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name: serving-and-runtime description: "Own FunASR serving, realtime, MCP, client, and edge-runtime guidance without mixing in core ASR or vLLM family selection." metadata: disco-role: operating disable-model-invocation: true license: NOASSERTION

FunASR serving and runtime

Use this sub-skill when the user wants to expose FunASR over HTTP, WebSocket, MCP, browser or desktop clients, or runtime SDK / ONNX / GGUF / Triton deployment guidance. Keep it separate from core ASR inference, model choice, text normalization, training/export, and LLM-family routing.

Fast route selection

• OpenAI-compatible HTTP API, health checks, /v1/models, /v1/audio/transcriptions, or the native /asr route → references/openai-api.md
• Realtime WebSocket serving, chunk sizing, endpoint mode, hotwords, or final session flushes → references/realtime-websocket.md
• MCP server, browser/client integrations, LangChain, Dify, or voice-input workflows → references/mcp-and-client-integrations.md
• ONNXRuntime, libtorch, GGUF/llama.cpp, Triton, or other edge/runtime guidance → references/runtime-sdk-and-edge.md
• Missing packages, CORS, port conflicts, empty realtime output, MCP path problems, or build/backend caveats → references/troubleshooting.md
• Nano / GLM / Qwen3 / vLLM family choice or acceleration caveats → route to llm-asr-and-vllm
• Plain batch transcription, subtitles, audio decoding, or local model selection → route to python-asr-pipelines

Minimum facts to collect

1. Which surface is needed: HTTP API, realtime WebSocket, MCP, browser/client integration, or runtime/edge deployment?
2. Does the user want a packaged CLI smoke, a small helper script, or a deployment reference?
3. What runtime target matters: CPU, CUDA, MPS, ONNXRuntime, GGUF, Triton, or a service wrapper around a local model?
4. Does the user need verbose_json, speaker labels, endpoint mode control, or deterministic hotword post-processing?
5. Is the integration local-only, mounted-file MCP access, or a remote service URL?

Operating workflow

1. Prefer the packaged CLIs (funasr-server, funasr-realtime-server) for service guidance and smoke checks.
2. Use scripts/openai_api_smoke_test.py for a safe HTTP client that can stop after health/model checks when no audio file is supplied.
3. Use scripts/funasr_mcp_server.py for a small stdio tool when the caller needs agent-style local file transcription.
4. Keep Docker images, system-service launchers, and native runtime build instructions in the reference docs rather than as default runnable helpers.
5. Cross-check the native candidates listed in the integration plan before claiming this sub-skill is ready.

Safety and boundary notes

• Do not drift into generic Python ASR, punctuation cleanup, training/export, or vLLM family choice.
• The bundled helpers must remain self-contained, cross-platform where practical, and safe by default.
• Reference docs may show command shapes and caveats, but they should not imply that the original source-tree examples must be run verbatim.
• Do not claim the edge/runtime paths are production-ready until the referenced backend and build requirements are met.