GraphCanon
Home/Compare/mlx-audio vs Fun-ASR

Comparison

mlx-audio vs Fun-ASR

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 Fun-ASR if fun-ASR is a real-time speech recognition tool supporting 31 languages with capabilities for handling dialects, accents, and speaker diarization.

Markdown twin · mlx-audio alternatives · Fun-ASR alternatives

GraphCanon updated 3w

mlx-audio logo

mlx-audio

Blaizzy/mlx-audio

★ 7.6kpushed Jul 28, 2026
vs
Fun-ASR logo

Fun-ASR

FunAudioLLM/Fun-ASR

★ 1.4kpushed Jul 24, 2026

Trust & integrity

Signalmlx-audioFun-ASR
Maintenance
Very active (0d since push)
As of 4w · github_public_v1
Very active (6d since push)
As of 3w · github_public_v1
Provenance
Not a fork · Personal account
As of 4w · github_public_v1
Not a fork · Organization account
As of 3w · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of 1mo · osv@v1
Published findings
As of 1mo · 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
mlx-audio trust reportFun-ASR trust reportTrust methodology

Tagline

mlx-audio
A text-to-speech (TTS), speech-to-text (STT) and speech-to-speech (STS) library on Apple's MLX framework.
Fun-ASR
Fun-ASR-Nano LLM-ASR model supports 31 languages for real-time speech recognition tasks

Stars

mlx-audio
7.6k
Fun-ASR
1.4k

Forks

mlx-audio
680
Fun-ASR
141

Open issues

mlx-audio
88
Fun-ASR
0

Language

mlx-audio
Python
Fun-ASR
C

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.
Fun-ASR
Fun-ASR is a real-time speech recognition tool supporting 31 languages with capabilities for handling dialects, accents, and speaker diarization.

Persona

mlx-audio
-
Fun-ASR
-

Runtime

mlx-audio
-
Fun-ASR
-

License

mlx-audio
MIT
Fun-ASR
Apache-2.0

Last pushed

mlx-audio
Jul 28, 2026
Fun-ASR
Jul 24, 2026

Categories

mlx-audio
Speech & Audio
Fun-ASR
LLM Frameworks, Speech & Audio

Trust and health

Days since push

mlx-audio
0d
Fun-ASR
6d

Open issues (now)

mlx-audio
88
Fun-ASR
0

Owner type

mlx-audio
User
Fun-ASR
Organization

OSV dependency advisories

mlx-audio
No lockfile (source not queried)
Fun-ASR
Published findings

Full report

mlx-audio
Trust report
Fun-ASR
Trust report

Shared compatibility

  • Python · mlx-audio: Python runtime · Fun-ASR: Python runtime

Choose mlx-audio if…

  • mlx-audio is primarily Python; Fun-ASR is C.
  • License: mlx-audio is MIT, Fun-ASR is Apache-2.0.
  • 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.
GitHubDetails

Choose Fun-ASR if…

  • Fun-ASR is primarily C; mlx-audio is Python.
  • License: Fun-ASR is Apache-2.0, mlx-audio is MIT.
  • Pricing: Open source under Apache License 2.0 allows free use with attribution and adherence to the license terms..
  • Requirements: Installation requires Python packages `funasr` version >=1.3.3, and `vllm` version >=0.12.0..
  • Tags unique to Fun-ASR: asr, audio-language-model, multilingual-asr, real-time-asr.
  • Also covers LLM Frameworks.
  • When you need support for 31 different languages including handling of various dialects and accents.

When NOT to use Fun-ASR

  • Avoid if your project requires a specific programming language other than C as Fun-ASR is primarily developed in C.
  • If you do not require on-device processing, as this tool is designed for on-device inference which may limit scalability for cloud-based applications.
GitHubDetails

Explore

mlx-audio trust report →Fun-ASR trust report →Speech & Audio category →LLM Frameworks category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: mlx-audio 7.6k · Fun-ASR 1.4k (synced Jul 29, 2026).

Common questions

What is the difference between mlx-audio and Fun-ASR?
mlx-audio: A text-to-speech (TTS), speech-to-text (STT) and speech-to-speech (STS) library on Apple's MLX framework.. Fun-ASR: Fun-ASR-Nano LLM-ASR model supports 31 languages for real-time speech recognition tasks. See the comparison table for live GitHub stats and shared categories.
When should I choose mlx-audio over Fun-ASR?
Choose mlx-audio over Fun-ASR when mlx-audio is primarily Python; Fun-ASR is C; License: mlx-audio is MIT, Fun-ASR is Apache-2.0; 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 Fun-ASR over mlx-audio?
Choose Fun-ASR over mlx-audio when Fun-ASR is primarily C; mlx-audio is Python; License: Fun-ASR is Apache-2.0, mlx-audio is MIT; Pricing: Open source under Apache License 2.0 allows free use with attribution and adherence to the license terms.; Requirements: Installation requires Python packages funasr version >=1.3.3, and vllm version >=0.12.0.; Tags unique to Fun-ASR: asr, audio-language-model, multilingual-asr, real-time-asr; Also covers LLM Frameworks; When you need support for 31 different languages including handling of various dialects and accents.
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 Fun-ASR?
Avoid if your project requires a specific programming language other than C as Fun-ASR is primarily developed in C. If you do not require on-device processing, as this tool is designed for on-device inference which may limit scalability for cloud-based applications.
Is mlx-audio or Fun-ASR more popular on GitHub?
mlx-audio has more GitHub stars (7,639 vs 1,449). Stars measure visibility, not whether either tool fits your constraints.
Are mlx-audio and Fun-ASR open source?
Yes - both are open-source projects on GitHub (mlx-audio: MIT, Fun-ASR: Apache-2.0).
Where can I find alternatives to mlx-audio or Fun-ASR?
GraphCanon lists graph-backed alternatives at mlx-audio alternatives and Fun-ASR alternatives (mlx-audio markdown twin, Fun-ASR 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 Fun-ASR?
mlx-audio: Very active. Fun-ASR: 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 Fun-ASR?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: mlx-audio trust report; Fun-ASR trust report.

Was this helpful?

Anonymous feedback helps us improve pages and translations.