Update app.py

app.py

@@ -102,49 +102,48 @@ class LightningModel(pl.LightningModule):
             predicted_class = torch.argmax(output, dim=1)
         return predicted_class
 
-if __name__ == "__main__":
-    print(torch.cuda.mem_get_info())
-
-    model = LightningModel()
-
-    run_name = "wav2vec"
-
-    checkpoint_path = "./wav2vec-epoch=epoch=4.ckpt.ckpt"
-    checkpoint = torch.load(checkpoint_path)
-    model.load_state_dict(checkpoint['state_dict'])
-    trainer = Trainer(
-        gpus=1
-    )
-
-    #trainer.fit(model, train_dataloader=trainloader, val_dataloaders=valloader)
-    #trainer.test(model,dataloaders=testloader,verbose=True)
-
-    #with torch.no_grad():
-     #   y_hat = model(wav_tensor)
-
-    def trabscribe(audio):
-        wav_tensor,_ = audio
-        wav_tensor = resmaple(wav_tensor)
-        #model = model.to('cuda') 
-        y_hat = model.predict(wav_tensor)
-        labels = {0:"branch_address : enquiry about bank branch location",
-        1:"activate_card : enquiry about activating card products",
-        2:"past_transactions : enquiry about past transactions in a specific time period",
-        3:"dispatch_status : enquiry about the dispatch status of card products",
-        4:"outstanding_balance : enquiry about outstanding balance on card products",
-        5:"card_issue : report about an issue with using card products",
-        6:"ifsc_code : enquiry about IFSC code of bank branch",
-        7:"generate_pin : enquiry about changing or generating a new pin for their card product",
-        8:"unauthorised_transaction : report about an unauthorised or fraudulent transaction",
-        9:"loan_query : enquiry about different kinds of loans",
-        10:"balance_enquiry : enquiry about bank account balance",
-        11:"change_limit : enquiry about changing the limit for card products",
-        12:"block : enquiry about blocking card or banking product",
-        13:"lost : report about losing a card product}
-        return labels[y_hat]
-
-    print(y_hat)
-    get_intent = gr.Interface(fn = transcribe,
-                           gr.Audio(source="microphone"), outputs="text").launch()
-
-    
+
+print(torch.cuda.mem_get_info())
+
+model = LightningModel()
+
+run_name = "wav2vec"
+
+checkpoint_path = "./wav2vec-epoch=epoch=4.ckpt.ckpt"
+checkpoint = torch.load(checkpoint_path)
+model.load_state_dict(checkpoint['state_dict'])
+trainer = Trainer(
+    gpus=1
+)
+
+#trainer.fit(model, train_dataloader=trainloader, val_dataloaders=valloader)
+#trainer.test(model,dataloaders=testloader,verbose=True)
+
+#with torch.no_grad():
+ #   y_hat = model(wav_tensor)
+
+def trabscribe(audio):
+    wav_tensor,_ = audio
+    wav_tensor = resmaple(wav_tensor)
+    #model = model.to('cuda') 
+    y_hat = model.predict(wav_tensor)
+    labels = {0:"branch_address : enquiry about bank branch location",
+    1:"activate_card : enquiry about activating card products",
+    2:"past_transactions : enquiry about past transactions in a specific time period",
+    3:"dispatch_status : enquiry about the dispatch status of card products",
+    4:"outstanding_balance : enquiry about outstanding balance on card products",
+    5:"card_issue : report about an issue with using card products",
+    6:"ifsc_code : enquiry about IFSC code of bank branch",
+    7:"generate_pin : enquiry about changing or generating a new pin for their card product",
+    8:"unauthorised_transaction : report about an unauthorised or fraudulent transaction",
+    9:"loan_query : enquiry about different kinds of loans",
+    10:"balance_enquiry : enquiry about bank account balance",
+    11:"change_limit : enquiry about changing the limit for card products",
+    12:"block : enquiry about blocking card or banking product",
+    13:"lost : report about losing a card product}
+    return labels[y_hat]
+
+print(y_hat)
+get_intent = gr.Interface(fn = transcribe,
+                       gr.Audio(source="microphone"), outputs="text").launch()
+