diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py
index 893f6524..a4cbb4f7 100644
--- a/GPT_SoVITS/TTS_infer_pack/TTS.py
+++ b/GPT_SoVITS/TTS_infer_pack/TTS.py
@@ -1,6 +1,6 @@
 from copy import deepcopy
 import math
-import os, sys
+import os, sys, gc
 import random
 import traceback
 
@@ -39,7 +39,7 @@ default:
   cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
   t2s_weights_path: GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt
   vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth
-  
+
 custom:
   device: cuda
   is_half: true
@@ -209,7 +209,7 @@ class TTS:
         self.text_preprocessor: TextPreprocessor = \
             TextPreprocessor(self.bert_model,
                              self.bert_tokenizer,
-                             self.configs.device, version=self.version)
+                             self.configs.device)
 
         self.prompt_cache: dict = {
             "ref_audio_path": None,
@@ -301,12 +301,12 @@ class TTS:
         if self.configs.is_half and str(self.configs.device) != "cpu":
             self.t2s_model = self.t2s_model.half()
 
-    def enable_half_precision(self, enable: bool = True):
+    def enable_half_precision(self, enable: bool = True, save: bool = True):
         '''
             To enable half precision for the TTS model.
             Args:
                 enable: bool, whether to enable half precision.
-                
+
         '''
         if str(self.configs.device) == "cpu" and enable:
             print("Half precision is not supported on CPU.")
@@ -314,7 +314,8 @@ class TTS:
 
         self.configs.is_half = enable
         self.precision = torch.float16 if enable else torch.float32
-        self.configs.save_configs()
+        if save:
+            self.configs.save_configs()
         if enable:
             if self.t2s_model is not None:
                 self.t2s_model = self.t2s_model.half()
@@ -334,14 +335,15 @@ class TTS:
             if self.cnhuhbert_model is not None:
                 self.cnhuhbert_model = self.cnhuhbert_model.float()
 
-    def set_device(self, device: torch.device):
+    def set_device(self, device: torch.device, save: bool = True):
         '''
             To set the device for all models.
             Args:
                 device: torch.device, the device to use for all models.
         '''
         self.configs.device = device
-        self.configs.save_configs()
+        if save:
+            self.configs.save_configs()
         if self.t2s_model is not None:
             self.t2s_model = self.t2s_model.to(device)
         if self.vits_model is not None:
@@ -353,13 +355,17 @@ class TTS:
 
     def set_ref_audio(self, ref_audio_path: str):
         '''
-            To set the reference audio for the TTS model, 
+            To set the reference audio for the TTS model,
                 including the prompt_semantic and refer_spepc.
             Args:
                 ref_audio_path: str, the path of the reference audio.
         '''
         self._set_prompt_semantic(ref_audio_path)
         self._set_ref_spec(ref_audio_path)
+        self._set_ref_audio_path(ref_audio_path)
+
+    def _set_ref_audio_path(self, ref_audio_path):
+        self.prompt_cache["ref_audio_path"] = ref_audio_path
 
     def _set_ref_spec(self, ref_audio_path):
         audio = load_audio(ref_audio_path, int(self.configs.sampling_rate))
@@ -545,11 +551,11 @@ class TTS:
     def recovery_order(self, data: list, batch_index_list: list) -> list:
         '''
         Recovery the order of the audio according to the batch_index_list.
-        
+
         Args:
             data (List[list(np.ndarray)]): the out of order audio .
             batch_index_list (List[list[int]]): the batch index list.
-        
+
         Returns:
             list (List[np.ndarray]): the data in the original order.
         '''
@@ -570,9 +576,9 @@ class TTS:
     def run(self, inputs: dict):
         """
         Text to speech inference.
-        
+
         Args:
-            inputs (dict): 
+            inputs (dict):
                 {
                     "text": "",                   # str.(required) text to be synthesized
                     "text_lang: "",               # str.(required) language of the text to be synthesized
@@ -801,7 +807,6 @@ class TTS:
                 audio_frag_end_idx = [sum(audio_frag_idx[:i + 1]) for i in range(0, len(audio_frag_idx))]
                 all_pred_semantic = torch.cat(pred_semantic_list).unsqueeze(0).unsqueeze(0).to(self.configs.device)
                 _batch_phones = torch.cat(batch_phones).unsqueeze(0).to(self.configs.device)
-
                 _batch_audio_fragment = (self.vits_model.decode(
                     all_pred_semantic, _batch_phones, refer_audio_spec
                 ).detach()[0, 0, :])
@@ -867,6 +872,7 @@ class TTS:
 
     def empty_cache(self):
         try:
+            gc.collect()  # 触发gc的垃圾回收。避免内存一直增长。
             if "cuda" in str(self.configs.device):
                 torch.cuda.empty_cache()
             elif str(self.configs.device) == "mps":
@@ -932,4 +938,4 @@ def speed_change(input_audio: np.ndarray, speed: float, sr: int):
     # 将管道输出解码为 NumPy 数组
     processed_audio = np.frombuffer(out, np.int16)
 
-    return processed_audio
+    return processed_audio
\ No newline at end of file