Open-source speech recognition toolkit for training, inference, streaming ASR, VAD, punctuation, speaker diarization pipelines, and OpenAI-compatible/MCP serving.
C-Rank Grade: S (Elite) - 74/100
External PR Merge Rate: 94%
Response Time: 24h
First Timer Success: 100%
modelscope/FunASR has a recorded first-timer success rate of 100.0%. Repositories ranked S typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in modelscope/FunASR respond to incoming external pull requests in approximately 23.8 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.
The C-Rank™ system evaluates GitHub projects on a 0–100 scale using real data: PR merge rates, review turnaround time, active maintainer presence, and first-time contributor success. A score of 74.1 places modelscope/FunASR in the S tier.
The external contributor pull request merge rate for modelscope/FunASR is 94.5%, based on public PR activity from non-core contributors.
modelscope/FunASR currently has 2 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".
Highly welcoming maintainers in modelscope/FunASR. Prompt code reviews with positive guidance for new contributors.
Average Response Latency
Tracks hours until a maintainer leaves a review, comment, or PR response.
External Acceptance Rate
Percentage of community pull requests successfully merged into main.
First PR Conversion
Rate at which developers submitting their first repository PR succeed.
Show contributors that your repository actively reviews and merges external pull requests.
[](https://getmerged.abhishekco.de/modelscope/FunASR?utm_source=github&utm_medium=badge)
Historical health & responsiveness evolution
External PR acceptance
First PR success rate
50% receive response in
90% receive response in
Maintainer review activity heatmap & prime window prediction
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Detected: UTC
Maintainers are most responsive around 20:00 UTC (20:00 UTC in your timezone). Recommended submission window is Wednesday - Friday from 18:00 to 22:00 UTC (18:00 - 22:00 UTC local).
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~15 hours (96% approval). Large PRs (>500 lines) take ~114 hours (43% approval). Keep your diff compact for rapid review.
我在1个月前用的1.3.9， 后来项目结束就没再碰，今天注意到已经升级到 1.4.3 而且注意到 fun_asr_nano 的 ws 服务代码重构了，就测试一下它的性能，对比1.3.9 发现并发性能反而全面下滑 （L20机器）， 这是一段 47秒音频测试结果 (测试脚本是之前用的) for n in 8 10 12 14 16; do for rep in 1 2 3; do python funasr-test-scripts/bench_streaming_ws.py \ --server ws://localhost:10095 \ --audio test-audio-sample/古人.wav --concurrency $n done done Sessions 1.3.9 1.4.3 8 ✅ 8/8 · p50 48.4 · 1st 1.3 · RTFx 7.6 ⚠️ 7-8/8 抖 · p50 72.3 · 1st 0.7 · RTFx 5.1 10 ✅ 10/10 · p50 51.6 · RTFx 8.9 ⚠️ 3-6/10 崩 · p50 ~70-79 · RTFx 5.9 12 ✅ 12/12 · p50 54.8 · RTFx 10.0 ❌ ALL FAILED 14 ✅ 14/14 · p50 60.7 · RTFx 9.5 ❌ ping timeout 16 ✅ 16/16 · p50 64.8 · RTFx 10.7 ❌ ping timeout 这个结果让我很意外，所以读了一下 realtime_
Bug Report: llama-funasr-sensevoice Vulkan Backend Crashes on AMD GPUs on Windows 中文报告 问题描述 在 Windows 系统下，使用 llama-funasr-sensevoice.exe 的 Vulkan 后端（--backend vulkan）时，程序在 AMD GPU 上崩溃，异常代码 0xc0000005（访问冲突）。 环境信息 OS: Windows 11 GPU: AMD Radeon RX 7600M XT (RDNA3) + AMD Radeon 780M (RDNA3, integrated) AMD 驱动版本: 32.0.31035.1003 (2026年7月24日，最新版) FunASR 版本: runtime-llamacpp-v0.1.9 (funasr-llamacpp-windows-x64-vulkan.zip) 模型: sensevoice-small-f16.gguf + fsmn-vad.gguf 复现步骤 下载并解压 funasr-llamacpp-windows-x64-vulkan.zip 下载模型：sensevoice-small-f16.gguf 和 fsmn-vad.gguf 运行命令： .\llama-funasr-sensevoice.exe -m models\sensevoice-small-f16.gguf --vad models\fsmn-vad.gguf -a audio\examples\zh.mp3 --backend vulkan 预期行为 程序应使用 AMD GPU (Vulkan) 完成语音识别并输出转录文本。 实际行为 程序在 GPU 设备检测后
我读完 community_growth_20k.md 我觉得应该再有一个 roadmap， 制定下一步的发展方向，甚至包括哪些 help wanted . 另外有个小问题，我好像以前也问过，但是看了 community_growth_20k 也没搞懂，就是 FunAudioLLM/Fun-ASR 这个项目的作用, 我能理解 FunAudioLLM 要强调 LLM-ASR model， 但还是搞不太清和 modelscope/FunASR 分工在哪里；另外如果作为使用者，需要知道 FunAudioLLM/Fun-ASR 这个项目吗？ Repository Role in the ecosystem Growth surface modelscope/FunASR Core toolkit, Python package, runtime services, deployment docs First-run success, production deployment, issue/PR operations FunAudioLLM/Fun-ASR Fun-ASR-Nano model family and LLM-ASR identity Model cards, Transformers integration, benchmarks, vLLM/GGUF stories 我们关于 Fun-ASR-Nano 问题讨论也都是在 modelscope/FunASR 里不是在 FunAudioLLM/Fun-ASR | Fun-ASR-Nano 然后我读 modelscope/FunASR 的 serve_realtime_ws.py 和
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When evaluating whether to contribute to modelscope/FunASR, response velocity and maintainer engagement are crucial. GetMerged continuously tracks pull request trajectories, first-comment latency, and code review rounds to help developers avoid submitting pull requests to backlogged repositories.
Currently, maintainers of modelscope/FunASR acknowledge new external contributions in approximately 23 hours. Out of all submitted pull requests from non-core authors in the last 180-day window, 94.5% were successfully merged into the primary branch.
modelscope/FunASR has a recorded first-timer success rate of 100.0%. Repositories ranked Elite typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~23 hours. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.
The C-Rank™ index scores repositories on a 0 to 100 scale using an objective formula: external PR merge rates, initial response speed, active maintainer count, and first-time contributor retention. A score of 74.1 places modelscope/FunASR in the Elite tier.
The external pull request merge rate is 94.5%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
Yes, modelscope/FunASR currently has 2 active issue(s) tagged with beginner-friendly labels. You can inspect these directly from the repository issues tab.
All metrics displayed for modelscope/FunASR are automatically retrieved via the public GitHub API and recalculated daily. Insider pull requests submitted by repository owners or organization members are excluded from merge rate calculations to preserve objective external contributor statistics.