No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by modelscope · MCP Server · ★ 20.5k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is FunASR safe to install? View the security audit →
(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 · <
| Stars | 20,458 |
| Forks | 2,043 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 68.234495387616/100 |
| Open Issues | 34 |
| Last Updated | 2026-09-21 |
| Created | 2022-11-24 |
| Platforms | mcp, python |
| Est. Tokens | ~16k |
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FunASR is Open-source speech recognition toolkit for training, inference, streaming ASR, VAD, punctuation, speaker diarization pipelines, and OpenAI-compatible/MCP serving.. It is categorized as a MCP Server with 20.5k GitHub stars.
FunASR is primarily written in Python. It covers topics such as asr, audio, chinese.
You can find installation instructions and usage details in the FunASR GitHub repository at github.com/modelscope/FunASR. The project has 20.5k stars and 2043 forks, indicating an active community.
FunASR is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to FunASR on Agent Skills Hub include Scrapling, ruflo, context7. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. 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: