FunASR: Open-source speech recognition toolkit for training, inference, streaming ASR, VAD, punctuation, speaker

by modelscope · MCP Server · ★ 21.0k

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install FunASR

pip install funasr
git clone https://github.com/modelscope/FunASR.git && cd FunASR

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 68/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

asraudiochineseemotion-recognitionfunasrmcp-servermultilingual-asropenai-compatible-apiparaformerpunctuation

Quick Facts

Stars20,953
Forks2,111
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score68/100
Open Issues48
Last Updated2026-11-07
Created2022-11-24
Platformsmcp, python
Est. Tokens~16k

About FunASR

(简体中文한국어) Industrial speech recognition. 170x faster than Whisper. 50+ languages. Speaker diarization · Emotion detection · Streaming · One API call Quick Start · Benchmark · Use cases · Models · <

Compatible Skills

These tools work well together with FunASR for enhanced workflows:

  • textgen — semantic(0.17)+complementary+shared_fw(openai)+same_lang+similar_pop+shared_platform (59%)
  • ChatTTS — semantic(0.17)+complementary+same_lang+similar_pop+shared_platform (56%)
  • bailing — semantic(0.18)+complementary+rare_topics+same_lang+similar_pop+shared_platform (56%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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