Update app.py

app.py

@@ -407,8 +407,8 @@ class TrinerModelVITS:
         self.len_dataset=len(self.DataSets['train'])
         self.load_model()
         self.init_wandb()
-        self.training_args=load_training_args(self.path_training_args)
-        training_args= self.training_args
+        # self.training_args=load_training_args(self.path_training_args)
+        # training_args= self.training_args
         scaler = GradScaler(enabled=True)
         for disc in self.model.discriminator.discriminators:
             disc.apply_weight_norm()
@@ -424,23 +424,23 @@ class TrinerModelVITS:
 
         optimizer = torch.optim.AdamW(
             self.model.parameters(),
-            training_args.learning_rate,
-            betas=[training_args.adam_beta1, training_args.adam_beta2],
-            eps=training_args.adam_epsilon,
+            2e-4,
+            betas=[0.8, 0.99],
+           # eps=training_args.adam_epsilon,
         )
 
         # Hack to be able to train on multiple device
         disc_optimizer = torch.optim.AdamW(
             discriminator.parameters(),
-            training_args.d_learning_rate,
-            betas=[training_args.d_adam_beta1, training_args.d_adam_beta2],
-            eps=training_args.adam_epsilon,
+             2e-4,
+            betas=[0.8, 0.99],
+          #  eps=training_args.adam_epsilon,
         )
         lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
-            optimizer, gamma=training_args.lr_decay, last_epoch=-1
+            optimizer, last_epoch=-1
         )
         disc_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
-            disc_optimizer, gamma=training_args.lr_decay, last_epoch=-1
+            disc_optimizer, last_epoch=-1
         )
         self.models=(self.model,discriminator)
         self.optimizers=(optimizer,disc_optimizer,scaler)