Update app.py

app.py

@@ -276,8 +276,8 @@ def trainer_to_cuda(self,
                     disc_optimizer, gamma=training_args.lr_decay, last_epoch=-1)
 
 
-    logger.info("***** Running training *****")
-    logger.info(f"  Num Epochs = {training_args.num_train_epochs}")
+    # logger.info("***** Running training *****")
+    # logger.info(f"  Num Epochs = {training_args.num_train_epochs}")
 
 
     #.......................loop training............................
@@ -526,11 +526,11 @@ def trainer_to_cuda(self,
 
     self.save_pretrained(path_save_model)
 
-    logger.info("Running final full generations samples... ")
+    # logger.info("Running final full generations samples... ")
 
 
 
-    logger.info("***** Training / Inference Done *****")
+    # logger.info("***** Training / Inference Done *****")
 def   modelspeech(texts):
      
     
@@ -572,6 +572,10 @@ ctrain_datasets,eval_dataset,full_generation_dataset=get_data_loader(train_datas
                                                               eval_dataset_dir = os.path.join(dataset_dir,'eval'),
                                                             full_generation_dir = os.path.join(dataset_dir,'full_generation'),
                                                                      device=device)
+
+
+
+
 def greet(text,id):
     global GK 
     b=int(id)
@@ -583,3 +587,50 @@ def greet(text,id):
 
 demo = gr.Interface(fn=greet, inputs=["text","text"], outputs="text")
 demo.launch()
+
+
+raining_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
+global_step=0
+dir_model='wasmdashai/vits-ar-huba-fine'
+
+
+
+wandb.login(key= "782b6a6e82bbb5a5348de0d3c7d40d1e76351e79")
+wandb.init(project= 'AZ',config = training_args.to_dict())
+
+for i in range(10000):
+# model.train(True)
+    print(f'clcye epochs ={i}')
+    model=VitsModel.from_pretrained(dir_model).to(device)
+    # model.setMfA(monotonic_align.maximum_path)
+    #dir_model_save=dir_model+'/vend'
+
+
+    trainer_to_cuda(model,
+                    ctrain_datasets = ctrain_datasets,
+                    eval_dataset = eval_dataset,
+                    full_generation_dataset = ctrain_datasets[0][0],
+                    feature_extractor = VitsFeatureExtractor(),
+                    training_args = training_args,
+                    full_generation_sample_index= -1,
+                    project_name = "AZ",
+                    wandbKey = "782b6a6e82bbb5a5348de0d3c7d40d1e76351e79",
+                    is_used_text_encoder=True,
+                    is_used_posterior_encode=True,
+                #    dict_state_grad_loss=sgl,
+                    nk=50,
+                        path_save_model=dir_model,
+                    maf=monotonic_align.maximum_path,
+
+                    n_back_save_model=3000,
+                    start_speeker=0,
+                    end_speeker=1,
+                    n_epoch=i*training_args.num_train_epochs,
+
+
+                                  )