修复了OutOfMemoryError时，显存无法释放的问题

GPT_SoVITS/TTS_infer_pack/TTS.py

@@ -2,6 +2,7 @@ from copy import deepcopy
 import math
 import os, sys
 import random
+import traceback
 now_dir = os.getcwd()
 sys.path.append(now_dir)
 import ffmpeg
@@ -48,7 +49,17 @@ custom:
 
 """
 
-
+# def set_seed(seed):
+#     random.seed(seed)
+#     os.environ['PYTHONHASHSEED'] = str(seed)
+#     np.random.seed(seed)
+#     torch.manual_seed(seed)
+#     torch.cuda.manual_seed(seed)
+#     torch.cuda.manual_seed_all(seed)
+#     torch.backends.cudnn.deterministic = True
+#     torch.backends.cudnn.benchmark = False
+#     torch.backends.cudnn.enabled = True
+# set_seed(1234) 
     
 class TTS_Config:
     default_configs={
@@ -630,7 +641,7 @@ class TTS:
                              split_bucket=split_bucket
                              )
         t2 = ttime()
-        
+        try:
             print("############ 推理 ############")
             ###### inference ######
             t_34 = 0.0
@@ -741,14 +752,30 @@ class TTS:
                                                 batch_index_list, 
                                                 speed_factor, 
                                                 split_bucket)         
+        except Exception as e:
+            traceback.print_exc()
+            # 必须返回一个空音频, 否则会导致显存不释放。
+            yield self.configs.sampling_rate, np.zeros(int(self.configs.sampling_rate),
+                                                            dtype=np.int16)
+            # 重置模型, 否则会导致显存释放不完全。
+            del self.t2s_model
+            del self.vits_model
+            self.t2s_model = None
+            self.vits_model = None
+            self.init_t2s_weights(self.configs.t2s_weights_path)
+            self.init_vits_weights(self.configs.vits_weights_path)
+        finally:
+            self.empty_cache()
     
+    def empty_cache(self):
         try:    
+            if str(self.configs.device) == "cuda":
                 torch.cuda.empty_cache()
+            elif str(self.configs.device) == "mps":
+                torch.mps.empty_cache()
         except:
             pass 
         
-       
-       
     def audio_postprocess(self, 
                           audio:List[torch.Tensor], 
                           sr:int,