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---
license: mit
---
<div align="center">
<p align="center">
<h1>MeanAudio: Fast and Faithful Text-to-Audio Generation with Mean Flows</h1>
<!-- <a href=>Paper</a> | <a href="https://meanaudio.github.io/">Webpage</a> -->
[](https://arxiv.org/abs/2508.06098)
[](https://github.com/xiquan-li/MeanAudio?tab=readme-ov-file)
[](https://huggingface.co/AndreasXi/MeanAudio)
[](https://huggingface.co/spaces/chenxie95/MeanAudio)
[](https://meanaudio.github.io/)
</p>
</div>
## Overview
MeanAudio is a novel MeanFlow-based model tailored for fast and faithful text-to-audio generation. It can synthesize realistic sound in a single step, achieving a real-time factor (RTF) of 0.013 on a single NVIDIA 3090 GPU. Moreover, it also demonstrates strong performance in multi-step generation.
## Environmental Setup
**1. Create a new conda environment:**
```bash
conda create -n meanaudio python=3.11 -y
conda activate meanaudio
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 --upgrade
```
<!-- ```
conda install -c conda-forge 'ffmpeg<7
```
(Optional, if you use miniforge and don't already have the appropriate ffmpeg) -->
**2. Install with pip:**
```bash
git clone https://github.com/xiquan-li/MeanAudio.git
cd MeanAudio
pip install -e .
```
<!-- (If you encounter the File "setup.py" not found error, upgrade your pip with pip install --upgrade pip) -->
## Quick Start
<!-- **1. Download pre-trained models:** -->
To generate audio with our pre-trained model, simply run:
```bash
python demo.py --prompt 'your prompt' --num_steps 1
```
This will automatically download the pre-trained checkpoints from huggingface, and generate audio according to your prompt.
The output audio will be at `MeanAudio/output/`, and the checkpoints will be at `MeanAudio/weights/`.
Have fun with MeanAudio 😊 !!! |