Update app.py

app.py

@@ -37,26 +37,9 @@ from  VitsModelSplit.dataset_features_collector import FeaturesCollectionDataset
 from torch.cuda.amp import autocast, GradScaler
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-# model1= VitsModel.from_pretrined("/content/drive/MyDrive/vitsM/OneBatch/S6/MMMMM-dash-azd60").to("cuda")
-# model= VitsModel.from_pretrained("/content/drive/MyDrive/vitsM/TO/sp3/core/vend").to("cuda")
-# model=VitsModel.from_pretrained("/content/drive/MyDrive/vitsM/heppa/EndCore3/v0").to("cuda")
-# model.discriminator=model1.discriminator
-# model.duration_predictor=model1.duration_predictor
-
-# model.setMfA(monotonic_align.maximum_path)
-# tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-ara",cache_dir="./")
+
 feature_extractor = VitsFeatureExtractor()
-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)
-sgl=get_state_grad_loss(mel=True,
-                       # generator=False,
-                    #    discriminator=False,
-                        duration=False)
-
-training_args.num_train_epochs=1000
-training_args.fp16=True
-training_args.eval_steps=300
+
 # sgl=get_state_grad_loss(k1=True,#generator=False,
 #                         discriminator=False,
 #                         duration=False
@@ -573,24 +556,37 @@ train_dataset_dirs=[
 
 
 
-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
-global_step=0
+
 dir_model='wasmdashai/vits-ar-huba-fine'
 
 
 
 wandb.login(key= "782b6a6e82bbb5a5348de0d3c7d40d1e76351e79")
 wandb.init(project= 'AZ',config = training_args.to_dict())
-
+global_step=0
 
 @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)
+    sgl=get_state_grad_loss(mel=True,
+                           # generator=False,
+                        #    discriminator=False,
+                            duration=False)
+    
+    
+    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
+    
     b=int(id)
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     ctrain_datasets,eval_dataset,full_generation_dataset=get_data_loader(train_dataset_dirs = train_dataset_dirs,