Update app.py

app.py

@@ -565,48 +565,52 @@ dir_model='wasmdashai/vits-ar-huba-fine'
 
 global_step=0
 wandb.login(key= "782b6a6e82bbb5a5348de0d3c7d40d1e76351e79")
+parser = HfArgumentParser((ModelArguments, DataTrainingArguments, VITSTrainingArguments))
+json_file = os.path.abspath('VitsModelSplit/finetune_config_ara.json')
+model_args, data_args, training_args = parser.parse_json_file(json_file = json_file)
+print('start')
+sgl=get_state_grad_loss(mel=True,
+                       # generator=False,
+                    #    discriminator=False,
+                        duration=False)
+
+print(training_args)
+training_args.num_train_epochs=1000
+training_args.fp16=True
+training_args.eval_steps=300
+training_args.weight_kl=1
+training_args.d_learning_rate=2e-4
+training_args.learning_rate=2e-4
+training_args.weight_mel=45
+training_args.num_train_epochs=4
+training_args.eval_steps=1000
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(device)
+
+b=int(id)
+ctrain_datasets,eval_dataset,full_generation_dataset=get_data_loader(train_dataset_dirs = train_dataset_dirs,
+                                                          eval_dataset_dir = os.path.join(dataset_dir,'eval'),
+                                                        full_generation_dir = os.path.join(dataset_dir,'full_generation'),
+                                                                 device="cuda")
+
+print('load Data')
+wandb.init(project= 'AZ')
+
+print('wandb')
+model=VitsModel.from_pretrained(dir_model,token=token).to("cuda")
+print('loadeed')
 @spaces.GPU
 def greet(text,id):
     global GK 
-    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, VITSTrainingArguments))
-    json_file = os.path.abspath('VitsModelSplit/finetune_config_ara.json')
-    model_args, data_args, training_args = parser.parse_json_file(json_file = json_file)
-    print('start')
-    sgl=get_state_grad_loss(mel=True,
-                           # generator=False,
-                        #    discriminator=False,
-                            duration=False)
-    
-    print(training_args)
-    training_args.num_train_epochs=1000
-    training_args.fp16=True
-    training_args.eval_steps=300
-    training_args.weight_kl=1
-    training_args.d_learning_rate=2e-4
-    training_args.learning_rate=2e-4
-    training_args.weight_mel=45
-    training_args.num_train_epochs=4
-    training_args.eval_steps=1000
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    print(device)
-    
-    b=int(id)
-    ctrain_datasets,eval_dataset,full_generation_dataset=get_data_loader(train_dataset_dirs = train_dataset_dirs,
-                                                              eval_dataset_dir = os.path.join(dataset_dir,'eval'),
-                                                            full_generation_dir = os.path.join(dataset_dir,'full_generation'),
-                                                                     device=device)
-
-    print('load Data')
-    wandb.init(project= 'AZ')
-    print('wandb')
+
     for i in range(10000):
 # model.train(True)
         print(f'clcye epochs ={i}')
         yield f'clcye epochs ={i}'
-        model=VitsModel.from_pretrained(dir_model,token=token).to(device)
+        
         # model.setMfA(monotonic_align.maximum_path)
         #dir_model_save=dir_model+'/vend'
-        print('loadeed')
+        
     
         trainer_to_cuda(model,
                         ctrain_datasets = ctrain_datasets,