diff --git a/tutorials/whisper_e2e.py b/tutorials/whisper_e2e.py
index bbf3188f..07779e9d 100644
--- a/tutorials/whisper_e2e.py
+++ b/tutorials/whisper_e2e.py
@@ -56,6 +56,48 @@ def process_test_file():
     return fn_pre(np.expand_dims(raw_audio, axis=0)), pre_m, post_m
 
 
+def get_model_inputs(ort_session, audio_data):
+    ort_names = list(map(lambda entry: entry.name, ort_session.get_inputs()))
+    print(ort_names)
+
+    inputs = [
+        audio_data,                          # audio_stream/input_features
+        np.asarray([200], dtype=np.int32),   # max_length
+        np.asarray([0], dtype=np.int32),     # min_length
+        np.asarray([2], dtype=np.int32),     # num_beams
+        np.asarray([1], dtype=np.int32),     # num_return_sequences
+        np.asarray([1.0], dtype=np.float32), # length_penalty
+        np.asarray([1.0], dtype=np.float32), # repetition_penalty
+    ]
+    required_input_names = {"audio_stream", "input_features", "max_length", "min_length", "num_beams",
+                            "num_return_sequences", "length_penalty", "repetition_penalty"}
+
+    # Add optional inputs if present in model
+    batch_size = 1
+    N_MELS = 80, N_FRAMES = 3000
+
+    vocab_size = 51864 if ".en" in MODEL_NAME else 51865
+    decoder_start_token_id = 50257 if ".en" in MODEL_NAME else 50258
+
+    for name in ort_names:
+        if name in required_input_names:
+            continue
+        elif name == "vocab_mask":
+            inputs.append(np.ones(vocab_size, dtype=np.int32))
+        elif name == "prefix_vocab_mask":
+            inputs.append(np.ones((batch_size, vocab_size), dtype=np.int32))
+        elif name == "attention_mask":
+            # For older ORT versions that have the dummy attention mask input for the beam search op
+            inputs.append(np.zeros((batch_size, N_MELS, N_FRAMES), dtype=np.int32))
+        elif name == "decoder_input_ids":
+            inputs.append(np.array([[decoder_start_token_id]], dtype=np.int32))
+        elif name == "logits_processor":
+            inputs.append(np.array([1], dtype=np.int32))
+        else:
+            raise NotImplementedError(f"'{name}' input is not supported")
+    return inputs
+
+
 def main():
     check_onnx_version()
     export_onnx_model()
@@ -63,13 +105,9 @@ def main():
 
     # Apply core ONNX model
     fn_core = OrtPyFunction.from_model(os.path.join(OUTPUT_DIR, "whisper-tiny.en_beamsearch.onnx"), cpu_only=True)
-    token_seq = fn_core(log_mel,
-                        np.asarray([200], dtype=np.int32),
-                        np.asarray([0], dtype=np.int32),
-                        np.asarray([2], dtype=np.int32),
-                        np.asarray([1], dtype=np.int32),
-                        np.asarray([1.0], dtype=np.float32),
-                        np.asarray([1.0], dtype=np.float32))
+    fn_core_ort_session = fn_core._ensure_ort_session()
+    model_inputs = get_model_inputs(fn_core_ort_session, log_mel)
+    token_seq = fn_core(*model_inputs)
     print(token_seq.shape)
 
     # Apply post processing
@@ -84,14 +122,11 @@ def main():
 
     # Test the final model
     raw_audio = np.fromfile(TEST_AUDIO_FILE, dtype=np.uint8)
-    text = OrtPyFunction.from_model(final_m, cpu_only=True)(
-        np.expand_dims(raw_audio, axis=0),
-        np.asarray([200], dtype=np.int32),
-        np.asarray([0], dtype=np.int32),
-        np.asarray([2], dtype=np.int32),
-        np.asarray([1], dtype=np.int32),
-        np.asarray([1.0], dtype=np.float32),
-        np.asarray([1.0], dtype=np.float32))
+    raw_audio = np.expand_dims(raw_audio, axis=0)
+    e2e_model = OrtPyFunction.from_model(final_m, cpu_only=True)
+    e2e_model_ort_session = e2e_model._ensure_ort_session()
+    model_inputs = get_model_inputs(e2e_model_ort_session, raw_audio)
+    text = e2e_model(*model_inputs)
     print(text)