---

name: wenet description: "Use WeNet for ASR package transcription, data preparation, training and decoding recipes, model export, and production runtime deployment." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

WeNet Repo Skill

Use this skill when the task involves WeNet, an end-to-end speech recognition (ASR) toolkit with package transcription, staged training recipes, model export, and production runtime deployment.

Quick install/import check

For package transcription or API inspection, install WeNet in the target Python environment and verify imports:

python - <<'PY'
import wenet
from wenet import load_model, load_feature, load_tokenizer
print("WeNet import OK")
PY

For broader workflow checks, run the bundled environment checker:

python scripts/check_wenet_environment.py --device cpu

Prefer device=cpu for safe diagnostics. CUDA, NPU, OpenVINO, IPEX, BPU, XPU, mobile, and Triton/TensorRT paths require target-specific dependencies and hardware.

Route by task

User task	Read
Transcribe one audio file with the installed wenet CLI/API; load a built-in or local model; validate a model directory; debug package import/backend selection.	sub-skills/package-transcription/SKILL.md
Build or validate wav.scp, text, raw/shard data.list, dictionaries, tokenizer resources, CMVN inputs, or custom ASR data layout.	sub-skills/data-preparation/SKILL.md
Adapt staged recipes; train/fine-tune; resume checkpoints; average models; run offline recognition; compare decoding modes; compute WER/CER; use LM/k2/context/LoRA paths.	sub-skills/training-and-decoding/SKILL.md
Export a trained checkpoint/config to TorchScript/JIT, quantized JIT, ONNX CPU/GPU, IPEX, or Horizon BPU artifacts.	sub-skills/model-export/SKILL.md
Choose and prepare production runtime deployment for libtorch, ONNX Runtime, OpenVINO, Android/iOS, web, Raspberry Pi, Triton/TensorRT, IPEX, BPU, or XPU.	sub-skills/runtime-deployment/SKILL.md

Shared references and scripts

• Read references/troubleshooting.md for cross-cutting installation, optional backend, artifact mismatch, network, and long-job issues.
• Read references/repo-provenance.md before deciding whether this skill is current for a WeNet checkout.
• references/repo-routing-metadata.json contains structured router metadata for managed repo-skill importers.
• Run scripts/check_wenet_environment.py to safely inspect package import and CPU/CUDA/NPU visibility.

Operating rules

• Do not launch downloads, full training, shard packaging, CMVN over large corpora, runtime builds, or services until the user approves network, storage, hardware, port, and runtime cost.
• Keep model artifacts together: train.yaml, checkpoint, units.txt, tokenizer resources, optional global_cmvn, and export metadata must match.
• Use the bundled scripts for safe validation; use WeNet's installed package or module entry points for real workflows in the user's environment.
• When a workflow spans multiple stages, validate data first, then train/decode, then export, then deploy.