--- title: "mlx-audio vs FluidAudio" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/blaizzy-mlx-audio-vs-fluidinference-fluidaudio" tools: ["blaizzy-mlx-audio", "fluidinference-fluidaudio"] --- # mlx-audio vs FluidAudio *GraphCanon updated Jul 30, 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 FluidAudio if fluidAudio provides CoreML-based models for tasks like text-to-speech, speech-to-text, voice activity detection, and speaker diarization in Swift, focused on iOS and macOS. [mlx-audio](https://blaizzy.github.io/mlx-audio/) reports 7.6k GitHub stars, 680 forks, and 88 open issues, last pushed Jul 28, 2026. [FluidAudio](https://docs.fluidinference.com/introduction) has 2.6k stars, 360 forks, and 19 open issues, last pushed Jul 26, 2026. Figures are from public GitHub metadata via [mlx-audio's repository](https://github.com/Blaizzy/mlx-audio) and [FluidAudio's repository](https://github.com/FluidInference/FluidAudio). | | [mlx-audio](/tools/blaizzy-mlx-audio.md) | [FluidAudio](/tools/fluidinference-fluidaudio.md) | | --- | --- | --- | | Tagline | A text-to-speech (TTS), speech-to-text (STT) and speech-to-speech (STS) library on Apple's MLX framework. | CoreML audio models for text-to-speech, speech-to-text, voice activity detection and speaker diarization in Swift. | | Stars | 7,639 | 2,554 | | Forks | 680 | 360 | | Open issues | 88 | 19 | | Language | Python | Swift | | 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. | FluidAudio provides CoreML-based models for tasks like text-to-speech, speech-to-text, voice activity detection, and speaker diarization in Swift, focused on iOS and macOS. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Speech & Audio | Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [mlx-audio](/tools/blaizzy-mlx-audio.md) | [FluidAudio](/tools/fluidinference-fluidaudio.md) | | --- | --- | --- | | Days since push | 0d | 4d | | Open issues (now) | 88 | 19 | | Owner type | User | Organization | | Full report | [trust report](/tools/blaizzy-mlx-audio/trust.md) | [trust report](/tools/fluidinference-fluidaudio/trust.md) | ## 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: FluidAudio - **Adopt for:** FluidAudio provides CoreML-based models for tasks like text-to-speech, speech-to-text, voice activity detection, and speaker diarization in Swift, focused on iOS and macOS. ## Choose when ### Choose mlx-audio if… - mlx-audio is primarily Python; FluidAudio is Swift. - License: mlx-audio is MIT, FluidAudio 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). ### Choose FluidAudio if… - FluidAudio is primarily Swift; mlx-audio is Python. - License: FluidAudio is Apache-2.0, mlx-audio is MIT. - Tags unique to FluidAudio: ane, asr, audio, automatic-speech-recognition. - You need accurate speech-to-text transcription with support for real-time processing in a Swift environment ## 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 FluidAudio - If your project requires cross-platform compatibility beyond Apple's ecosystem - For projects that do not require CoreML-based optimizations and can run on more universally adopted frameworks across multiple operating systems ## Common questions ### What is the difference between mlx-audio and FluidAudio? mlx-audio: A text-to-speech (TTS), speech-to-text (STT) and speech-to-speech (STS) library on Apple's MLX framework.. FluidAudio: CoreML audio models for text-to-speech, speech-to-text, voice activity detection and speaker diarization in Swift.. See the comparison table for live GitHub stats and shared categories. ### When should I choose mlx-audio over FluidAudio? Choose mlx-audio over FluidAudio when mlx-audio is primarily Python; FluidAudio is Swift; License: mlx-audio is MIT, FluidAudio 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 FluidAudio over mlx-audio? Choose FluidAudio over mlx-audio when FluidAudio is primarily Swift; mlx-audio is Python; License: FluidAudio is Apache-2.0, mlx-audio is MIT; Tags unique to FluidAudio: ane, asr, audio, automatic-speech-recognition; You need accurate speech-to-text transcription with support for real-time processing in a Swift environment. ### 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 FluidAudio? If your project requires cross-platform compatibility beyond Apple's ecosystem For projects that do not require CoreML-based optimizations and can run on more universally adopted frameworks across multiple operating systems ### Is mlx-audio or FluidAudio more popular on GitHub? mlx-audio has more GitHub stars (7,639 vs 2,554). Stars measure visibility, not whether either tool fits your constraints. ### Are mlx-audio and FluidAudio open source? Yes - both are open-source projects on GitHub (mlx-audio: MIT, FluidAudio: Apache-2.0). ### Where can I find alternatives to mlx-audio or FluidAudio? GraphCanon lists graph-backed alternatives at [mlx-audio alternatives](/tools/blaizzy-mlx-audio/alternatives) and [FluidAudio alternatives](/tools/fluidinference-fluidaudio/alternatives) ([mlx-audio markdown twin](/tools/blaizzy-mlx-audio/alternatives.md), [FluidAudio markdown twin](/tools/fluidinference-fluidaudio/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-fluidinference-fluidaudio.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, mlx-audio or FluidAudio? mlx-audio: Very active. FluidAudio: 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 FluidAudio? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [mlx-audio trust report](/tools/blaizzy-mlx-audio/trust); [FluidAudio trust report](/tools/fluidinference-fluidaudio/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/_