diff --git a/GPT_SoVITS/inference_webui.py b/GPT_SoVITS/inference_webui.py
index 7d79fbd..7920d60 100644
--- a/GPT_SoVITS/inference_webui.py
+++ b/GPT_SoVITS/inference_webui.py
@@ -17,16 +17,11 @@ if "_CUDA_VISIBLE_DEVICES" in os.environ:
 is_half = eval(os.environ.get("is_half", "True"))
 import gradio as gr
 from transformers import AutoModelForMaskedLM, AutoTokenizer
-import torch, numpy as np
-import os, librosa, torch
-
-# torch.backends.cuda.sdp_kernel("flash")
-# torch.backends.cuda.enable_flash_sdp(True)
-# torch.backends.cuda.enable_mem_efficient_sdp(True)  # Not avaliable if torch version is lower than 2.0
-# torch.backends.cuda.enable_math_sdp(True)
+import numpy as np
+import librosa,torch
 from feature_extractor import cnhubert
+cnhubert.cnhubert_base_path=cnhubert_base_path
 
-cnhubert.cnhubert_base_path = cnhubert_base_path
 from module.models import SynthesizerTrn
 from AR.models.t2s_lightning_module import Text2SemanticLightningModule
 from text import cleaned_text_to_sequence
@@ -63,21 +58,40 @@ def get_bert_feature(text, word2ph):
 
 
 n_semantic = 1024
-dict_s2 = torch.load(sovits_path, map_location="cpu")
-hps = dict_s2["config"]
 
+dict_s2=torch.load(sovits_path,map_location="cpu")
+hps=dict_s2["config"]
 
-class DictToAttrRecursive:
+class DictToAttrRecursive(dict):
     def __init__(self, input_dict):
+        super().__init__(input_dict)
         for key, value in input_dict.items():
             if isinstance(value, dict):
-                # 如果值是字典，递归调用构造函数
-                setattr(self, key, DictToAttrRecursive(value))
-            else:
-                setattr(self, key, value)
+                value = DictToAttrRecursive(value)
+            self[key] = value
+            setattr(self, key, value)
+
+    def __getattr__(self, item):
+        try:
+            return self[item]
+        except KeyError:
+            raise AttributeError(f"Attribute {item} not found")
+
+    def __setattr__(self, key, value):
+        if isinstance(value, dict):
+            value = DictToAttrRecursive(value)
+        super(DictToAttrRecursive, self).__setitem__(key, value)
+        super().__setattr__(key, value)
+
+    def __delattr__(self, item):
+        try:
+            del self[item]
+        except KeyError:
+            raise AttributeError(f"Attribute {item} not found")
 
 
 hps = DictToAttrRecursive(hps)
+
 hps.model.semantic_frame_rate = "25hz"
 dict_s1 = torch.load(gpt_path, map_location="cpu")
 config = dict_s1["config"]