---
license: mit
---

<div align="center">
<p align="center">
  <h2>Resonate: Reinforcing Text-to-Audio Generation with Online Feedbacks from Large Audio Language Models</h2>
  <!-- <a href=>Paper</a> | <a href="https://meanaudio.github.io/">Webpage</a>  -->
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## Overview 
Reosnate is a SOTA text-to-audio generator reinforced with online GRPO algorithm. 
It leverages the sophisticated reasoning capabilities of modern Large Audio Language Models as reward models. 
This repo provides a comprehensive pipeline for audio generation, covering Pre-training, SFT, DPO, and GRPO. 

## Environmental Setup

1. Create a new conda environment:

```bash
conda create -n resonate python=3.11 -y
conda activate resonate
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/Resonate.git

cd Resonate
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'
```
This will automatically download the pre-trained checkpoints from huggingface, and generate audio according to your prompt. 
By default, this will use [Resonate-GRPO](https://huggingface.co/AndreasXi/Resonate/blob/main/Resonate_GRPO.pth). 
The output audio will be at `Resonate/output/`, and the checkpoints will be at `Resonate/weights/`.