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/feature_extractor/__init__.py

@@ -1,6 +1,3 @@
 from . import cnhubert, whisper_enc
 
-content_module_map = {
-    'cnhubert': cnhubert,
-    'whisper': whisper_enc
-}
+content_module_map = {"cnhubert": cnhubert, "whisper": whisper_enc}

GPT_SoVITS/feature_extractor/cnhubert.py

@@ -1,14 +1,11 @@
-import time
-
-import librosa
 import torch
-import torch.nn.functional as F
-import soundfile as sf
 import os
 from transformers import logging as tf_logging
+
 tf_logging.set_verbosity_error()
 
 import logging
+
 logging.getLogger("numba").setLevel(logging.WARNING)
 
 from transformers import (
@@ -23,21 +20,19 @@ cnhubert_base_path = None
 
 
 class CNHubert(nn.Module):
-    def __init__(self, base_path:str=None):
+    def __init__(self, base_path: str = None):
         super().__init__()
         if base_path is None:
             base_path = cnhubert_base_path
-        if os.path.exists(base_path):...
-        else:raise FileNotFoundError(base_path)
+        if os.path.exists(base_path):
+            ...
+        else:
+            raise FileNotFoundError(base_path)
         self.model = HubertModel.from_pretrained(base_path, local_files_only=True)
-        self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
-            base_path, local_files_only=True
-        )
+        self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(base_path, local_files_only=True)
 
     def forward(self, x):
-        input_values = self.feature_extractor(
-            x, return_tensors="pt", sampling_rate=16000
-        ).input_values.to(x.device)
+        input_values = self.feature_extractor(x, return_tensors="pt", sampling_rate=16000).input_values.to(x.device)
         feats = self.model(input_values)["last_hidden_state"]
         return feats
 

GPT_SoVITS/feature_extractor/whisper_enc.py

@@ -19,7 +19,5 @@ def get_content(model=None, wav_16k_tensor=None):
     feature_len = mel.shape[-1] // 2
     assert mel.shape[-1] < 3000, "输入音频过长，只允许输入30以内音频"
     with torch.no_grad():
-        feature = model(pad_or_trim(mel, 3000).unsqueeze(0))[
-            :1, :feature_len, :
-        ].transpose(1, 2)
+        feature = model(pad_or_trim(mel, 3000).unsqueeze(0))[:1, :feature_len, :].transpose(1, 2)
     return feature