, weights_only=False

_run.py

@@ -99,7 +99,7 @@ model = build_model(model_params, text_aligner, pitch_extractor, plbert)
 _ = [model[key].eval() for key in model]
 _ = [model[key].to(device) for key in model]
 
-params_whole = torch.load("Models/LibriTTS/epochs_2nd_00020.pth", map_location='cpu')
+params_whole = torch.load("Models/LibriTTS/epochs_2nd_00020.pth", map_location='cpu', weights_only=False)
 params = params_whole['net']
 
 for key in model:

models.py

@@ -722,7 +722,7 @@ def load_F0_models(path):
     # load F0 model
 
     F0_model = JDCNet(num_class=1, seq_len=192)
-    params = torch.load(path, map_location="cpu")["net"]
+    params = torch.load(path, map_location="cpu", weights_only=False)["net"]
     F0_model.load_state_dict(params)
     _ = F0_model.train()
 
@@ -739,7 +739,7 @@ def load_ASR_models(ASR_MODEL_PATH, ASR_MODEL_CONFIG):
 
     def _load_model(model_config, model_path):
         model = ASRCNN(**model_config)
-        params = torch.load(model_path, map_location="cpu")["model"]
+        params = torch.load(model_path, map_location="cpu", weights_only=False)["model"]
         model.load_state_dict(params)
         return model
 
@@ -862,7 +862,7 @@ def build_model(args, text_aligner, pitch_extractor, bert):
 
 
 def load_checkpoint(model, optimizer, path, load_only_params=True, ignore_modules=[]):
-    state = torch.load(path, map_location="cpu")
+    state = torch.load(path, map_location="cpu", weights_only=False)
     params = state["net"]
     for key in model:
         if key in params and key not in ignore_modules:

styletts2importable.py

@@ -105,7 +105,7 @@ _ = [model[key].eval() for key in model]
 _ = [model[key].to(device) for key in model]
 
 # params_whole = torch.load("Models/LibriTTS/epochs_2nd_00020.pth", map_location='cpu')
-params_whole = torch.load(str(cached_path("hf://yl4579/StyleTTS2-LibriTTS/Models/LibriTTS/epochs_2nd_00020.pth")), map_location='cpu')
+params_whole = torch.load(str(cached_path("hf://yl4579/StyleTTS2-LibriTTS/Models/LibriTTS/epochs_2nd_00020.pth")), map_location='cpu', weights_only=False)
 params = params_whole['net']
 
 for key in model:
@@ -169,7 +169,7 @@ def inference(text, ref_s, lang='en-us', alpha = 0.3, beta = 0.7, diffusion_step
     # replace IPA sections with []
     if (ipa_sections is not None):
         text = re.sub(regex, '[]', text, 0, re.MULTILINE)
-        
+
     if lang in LANG_NAMES:
         local_phonemizer = phonemizer.backend.EspeakBackend(language=lang, preserve_punctuation=True, with_stress=True)
         ps = local_phonemizer.phonemize([text])
@@ -186,7 +186,7 @@ def inference(text, ref_s, lang='en-us', alpha = 0.3, beta = 0.7, diffusion_step
     if (ipa_sections is not None):
         for ipa in ipa_sections:
             ps = ps.replace('[ ]', ipa, 1)
-            
+
     tokens = textclenaer(ps)
     tokens.insert(0, 0)
     tokens = torch.LongTensor(tokens).to(device).unsqueeze(0)