Update app.py

app.py

@@ -36,8 +36,8 @@ from VitsModelSplit.Arguments import DataTrainingArguments, ModelArguments, VITS
 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")
-model=VitsModel.from_pretrained("facebook/mms-tts-eng").to(device)
-# model1= VitsModel.from_pretrained("/content/drive/MyDrive/vitsM/OneBatch/S6/MMMMM-dash-azd60").to("cuda")
+
+# 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
@@ -570,10 +570,6 @@ train_dataset_dirs=[
 
                     ]
 
-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)
 
 
 
@@ -596,6 +592,12 @@ wandb.init(project= 'AZ',config = training_args.to_dict())
 def greet(text,id):
     global GK 
     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,
+                                                              eval_dataset_dir = os.path.join(dataset_dir,'eval'),
+                                                            full_generation_dir = os.path.join(dataset_dir,'full_generation'),
+                                                                     device=device)
+
     for i in range(10000):
 # model.train(True)
         print(f'clcye epochs ={i}')
@@ -628,11 +630,7 @@ def greet(text,id):
     
     
                                       )
-    while True:
-        GK+=1
-        texts=[text]*b
-        out=modelspeech(texts)
-        yield f"namber is {GK}"
+
 
 demo = gr.Interface(fn=greet, inputs=["text","text"], outputs="text")
 demo.launch()