"Use WeNet as an installed Python package or console CLI for speech
Scanned 9/9/2026
Install to Claude Code
npx -y skills add VectorSpaceLab/AREX-Skill --skill package-transcription --agent claude-codeInstalls into .claude/skills of the current project.
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---
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:
```bash
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:
```bash
wenet --help
```
3. For local model directories, run the bundled checker before loading the
model:
```bash
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](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](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](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](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](../training-and-decoding/SKILL.md), and
route export/deployment artifacts to
[../model-export/SKILL.md](../model-export/SKILL.md).
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