恢复先前缩进

GPT_SoVITS/TTS_infer_pack/TTS.py

@@ -280,6 +280,7 @@ class TTS:
         if self.configs.is_half and str(self.configs.device)!="cpu":
             self.vits_model = self.vits_model.half()
 
+        
     def init_t2s_weights(self, weights_path: str):
         print(f"Loading Text2Semantic weights from {weights_path}")
         self.configs.t2s_weights_path = weights_path
@@ -376,6 +377,7 @@ class TTS:
         # self.refer_spec = spec
         self.prompt_cache["refer_spec"] = spec
         
+        
     def _set_prompt_semantic(self, ref_wav_path:str):
         zero_wav = np.zeros(
             int(self.configs.sampling_rate * 0.3),
@@ -416,8 +418,7 @@ class TTS:
             max_length = max(seq_lengths)
         else:
             max_length = max(seq_lengths) if max_length < max(seq_lengths) else max_length
-        # 我爱套 torch.no_grad()
-        # with torch.no_grad():
+
         padded_sequences = []
         for seq, length in zip(sequences, seq_lengths):
             padding = [0] * axis + [0, max_length - length] + [0] * (ndim - axis - 1)
@@ -434,7 +435,6 @@ class TTS:
                  device:torch.device=torch.device("cpu"),
                  precision:torch.dtype=torch.float32,
                  ):
-        
         # 但是这里不能套，反而会负优化
         # with torch.no_grad():
         _data:list = []
@@ -472,6 +472,7 @@ class TTS:
                     batch_index_list.append([])
                 batch_index_list[-1].append(i)
 
+                
         for batch_idx, index_list in enumerate(batch_index_list):
             item_list = [data[idx] for idx in index_list]
             phones_list = []
@@ -513,6 +514,7 @@ class TTS:
             all_phones_batch = all_phones_list
             all_bert_features_batch = all_bert_features_list
             
+            
             # max_len = max(bert_max_len, phones_max_len)
             # phones_batch = self.batch_sequences(phones_list, axis=0, pad_value=0, max_length=max_len)
             #### 直接对phones和bert_features进行pad，会增大复读概率。
@@ -567,6 +569,7 @@ class TTS:
         '''
         self.stop_flag = True
         
+        
     def run(self, inputs:dict):
         """
         Text to speech inference.
@@ -881,12 +884,14 @@ class TTS:
                 audio_fragment:torch.Tensor = torch.cat([audio_fragment, zero_wav], dim=0)
                 audio[i][j] = audio_fragment.cpu().numpy()
             
+        
         if split_bucket:
             audio = self.recovery_order(audio, batch_index_list)
         else:
             # audio = [item for batch in audio for item in batch]
             audio = sum(audio, [])
             
+            
         audio = np.concatenate(audio, 0)
         audio = (audio * 32768).astype(np.int16) 
         
@@ -899,6 +904,8 @@ class TTS:
         return sr, audio
             
         
+        
+       
 def speed_change(input_audio:np.ndarray, speed:float, sr:int):
     # 将 NumPy 数组转换为原始 PCM 流
     raw_audio = input_audio.astype(np.int16).tobytes()