diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py
index 53f392b0..04287312 100644
--- a/GPT_SoVITS/TTS_infer_pack/TTS.py
+++ b/GPT_SoVITS/TTS_infer_pack/TTS.py
@@ -583,11 +583,18 @@ class TTS:
                 self.vocoder.cpu()
                 del self.vocoder
                 self.empty_cache()
-            
-            self.vocoder = BigVGAN.from_pretrained(
-                "%s/GPT_SoVITS/pretrained_models/models--nvidia--bigvgan_v2_24khz_100band_256x" % (now_dir,),
-                use_cuda_kernel=False,
-            )  # if True, RuntimeError: Ninja is required to load C++ extensions
+            if not vocoder_path:
+                self.vocoder = BigVGAN.from_pretrained(
+                    "%s/GPT_SoVITS/pretrained_models/models--nvidia--bigvgan_v2_24khz_100band_256x" % (now_dir,),
+                    use_cuda_kernel=False,
+                )  # if True, RuntimeError: Ninja is required to load C++ extensions
+            else:
+                self.vocoder = BigVGAN.from_pretrained(
+                    vocoder_path,
+                    use_cuda_kernel=False,
+                    local_files_only=True,
+                    cache_dir=vocoder_path
+                )
             # remove weight norm in the model and set to eval mode
             self.vocoder.remove_weight_norm()
 
diff --git a/GPT_SoVITS/process_ckpt.py b/GPT_SoVITS/process_ckpt.py
index 338b2309..82fb158d 100644
--- a/GPT_SoVITS/process_ckpt.py
+++ b/GPT_SoVITS/process_ckpt.py
@@ -4,6 +4,12 @@ from time import time as ttime
 import shutil
 import os
 import torch
+import sys
+import GPT_SoVITS.utils as _gpt_utils
+sys.modules['utils'] = _gpt_utils
+from GPT_SoVITS.utils import HParams
+HParams.__module__ = 'utils'
+torch.serialization.add_safe_globals([HParams])
 from GPT_SoVITS.tools.i18n.i18n import I18nAuto
 
 i18n = I18nAuto()
@@ -120,5 +126,5 @@ def load_sovits_new(sovits_path):
         bio = BytesIO()
         bio.write(data)
         bio.seek(0)
-        return torch.load(bio, map_location="cpu", weights_only=False)
-    return torch.load(sovits_path, map_location="cpu", weights_only=False)
+        return torch.load(bio, map_location="cpu", weights_only=True)
+    return torch.load(sovits_path, map_location="cpu", weights_only=True)