bambu-studio-ai
Bambu Lab 3D printer control and automation. Activate when user mentions: printer status, 3D printing, slice, analyze model, generate 3D, AMS filament, print monitor, Bambu Lab, or any 3D printing task. Full pi...
Local 24x7 OpenAI-compatible API server for STT/TTS, powered by MLX on your Mac.
请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。
SkillHub CLI
skillhub install guoqiao/mlx-audio-server打开来源仓库,查看说明和兼容环境后,按来源提供的命令或步骤安装。
将来源链接提供给你的 Agent,让它先读取说明、核对依赖,并向你说明将执行的安装步骤。
Local 24x7 OpenAI-compatible API server for STT/TTS, powered by MLX on your Mac.
mlx-audio: The best audio processing library built on Apple's MLX framework, providing fast and efficient text-to-speech (TTS), speech-to-text (STT), and speech-to-speech (STS) on Apple Silicon.
guoqiao/tap/mlx-audio-server: Homebrew Formula to install mlx-audio with brew, and run mlx_audio.server as a LaunchAgent service on macOS.
mlx: macOS with Apple Siliconbrew: used to install deps if not availablebash ${baseDir}/install.sh
This script will:
mlx-audio-server from guoqiao/tap
mlx-audio-server
STT/Speech-To-Text(default model: mlx-community/glm-asr-nano-2512-8bit):
# input will be converted to wav with ffmpeg, if not yet.
# output will be transcript text only.
bash ${baseDir}/run_stt.sh <audio_or_video_path>
TTS/Text-To-Speech(default model: mlx-community/Qwen3-TTS-12Hz-1.7B-VoiceDesign-bf16):
# audio will be saved into a tmp dir, with default name `speech.wav`, and print to stdout.
bash ${baseDir}/run_tts.sh "Hello, Human!"
# or you can specify a output dir
bash ${baseDir}/run_tts.sh "Hello, Human!" ./output
# output will be audio path only.
You can use both scripts directly, or as example/reference.
这些信息来自收录时的来源分析,可能滞后;使用前请核对最新来源文件。
mlx-audio is an audio processing library built on Apple's MLX framework for text-to-speech (TTS), speech-to-text (STT), and speech-to-speech (STS) on Apple Silicon.
Currently, it is a python package with a few cli tools such as mlx_audio.server. It's dependencies for server is not working properly (yet), and has no builtin support to run it as system service in background.
This Homebrew Formula re-packages it for homebrew with missing deps, and the main focus is to add LaunchAgent service support for the mlx_audio.server on macOS, so you can use it as an 24x7 local OpenAI-compatible API server.
Tap and install:
# install the formula with cli tools, not only server
brew install --HEAD guoqiao/tap/mlx-audio-server
# start the mlx-audio.server as a LaunchAgent service
brew services start mlx-audio-server
# verify
curl -Ss http://localhost:8899/v1/models
Here are examples to use GLM-ASR-Nano-2512 model for STT with different ways:
with curl:
bash stt_glmasr.sh path/to/audio
with openai python sdk:
uv run openai_api_transcribe.py path/to/audio
with Spokenly iOS/macOS app, as OpenAI Compatible API:
URL: http://<IP>:8899
Model: mlx-community/glm-asr-nano-2512-8bit
API KEY: <blank>
NOTE: /v1 is not needed in URL here.
This is a Homebrew tap for the mlx-audio Python package, providing fast and efficient audio processing on Apple Silicon using MLX framework.
For more information, visit the mlx-audio repository.
# MLX Audio Server
Local 24x7 OpenAI-compatible API server for STT/TTS, powered by MLX on your Mac.
[mlx-audio](https://github.com/Blaizzy/mlx-audio): The best audio processing library built on Apple's MLX framework, providing fast and efficient text-to-speech (TTS), speech-to-text (STT), and speech-to-speech (STS) on Apple Silicon.
[guoqiao/tap/mlx-audio-server](https://github.com/guoqiao/homebrew-tap/blob/main/Formula/mlx-audio-server.rb): Homebrew Formula to install `mlx-audio` with `brew`, and run `mlx_audio.server` as a LaunchAgent service on macOS.
## Requirements
- `mlx`: macOS with Apple Silicon
- `brew`: used to install deps if not available
## Installation
```bash
bash ${baseDir}/install.sh
```
This script will:
- install ffmpeg/jq with brew if missing.
- install homebrew formula `mlx-audio-server` from `guoqiao/tap`
- start brew service for `mlx-audio-server`
## Usage
STT/Speech-To-Text(default model: **mlx-community/glm-asr-nano-2512-8bit**):
```bash
# input will be converted to wav with ffmpeg, if not yet.
# output will be transcript text only.
bash ${baseDir}/run_stt.sh <audio_or_video_path>
```
TTS/Text-To-Speech(default model: **mlx-community/Qwen3-TTS-12Hz-1.7B-VoiceDesign-bf16**):
```bash
# audio will be saved into a tmp dir, with default name `speech.wav`, and print to stdout.
bash ${baseDir}/run_tts.sh "Hello, Human!"
# or you can specify a output dir
bash ${baseDir}/run_tts.sh "Hello, Human!" ./output
# output will be audio path only.
```
You can use both scripts directly, or as example/reference.# Homebrew Formula mlx-audio-server [mlx-audio](https://github.com/Blaizzy/mlx-audio) is an audio processing library built on Apple's MLX framework for text-to-speech (TTS), speech-to-text (STT), and speech-to-speech (STS) on Apple Silicon. Currently, it is a python package with a few cli tools such as `mlx_audio.server`. It's dependencies for server is not working properly (yet), and has no builtin support to run it as system service in background. This Homebrew Formula re-packages it for homebrew with missing deps, and the main focus is to add LaunchAgent service support for the `mlx_audio.server` on macOS, so you can use it as an 24x7 local OpenAI-compatible API server. ## Installation Tap and install: ```bash # install the formula with cli tools, not only server brew install --HEAD guoqiao/tap/mlx-audio-server # start the mlx-audio.server as a LaunchAgent service brew services start mlx-audio-server # verify curl -Ss http://localhost:8899/v1/models ``` ## Usage Here are examples to use `GLM-ASR-Nano-2512` model for STT with different ways: with curl: ``` bash stt_glmasr.sh path/to/audio ``` with openai python sdk: ``` uv run openai_api_transcribe.py path/to/audio ``` with Spokenly iOS/macOS app, as OpenAI Compatible API: ``` URL: http://<IP>:8899 Model: mlx-community/glm-asr-nano-2512-8bit API KEY: <blank> ``` NOTE: `/v1` is not needed in URL here. ## About This is a Homebrew tap for the mlx-audio Python package, providing fast and efficient audio processing on Apple Silicon using MLX framework. For more information, visit the [mlx-audio repository](https://github.com/Blaizzy/mlx-audio).
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