more code refactor

GPT_SoVITS/feature_extractor/whisper_enc.py

@@ -3,20 +3,23 @@ import torch
 
 def get_model():
     import whisper
-    model = whisper.load_model("small", device='cpu')
+
+    model = whisper.load_model("small", device="cpu")
 
     return model.encoder
 
 
 def get_content(model=None, wav_16k_tensor=None):
     from whisper import log_mel_spectrogram, pad_or_trim
+
     dev = next(model.parameters()).device
     mel = log_mel_spectrogram(wav_16k_tensor).to(dev)[:, :3000]
     # if torch.cuda.is_available():
     #     mel = mel.to(torch.float16)
     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"
+    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
-