Speech, rendered on the metal.
An open-source, MLX-native speech library for Apple Silicon. Text-to-speech, voice cloning, dialogue, sound effects, and recognition, all running on the Apple GPU. Weights download on first use. Load any model by a short alias.
The laptop is the whole runtime.
Everything runs on the Apple GPU through MLX. No Python-side Torch, no server, nothing leaves the machine. Every model returns a real waveform, from text to audio.
Pure MLX runtime
No torch-backed inference under an MLX label. Weights ship as .safetensors with explicit remapping. The Apple GPU does the work.
Local & private
Converted weights download once, then run fully offline. Aliases and local checkpoint paths are interchangeable.
One clean interface
tts.load() / asr.load() in Python, or the mlx-speech CLI. Per-family scripts expose the full surface.
Fourteen models. One loader.
Synthesis, cloning, dialogue, editing, sound effects, and recognition. Every catalog entry links to its converted weights and model-family guide.
Published weights live under appautomaton on Hugging Face and load by alias or full repo id. Original checkpoints can also be loaded from local paths when a family guide documents that path.
Install. Load. Generate.
Requires an Apple Silicon Mac (M1 or later) and Python 3.13+. Weights download on first use.