Update app.py

app.py

@@ -163,20 +163,21 @@ def convert_model_to_onnx():
     dummy_input = tokenizer(dummy_prompt, return_tensors="pt").input_ids.to(device)
     file_path = "orpheus_model.onnx"
     try:
-        # Export the model to ONNX format
-        torch.onnx.export(
-            model,
-            dummy_input,
-            file_path,
-            export_params=True,
-            opset_version=14,
-            input_names=["input_ids"],
-            output_names=["logits"],
-            dynamic_axes={
-                "input_ids": {0: "batch_size", 1: "sequence_length"},
-                "logits": {0: "batch_size", 1: "sequence_length"}
-            },
-        )
+        # Disable Torch Dynamo during export to avoid FX-tracing issues.
+        with torch._dynamo.disable():
+            torch.onnx.export(
+                model,
+                dummy_input,
+                file_path,
+                export_params=True,
+                opset_version=14,
+                input_names=["input_ids"],
+                output_names=["logits"],
+                dynamic_axes={
+                    "input_ids": {0: "batch_size", 1: "sequence_length"},
+                    "logits": {0: "batch_size", 1: "sequence_length"}
+                },
+            )
         return f"Model converted to ONNX and saved as '{file_path}'."
     except Exception as e:
         return f"Error during ONNX conversion: {e}"
@@ -192,10 +193,10 @@ VOICES = ["tara", "dan", "josh", "emma"]
 with gr.Blocks(title="Orpheus Text-to-Speech") as demo:
     gr.Markdown("""
     # 🎵 Orpheus Text-to-Speech
-    Enter text to hear it converted to natural-sounding speech.
+    Enter text to have it converted into natural-sounding speech.
     
     **Tips:**
-    - Use paralinguistic cues like `<chuckle>` or `<sigh>`.
+    - Include paralinguistic cues like `<chuckle>` or `<sigh>`.
     - Longer text can produce more natural results.
     """)
     with gr.Row():