Back to browse
GitHub Repository

OpenClaw local TTS plugin powered by mlx-audio, zero API key, zero cloud dependency

14 starsTypeScript

Local TTS for OpenClaw on Apple Silicon (MLX-Powered, Zero Setup)

by ZacharyZZ·Feb 20, 2026·2 points·0 comments

AI Analysis

●●SolidNiche GemCozy

MLX-powered local TTS plugin for OpenClaw—elegant but audience is Apple Silicon only.

Strengths
  • Zero setup: bootstrap uv, download model, auto-restart on crash
  • OpenAI endpoint compatibility means drop-in replacement
  • Multi-language support (Kokoro, Qwen3) with 3-sec voice cloning option
Weaknesses
  • Locked to Apple Silicon M1+; no Windows/Linux/Intel support
  • Audience is intersection of OpenClaw users + macOS—very narrow
  • Existing alternatives (Edge TTS in OpenClaw, Self-hosted GPU servers) already work
Target Audience

Apple Silicon Mac users running OpenClaw; developers needing private/offline TTS

Similar To

OpenClaw built-in Edge TTS · oobabooga local TTS · Bark

Post Description

I built an OpenClaw plugin that runs text-to-speech entirely on your Mac. No API keys, no cloud, no pre-installed Python required.

It wraps mlx-audio and handles the full lifecycle: bootstraps its own Python environment via uv, downloads the model on first run, manages the server process, auto-restarts on crash, and exposes a standard OpenAI-compatible /v1/audio/speech endpoint.

Installation:

openclaw plugin install @cosformula/openclaw-mlx-audio Four models out of the box:

• Kokoro-82M: ~400 MB RAM, fastest, good for English/Japanese • Qwen3-TTS-0.6B: ~1.4 GB RAM, best Chinese quality, 3-second voice cloning • Qwen3-TTS-1.7B VoiceDesign: generate voices from text descriptions • Chatterbox: 16 languages, ~3.5 GB RAM

Works on 8 GB Macs with Kokoro or Qwen3-0.6B. A proxy layer injects model-specific parameters so OpenClaw's TTS client needs zero changes.

Why not just run mlx-audio directly? You can. This plugin removes the setup friction: no Python version juggling, no pip install, no manual server management. It also adds OOM detection, memory pre-checks, startup progress tracking, and hot config reload.

GitHub: https://github.com/cosformula/openclaw-mlx-audio

Similar Projects

AI/ML●●●Banger

Rapid-MLX – Run local LLMs on Mac, 2-3x faster than alternatives

Claims 4.2x Ollama speed with 0.08s cached TTFT on Apple Silicon.

WizardrySolve My Problem
raullen
941mo ago
AI/ML●●●Banger

I built a free CharacterAI that runs locally

Free local CharacterAI with voice cloning under 10s audio, plus ESP32 hardware integration.

Zero to OneWizardrySolve My Problem
akadeb
842mo ago
AI/ML●●●Banger

SwiftLM – Qwen Chat on iPhone, 100B+ Moe on M5 Pro 64GB (Native Swift)

Native Swift inference with SSD streaming runs 100B MoE models without kernel panics.

WizardryNiche Gem
aegis_camera
122mo ago
Data●●Solid

Benchmarking Apple Silicon unified mem for GPU-accelerated SQL analysis

The repo does one practical thing well: quantify the real-world impact of Apple Silicon's unified memory on analytics by running six TPC-H queries plus a GPU-favorable QX and shipping the raw charts and code. It's specific and empirical — you get MLX vs NumPy vs DuckDB numbers and PNGs, not just hand-wavy claims — but it's narrowly scoped to M4 hardware and small-ish scales, so its conclusions are useful for experimentation rather than sweeping generalization.

WizardryNiche Gem
sadopc
313mo ago