#31 · Primary category： Speech & Audio
mlx-audio
A text-to-speech (TTS), speech-to-text (STT) and speech-to-speech (STS) library built on Apple's MLX framework, providing efficient speech analysis on Apple Silicon.
Project last updated：08/29/26
GitHub Stars
7.8K
Forks
700
Contributors
114
License
MIT
Why we included this project
Apple Silicon users get a single, MLX-native library that covers text-to-speech, speech-to-text, and speech-to-speech. You can generate voices with models like Kokoro and Qwen3-TTS, transcribe audio, and chain the two into conversational pipelines without stitching together separate tools. On-device deployment is a real option: quantization from 3-bit to 8-bit keeps models lean, and a Swift package brings the same capabilities to iOS and macOS apps. An OpenAI-compatible REST API and an interactive web interface with audio visualization make it easy to prototype or drop into existing services. For teams already building on M-series chips, this is a practical way to ship real-time speech features in Python or Swift.
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