---

name: package-transcription description: "Use WeNet as an installed Python package or console CLI for speech transcription, model loading, model-directory checks, and safe backend selection." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

WeNet Package Transcription

Use this sub-skill when the task is to transcribe audio with the installed wenet package, load a pretrained or local WeNet model from Python, validate a local model directory, or diagnose the user-facing package CLI.

Start here

1. Confirm the package imports before planning any download or inference:

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

2. For CLI tasks, inspect the installed console entry point:

   wenet --help
   

3. For local model directories, run the bundled checker before loading the model:

   python sub-skills/package-transcription/scripts/check_wenet_package.py \
     --model-dir /path/to/model_dir --device cpu
   

Route by task

• Read references/api-reference.md when the user asks for Python usage, model loading, model directory contents, feature or tokenizer loading, built-in model names, or return-object expectations.
• Read references/cli-reference.md when the user asks for the wenet command, CLI flags, alignment, context biasing, device selection, or punctuation options.
• Run scripts/check_wenet_package.py for safe import checks, backend availability checks, and local model-directory preflight validation. The script does not download models or transcribe audio.
• Read references/troubleshooting.md when imports fail, model loading downloads unexpectedly, a model directory is incomplete, --device cuda/--device npu fails, or audio/alignment/context options behave unexpectedly.

Key decisions

• Use the installed package API (import wenet) for simple transcription and model loading. Do not rely on source-checkout scripts for package tasks.
• Built-in model names may trigger a network download through the model hub. Validate network and storage constraints before calling load_model() with a built-in name.
• A local model directory must contain train.yaml, final.pt, and units.txt; global_cmvn is optional and used when present.
• device="cpu" is the safest default. device="cuda" and device="npu" require matching framework/runtime support in the user's environment; the CLI accepting the flag does not prove the backend exists.
• Route batch/offline decoding of a trained experiment to ../training-and-decoding/SKILL.md, and route export/deployment artifacts to ../model-export/SKILL.md.