--- title: "mlx-audio vs FunASR" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/blaizzy-mlx-audio-vs-modelscope-funasr" tools: ["blaizzy-mlx-audio", "modelscope-funasr"] --- # mlx-audio vs FunASR *GraphCanon updated Sep 20, 2026* ## Verdict Pick mlx-audio if mlx-audio is designed to offer an efficient speech processing library on Apple's MLX framework for tasks involving TTS, STT, and STS operations; pick FunASR if funASR is an industrial-grade toolkit supporting real-time speech recognition across over 50 languages. [mlx-audio](https://blaizzy.github.io/mlx-audio/) reports 7.9k GitHub stars, 722 forks, and 107 open issues, last pushed Sep 18, 2026. [FunASR](https://github.com/modelscope/FunASR) has 20k stars, 2.0k forks, and 35 open issues, last pushed Sep 20, 2026. Figures are from public GitHub metadata via [mlx-audio's repository](https://github.com/Blaizzy/mlx-audio) and [FunASR's repository](https://github.com/modelscope/FunASR). | | [mlx-audio](/tools/blaizzy-mlx-audio.md) | [FunASR](/tools/modelscope-funasr.md) | | --- | --- | --- | | Tagline | A text-to-speech (TTS), speech-to-text (STT) and speech-to-speech (STS) library on Apple's MLX framework. | Industrial-grade speech recognition toolkit | | Stars | 7,914 | 20,433 | | Forks | 722 | 2,039 | | Open issues | 107 | 35 | | Language | Python | Python | | Adopt for | mlx-audio is designed to offer an efficient speech processing library on Apple's MLX framework for tasks involving TTS, STT, and STS operations. | FunASR is an industrial-grade toolkit supporting real-time speech recognition across over 50 languages. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT License allows free use and modification. | | Categories | Speech & Audio | Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [mlx-audio](/tools/blaizzy-mlx-audio.md) | [FunASR](/tools/modelscope-funasr.md) | | --- | --- | --- | | Days since push | 1d | 0d | | Open issues (now) | 107 | 35 | | Stars delta | +275 (30d) | +879 (30d) | | Open issues delta | +19 (30d) | +30 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/blaizzy-mlx-audio/trust.md) | [trust report](/tools/modelscope-funasr/trust.md) | ## Shared compatibility - **Python**: [mlx-audio](/tools/blaizzy-mlx-audio.md) - Python runtime; [FunASR](/tools/modelscope-funasr.md) - Python runtime ## Decision facts: mlx-audio - **Adopt for:** mlx-audio is designed to offer an efficient speech processing library on Apple's MLX framework for tasks involving TTS, STT, and STS operations. ## Decision facts: FunASR - **Requirements:** Requires Python ≥ 3.8.; PyTorch and torchaudio must be installed separately before running FunASR. - **Adopt for:** FunASR is an industrial-grade toolkit supporting real-time speech recognition across over 50 languages. - **License detail:** MIT License allows free use and modification. - **Runtime:** unknown ## Choose when ### Choose mlx-audio if… - Tags unique to mlx-audio: apple-silicon, audio-processing, mlx, multimodal. - Use mlx-audio if you require high performance in text-to-speech, speech-to-text, or speech-to-speech transformations specifically optimized for Apple Silicon Macs (M1/M2/M3/M4). ### Choose FunASR if… - Requirements: Requires Python ≥ 3.8.; PyTorch and torchaudio must be installed separately before running FunASR.. - Tags unique to FunASR: asr, audio, chinese, emotion-recognition. - When requiring high-speed real-time processing of up to 170x realtime speech recognition. ## When NOT to use mlx-audio - Do not use mlx-audio if your project or target hardware is not based on Apple Silicon. It requires specifically designed optimizations that do not apply to Intel processors. - Avoid mlx-audio when the dependency on ffmpeg for audio format handling becomes a limitation due to licensing, compatibility with existing pipelines, or specific codec requirements. - Do not opt for mlx-audio if your application does not require seamless integration within the MLX framework and does not gain any significant benefit from its specialized support. ## When NOT to use FunASR - In scenarios where only a narrow set of languages are required, as overhead for supporting over 50 languages may be unnecessary. - For projects lacking GPU resources since certain models, like the Fun-ASR-Nano flagship model, require a GPU to run effectively. ## Common questions ### What is the difference between mlx-audio and FunASR? mlx-audio: A text-to-speech (TTS), speech-to-text (STT) and speech-to-speech (STS) library on Apple's MLX framework.. FunASR: Industrial-grade speech recognition toolkit. See the comparison table for live GitHub stats and shared categories. ### When should I choose mlx-audio over FunASR? Choose mlx-audio over FunASR when Tags unique to mlx-audio: apple-silicon, audio-processing, mlx, multimodal; Use mlx-audio if you require high performance in text-to-speech, speech-to-text, or speech-to-speech transformations specifically optimized for Apple Silicon Macs (M1/M2/M3/M4). ### When should I choose FunASR over mlx-audio? Choose FunASR over mlx-audio when Requirements: Requires Python ≥ 3.8.; PyTorch and torchaudio must be installed separately before running FunASR.; Tags unique to FunASR: asr, audio, chinese, emotion-recognition; When requiring high-speed real-time processing of up to 170x realtime speech recognition. ### When should I avoid mlx-audio? Do not use mlx-audio if your project or target hardware is not based on Apple Silicon. It requires specifically designed optimizations that do not apply to Intel processors. Avoid mlx-audio when the dependency on ffmpeg for audio format handling becomes a limitation due to licensing, compatibility with existing pipelines, or specific codec requirements. Do not opt for mlx-audio if your application does not require seamless integration within the MLX framework and does not gain any significant benefit from its specialized support. ### When should I avoid FunASR? In scenarios where only a narrow set of languages are required, as overhead for supporting over 50 languages may be unnecessary. For projects lacking GPU resources since certain models, like the Fun-ASR-Nano flagship model, require a GPU to run effectively. ### Is mlx-audio or FunASR more popular on GitHub? FunASR has more GitHub stars (20,433 vs 7,914). Stars measure visibility, not whether either tool fits your constraints. ### Are mlx-audio and FunASR open source? Yes - both are open-source projects on GitHub (mlx-audio: MIT, FunASR: MIT). ### Where can I find alternatives to mlx-audio or FunASR? GraphCanon lists graph-backed alternatives at [mlx-audio alternatives](/tools/blaizzy-mlx-audio/alternatives) and [FunASR alternatives](/tools/modelscope-funasr/alternatives) ([mlx-audio markdown twin](/tools/blaizzy-mlx-audio/alternatives.md), [FunASR markdown twin](/tools/modelscope-funasr/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/blaizzy-mlx-audio-vs-modelscope-funasr.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, mlx-audio or FunASR? mlx-audio: Very active. FunASR: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for mlx-audio and FunASR? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [mlx-audio trust report](/tools/blaizzy-mlx-audio/trust); [FunASR trust report](/tools/modelscope-funasr/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=blaizzy-mlx-audio`](/api/graphcanon/graph?tool=blaizzy-mlx-audio) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_