KO -> more comment

app.py

@@ -51,21 +51,21 @@ class Tango:
         self.stft = TacotronSTFT(**stft_config).to(device)
         self.model = AudioDiffusion(**main_config).to(device)
         
-        vae_weights = torch.load("{}/pytorch_model_vae.bin".format(path), map_location = device)
-        stft_weights = torch.load("{}/pytorch_model_stft.bin".format(path), map_location = device)
-        main_weights = torch.load("{}/pytorch_model_main.bin".format(path), map_location = device)
-        
-        self.vae.load_state_dict(vae_weights)
-        self.stft.load_state_dict(stft_weights)
-        self.model.load_state_dict(main_weights)
-
-        print ("Successfully loaded checkpoint from:", name)
-        
-        self.vae.eval()
-        self.stft.eval()
-        self.model.eval()
-        
-        self.scheduler = DDPMScheduler.from_pretrained(main_config["scheduler_name"], subfolder = "scheduler")
+#        vae_weights = torch.load("{}/pytorch_model_vae.bin".format(path), map_location = device)
+#        stft_weights = torch.load("{}/pytorch_model_stft.bin".format(path), map_location = device)
+#        main_weights = torch.load("{}/pytorch_model_main.bin".format(path), map_location = device)
+#        
+#        self.vae.load_state_dict(vae_weights)
+#        self.stft.load_state_dict(stft_weights)
+#        self.model.load_state_dict(main_weights)
+#
+#        print ("Successfully loaded checkpoint from:", name)
+#        
+#        self.vae.eval()
+#        self.stft.eval()
+#        self.model.eval()
+#        
+#        self.scheduler = DDPMScheduler.from_pretrained(main_config["scheduler_name"], subfolder = "scheduler")
         
     def chunks(self, lst, n):
         # Yield successive n-sized chunks from a list