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.

Table of Contents

• Features
• Installation
• Quick Start
• Supported Models
• Model Examples
• Web Interface \& API Server
• Quantization
• Swift
• Requirements
• License
• Citation
• Acknowledgements

Features

• Fast inference optimized for Apple Silicon (M series chips)
• Multiple model architectures for TTS, STT, and STS
• Multilingual support across models
• Voice customization and cloning capabilities
• Adjustable speech speed control
• Interactive web interface with 3D audio visualization
• OpenAI-compatible REST API
• Quantization support (3-bit, 4-bit, 6-bit, 8-bit, and more) for optimized performance
• Swift package for iOS/macOS integration

Installation

Using pip

pip install mlx-audio

Using uv to install only the command line tools

Latest release from pypi:

uv tool install --force mlx-audio --prerelease=allow

Latest code from github:

uv tool install --force git+https://github.com/Blaizzy/mlx-audio.git --prerelease=allow

For development or web interface:

git clone https://github.com/Blaizzy/mlx-audio.git
cd mlx-audio
pip install -e ".[dev, server]"

Quick Start

Command Line

# Basic TTS generation
mlx_audio.tts.generate --model mlx-community/Qwen3-TTS-12Hz-1.7B-Base-8bit --text 'Hello, world!' --voice Chelsie

# With a different voice and language hint
mlx_audio.tts.generate --model mlx-community/Qwen3-TTS-12Hz-1.7B-Base-8bit --text 'Welcome to MLX-Audio!' --voice Ethan --lang_code English

# Play audio immediately
mlx_audio.tts.generate --model mlx-community/Qwen3-TTS-12Hz-1.7B-Base-8bit --text 'Hello!' --voice Chelsie --play

# Save to a specific directory
mlx_audio.tts.generate --model mlx-community/Qwen3-TTS-12Hz-1.7B-Base-8bit --text 'Hello!' --voice Chelsie --output_path ./my_audio

# Stream audio during generation
mlx_audio.tts.generate --model mlx-community/Qwen3-TTS-12Hz-1.7B-Base-8bit --text 'Hello!' --voice Chelsie --stream

# Stream audio during generation and save it to disk
mlx_audio.tts.generate --model mlx-community/Qwen3-TTS-12Hz-1.7B-Base-8bit --text 'Hello!' --voice Chelsie --stream --save

# Join multiple generated segments into one file
mlx_audio.tts.generate --model mlx-community/Qwen3-TTS-12Hz-1.7B-Base-8bit --text $'Hello!\nHow are you?' --voice Chelsie --join_audio

By default, when generation yields multiple segments, mlx-audio saves numbered files such as audio_000.wav and audio_001.wav. Use --join_audio to save one combined file instead. When using --stream, add --save to write the streamed audio to disk.

Python API

from mlx_audio.tts.utils import load_model

# Load model
model = load_model("mlx-community/Qwen3-TTS-12Hz-1.7B-Base-8bit")

# Generate speech
for result in model.generate(
    "Hello from MLX-Audio!",
    voice="Chelsie",
    lang_code="English",
):
    print(f"Generated {result.audio.shape[0]} samples")
    # result.audio contains the waveform as mx.array

Supported Models

Text-to-Speech (TTS)

Speech-to-Text (STT)

Voice Activity Detection / Speaker Diarization (VAD)

See the model READMEs for API details, streaming examples, and conversion steps.

Speech-to-Speech (STS)

Model Examples

Qwen3-TTS

Alibaba's state-of-the-art multilingual TTS with voice cloning, emotion control, and voice design capabilities.

from mlx_audio.tts.utils import load_model

model = load_model("mlx-community/Qwen3-TTS-12Hz-0.6B-Base-bf16")
results = list(model.generate(
    text="Hello, welcome to MLX-Audio!",
    voice="Chelsie",
    language="English",
))

audio = results[0].audio  # mx.array

See the Qwen3-TTS README for voice cloning, CustomVoice, VoiceDesign, and all available models.

OmniVoice

OmniVoice is a zero-shot multilingual TTS model for 646+ languages with voice cloning, batch generation, pronunciation controls, and nonverbal tags such as [laughter] and [sigh]. It uses a bidirectional Qwen3 backbone with iterative masked generation and a Hig