Update app.py

app.py

@@ -98,6 +98,25 @@ dataset[0]["audio"]
 # **4**. Create a function to preprocess the audio `array` with the feature extractor, and truncate and pad the sequences into tidy rectangular tensors. The most important thing to remember is to call the audio `array` in the feature extractor since the `array` - the actual speech signal - is the model input.
 # 
 # Once you have a preprocessing function, use the [map()](https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Dataset.map) function to speed up processing by applying the function to batches of examples in the dataset.
+import torch
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # Use GPU if available
+model.to(device)  # Move model to the device
+
+optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5)  # Example optimizer
+
+for epoch in range(num_epochs):  # Replace num_epochs
+    for batch in dataloader:
+        input_values = batch["input_values"].to(device)
+        labels = batch["labels"].to(device)
+
+        optimizer.zero_grad()
+        outputs = model(input_values, labels=labels)
+        loss = outputs.loss
+        loss.backward()
+        optimizer.step()
+
+        print(f"Epoch: {epoch+1}, Loss: {loss.item()}") # Print Loss for monitoring
 
 # %%
 def preprocess_function(examples):