Files
FunASR/docs
2026-09-09 22:31:09 +00:00
..
2023-01-16 18:53:59 +08:00
2023-01-16 18:53:59 +08:00

FunASR Documentation

中文文档 | English documentation | Model Zoo | Releases

Start Here

Your task Guide
Install and check the Python environment Installation / 中文
Choose a model, language, or timestamp capability Model selection / 中文
Run the first transcription Quickstart / 中文 / CLI
Use native Hugging Face Transformers APIs Fun-ASR-Nano native guide / 中文
Configure AutoModel, generate, and streaming cache Python SDK / 中文
Move from Whisper or a cloud API Migration / 中文

Train and Extend

Your task Guide
Select a recipe, prepare data, train and evaluate Fine-tuning / 中文
Add a custom architecture to AutoModel Model registration / 中文
Locate supported checkpoints and individual licenses Model Zoo / 中文

Models and Deployment

Your task Guide
Compare CPU, GPU, realtime and API runtimes Deployment matrix / 中文
Handle streaming speech boundaries and finalization Streaming VAD / 中文
Detect keywords and manage utterance-end KWS results Keyword spotting / 中文
Distinguish speaker vectors, anonymous labels and emotion tags Speakers and emotion tags / 中文
Transcribe and diarize with third-party MOSS MOSS / 中文
Accelerate with the FunASR vLLM split engine vLLM / 中文
Evaluate official native vLLM serving Official checkpoint validation / 中文
Inspect the historical community native vLLM run (2026-08-13) Historical community record
Deploy llama.cpp, TensorRT, Docker or Kubernetes Deployment manuals
Choose a service entry point and runtime SDK Runtime guide / 中文
Select a development or serving container Docker / 中文
Expose a Python HTTP transcription service Server / 中文

Integrate Against a Contract

Python SDK, Python HTTP, native vLLM HTTP and C++ WebSocket are different interfaces. Choose the matching protocol before selecting a client.

Evaluate and Operate

The product-site documentation is rendered from these Markdown sources. Edit the source once; the site build updates article content, navigation and local search. The catalogue lives in documentation.json. The established GitHub Pages tutorial, training and model-registration URLs are generated from the same guides. The API reference is separately extracted from Python source. Historical C++ release notes remain in the runtime archive; they are not current deployment recommendations. Model weights retain their individual licenses; the FunASR toolkit is MIT licensed.

Build the Product Documentation

Japanese and Korean legacy guides have repository-owned sources: Agent 日本語 / 한국어, historical ASR 日本語 / 한국어. The localized_pages catalogue exports them to their established ja/agent.html, ko/agent.html, ja/benchmark.html and ko/benchmark.html GitHub Pages addresses. Their old section links are retained. These translations do not create Japanese or Korean product-site navigation or search; the full product site remains bilingual.

From the repository root:

python -m pip install -r web-pages/product-site/requirements-site.txt
python web-pages/product-site/build.py --output /tmp/funasr-docs
python web-pages/product-site/validate.py /tmp/funasr-docs
python scripts/gen_api_docs.py
python web-pages/product-site/export_docs.py --site /tmp/funasr-docs --output gh-pages-output
python -m http.server --directory /tmp/funasr-docs 8000

Build the Sphinx Reference

The legacy Sphinx reference is a separate local build. Use an isolated environment with the dependency versions in the legacy-docs-links job of product-site.yml; installing only the FunASR package does not reproduce that builder. From the repository root:

python -m pytest tests/test_sphinx_links.py tests/test_reference_docs_contract.py -q
python -m sphinx -b html -n -E -d /tmp/funasr-sphinx-doctrees docs /tmp/funasr-sphinx-html

Open /tmp/funasr-sphinx-html/index.html after the build. Markdown heading fragments remain usable alongside existing Sphinx anchors, including numbered and Chinese headings. Missing destinations still produce warnings; inspect the build log rather than treating an exit code alone as proof of correct links. The GitHub Pages publishing workflow uses the product documentation exporter and generated API reference, not this standalone Sphinx output.