Blaizzy/mlx-audio

Health v2 · maintenance only

Adoption not part of the score

7793 stars · 698 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

MLX-Audio is a Python library built on Apple's MLX framework for fast text-to-speech (TTS), speech-to-text (STT), and speech-to-speech (STS) inference on Apple Silicon Macs. It supports multiple model architectures, voice cloning, quantization, a CLI, an OpenAI-compatible REST API server with a web UI, and a Swift package for iOS/macOS.

Use cases

• generate speech from text on my mac
• transcribe audio files locally with whisper
• clone a voice for text-to-speech
• run a local openai-compatible tts api server
• convert text to speech on apple silicon
• stream speech-to-text with word-level timestamps
• on-device tts for ios app
• separate vocals from background audio

When to choose

• you want fast local speech inference on Apple Silicon M-series chips
• you need TTS, STT, or STS with voice cloning and multilingual support
• you want an OpenAI-compatible audio API server running locally
• you need quantized models for memory-efficient inference on a Mac

When to avoid

• you need GPU inference on NVIDIA/AMD hardware or Linux servers
• you need production-grade enterprise speech services with SLAs
• you work primarily on non-macOS platforms
• you need training or fine-tuning of speech models rather than inference

Facets

library · maturity active

tts speech-recognition audio-processing llm-inference cli http-server speech-processing machine-learning artificial-intelligence python cli apple-silicon mlx voice-cloning speech-to-speech quantization openai-compatible-api music-generation audio macos swift web-server

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