Update README.md

README.md

@@ -98,6 +98,46 @@ python <NEMO_ROOT>/examples/asr/speech_classification/frame_vad_infer.py \
   out_manifest_filepath=<Path of output manifest file>
 ```
 
+### Export a PyTorch model to ONNX
+
+```python
+import torch, onnx 
+from nemo.core import typecheck
+import nemo.collections.asr as nemo_asr
+typecheck.set_typecheck_enabled(False)
+
+ONNX_EXPORT_PATH = "frame_vad_multilingual_marblenet_v2.0.onnx"
+
+# Load pretrained frame-level VAD model and move to CPU in eval mode
+vad_model = nemo_asr.models.EncDecFrameClassificationModel.from_pretrained(
+    model_name="nvidia/frame_vad_multilingual_marblenet_v2.0"
+).eval().cpu()
+
+# Define input example for ONNX export
+B, F, T = 16, 80, 400  # batch, feature dim, sequence length
+inputs = {
+    "processed_signal": torch.randn(B, F, T).float(),
+    "processed_signal_length": torch.full((B,), T, dtype=torch.long)
+}
+
+# Export model to ONNX
+torch.onnx.export(
+    model=vad_model,
+    args=inputs,
+    f="frame_vad_multilingual_marblenet_v2.0.onnx",
+    input_names=["processed_signal", "processed_signal_length"],
+    output_names=["output"],
+    dynamic_axes={
+        "processed_signal": {0: "batch_size", 2: "sequence_length"},
+        "processed_signal_length": {0: "batch_size"},
+        "output": {0: "batch_size", 1: "sequence_length"}
+    }
+)
+
+# Validate exported ONNX model
+onnx.checker.check_model(onnx.load(ONNX_EXPORT_PATH))
+```
+
 ## Software Integration:
 **Runtime Engine(s):** 
 * NeMo-2.0.0 <br>