---

name: model-export description: "Preflight and run WeNet checkpoint export to TorchScript, ONNX CPU or GPU, IPEX, Horizon BPU, and deployment-ready model artifacts." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

WeNet Model Export

Use this sub-skill when the user has a trained WeNet checkpoint and wants TorchScript/JIT, quantized JIT, ONNX, IPEX, Horizon BPU, or other deployment artifacts.

Start here

1. Confirm the experiment has a compatible train.yaml and checkpoint. If the user provides a model directory, preflight it first:

   python sub-skills/model-export/scripts/check_export_inputs.py \
     --model-dir exp/conformer --mode jit
   

2. Read references/export-reference.md for export modes, command templates, required files, streaming chunk choices, output artifacts, and optional dependencies.

3. Read references/troubleshooting.md when files are missing, ONNX dependencies are absent, streaming parameters are inconsistent, CUDA providers are unavailable, quantized export fails, or a vendor toolchain is missing.

Route by task

• Use ../training-and-decoding/SKILL.md when the user still needs to train, average, or locate checkpoints.
• Use ../runtime-deployment/SKILL.md after export to choose C++/mobile/web/server runtime targets.
• Use ../package-transcription/SKILL.md when a local model directory is intended only for package load_model().

Key decisions

• JIT export is the common CPU-safe deployment artifact for the C++ libtorch runtime.
• ONNX CPU export creates separate encoder, CTC, and decoder graphs and checks them with ONNX Runtime CPU when dependencies are installed.
• ONNX GPU, TensorRT, IPEX, Horizon BPU, and vendor exports require matching optional packages and toolchains. Treat parser acceptance as weaker evidence than a completed export and runtime check.
• Streaming export must use chunk settings compatible with the intended runtime mode; non-streaming -1/-1 export should not be reused as a streaming model.