Update app.py

app.py

@@ -83,10 +83,21 @@ if audio_file:
     # ================================
     # ✅ Preprocess Audio with Processor (Corrected)
     # ================================
+    # Ensure the input has batch dimension (even if it's one example)
     inputs = processor(adversarial_waveform.squeeze().numpy(), sampling_rate=16000, return_tensors="pt", padding=True)
 
-    # Ensure the input has batch dimension (even if it's one example)
-    input_features = inputs["input_features"].to("cuda" if torch.cuda.is_available() else "cpu")
+    # Check the structure of the returned `inputs` to understand what it contains
+    st.write("Processor Output:", inputs)
+
+    # Extract the correct key (input_features or input_values depending on the model)
+    if "input_features" in inputs:
+        input_features = inputs["input_features"]
+    elif "input_values" in inputs:
+        input_features = inputs["input_values"]
+    else:
+        raise ValueError("❌ The processor output does not contain 'input_features' or 'input_values'.")
+
+    input_features = input_features.to("cuda" if torch.cuda.is_available() else "cpu")
 
     # ================================
     # ✅ Fast Transcription Processing with Conformer