Comparison
mlx-audio vs FunASR
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.
Markdown twin · mlx-audio alternatives · FunASR alternatives
GraphCanon updated Sep 20, 2026
vs
Trust & integrity
| Signal | mlx-audio | FunASR |
|---|---|---|
| Maintenance | Very active (1d since push) As of Sep 20, 2026 · github_public_v1 | Very active (0d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 20, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Aug 16, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- 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
Stars
- mlx-audio
- 7.9k
- FunASR
- 20k
Forks
- mlx-audio
- 722
- FunASR
- 2.0k
Open issues
- mlx-audio
- 107
- FunASR
- 35
Language
- mlx-audio
- Python
- FunASR
- Python
Adopt for
- mlx-audio
- 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
- FunASR is an industrial-grade toolkit supporting real-time speech recognition across over 50 languages.
Persona
- mlx-audio
- -
- FunASR
- -
Runtime
- mlx-audio
- -
- FunASR
- -
License
- mlx-audio
- MIT
- FunASR
- MIT License allows free use and modification.
Last pushed
- mlx-audio
- Sep 18, 2026
- FunASR
- Sep 20, 2026
Categories
- mlx-audio
- Speech & Audio
- FunASR
- Speech & Audio
Trust and health
Days since push
- mlx-audio
- 1d
- FunASR
- 0d
Open issues (now)
- mlx-audio
- 107
- FunASR
- 35
Stars delta
- mlx-audio
- +275 (30d)
- FunASR
- +879 (30d)
Open issues delta
- mlx-audio
- +19 (30d)
- FunASR
- +30 (30d)
Owner type
- mlx-audio
- User
- FunASR
- Organization
Full report
- mlx-audio
- Trust report
- FunASR
- Trust report
Shared compatibility
- Python · mlx-audio: Python runtime · FunASR: Python runtime
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).
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.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (Blaizzy/mlx-audio) · observed Sep 20, 2026
- GitHub forks (Blaizzy/mlx-audio) · observed Sep 20, 2026
- Last push (Blaizzy/mlx-audio) · observed Sep 18, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (modelscope/FunASR) · observed Sep 20, 2026
- GitHub forks (modelscope/FunASR) · observed Sep 20, 2026
- Last push (modelscope/FunASR) · observed Sep 20, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Aug 16, 2026
GitHub stars on cards: mlx-audio 7.9k · FunASR 20k (synced Sep 20, 2026).
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 and FunASR alternatives (mlx-audio markdown twin, FunASR markdown twin), 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 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; FunASR trust report.