (
-            int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
-        )
+        self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
         self.stride = ratio
         self.pad = self.kernel_size // ratio - 1
         self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2
-        self.pad_right = (
-            self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2
-        )
-        filter = kaiser_sinc_filter1d(
-            cutoff=0.5 / ratio, half_width=0.6 / ratio, kernel_size=self.kernel_size
-        )
+        self.pad_right = self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2
+        filter = kaiser_sinc_filter1d(cutoff=0.5 / ratio, half_width=0.6 / ratio, kernel_size=self.kernel_size)
         self.register_buffer("filter", filter)
 
     # x: [B, C, T]
@@ -30,9 +24,7 @@ class UpSample1d(nn.Module):
         _, C, _ = x.shape
 
         x = F.pad(x, (self.pad, self.pad), mode="replicate")
-        x = self.ratio * F.conv_transpose1d(
-            x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C
-        )
+        x = self.ratio * F.conv_transpose1d(x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C)
         x = x[..., self.pad_left : -self.pad_right]
 
         return x
@@ -42,9 +34,7 @@ class DownSample1d(nn.Module):
     def __init__(self, ratio=2, kernel_size=None):
         super().__init__()
         self.ratio = ratio
-        self.kernel_size = (
-            int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
-        )
+        self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
         self.lowpass = LowPassFilter1d(
             cutoff=0.5 / ratio,
             half_width=0.6 / ratio,