README.md

---
license: cc-by-nc-4.0
pipeline_tag: text-to-audio
library_name: stable-audio-tools
---

# AudioX

## 🎧 [ICLR 2026] AudioX: Diffusion Transformer for Anything-to-Audio Generation

**Accepted to ICLR 2026** 🎉

[TL;DR]: AudioX is a unified Diffusion Transformer model for Anything-to-Audio and Music Generation, capable of generating high-quality general audio and music, offering flexible natural language control, and seamlessly processing various modalities including text, video, image, music, and audio.

### Links
- **[Paper](https://arxiv.org/abs/2503.10522)**: Explore the research behind AudioX.
- **[Project](https://zeyuet.github.io/AudioX/)**: Visit the official project page for more information and updates.
- **[Code](https://github.com/ZeyueT/AudioX)**: Implementation of AudioX.


## Clone the repository
```bash
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/HKUSTAudio/AudioX
cd AudioX

conda create -n AudioX python=3.8.20
conda activate AudioX
pip install git+https://github.com/ZeyueT/AudioX.git
conda install -c conda-forge ffmpeg libsndfile
```

## Usage

```py
import torch
import torchaudio
from einops import rearrange
from stable_audio_tools import get_pretrained_model
from stable_audio_tools.inference.generation import generate_diffusion_cond
from stable_audio_tools.data.utils import read_video, merge_video_audio
from stable_audio_tools.data.utils import load_and_process_audio
import os

device = "cuda" if torch.cuda.is_available() else "cpu"

# Download model
model, model_config = get_pretrained_model("HKUSTAudio/AudioX")
sample_rate = model_config["sample_rate"]
sample_size = model_config["sample_size"]
target_fps = model_config["video_fps"]
seconds_start = 0
seconds_total = 10

model = model.to(device)

# for video-to-music generation
video_path = "video.mp4"
text_prompt = "Generate music for the video" 
audio_path = None

video_tensor = read_video(video_path, seek_time=0, duration=seconds_total, target_fps=target_fps)
audio_tensor = load_and_process_audio(audio_path, sample_rate, seconds_start, seconds_total)

conditioning = [{
    "video_prompt": [video_tensor.unsqueeze(0)],        
    "text_prompt": text_prompt,
    "audio_prompt": audio_tensor.unsqueeze(0),
    "seconds_start": seconds_start,
    "seconds_total": seconds_total
}]
    
# Generate stereo audio
output = generate_diffusion_cond(
    model,
    steps=250,
    cfg_scale=7,
    conditioning=conditioning,
    sample_size=sample_size,
    sigma_min=0.3,
    sigma_max=500,
    sampler_type="dpmpp-3m-sde",
    device=device
)

# Rearrange audio batch to a single sequence
output = rearrange(output, "b d n -> d (b n)")

# Peak normalize, clip, convert to int16, and save to file
output = output.to(torch.float32).div(torch.max(torch.abs(output))).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
torchaudio.save("output.wav", output, sample_rate)

if video_path is not None and os.path.exists(video_path):
    merge_video_audio(video_path, "output.wav", "output.mp4", 0, seconds_total)

```



## Citation
If you find our work useful, please consider citing:

```bibtex
@article{tian2025audiox,
  title={Audiox: Diffusion transformer for anything-to-audio generation},
  author={Tian, Zeyue and Jin, Yizhu and Liu, Zhaoyang and Yuan, Ruibin and Tan, Xu and Chen, Qifeng and Xue, Wei and Guo, Yike},
  journal={arXiv preprint arXiv:2503.10522},
  year={2025}
}

@inproceedings{tian2025vidmuse,
  title={Vidmuse: A simple video-to-music generation framework with long-short-term modeling},
  author={Tian, Zeyue and Liu, Zhaoyang and Yuan, Ruibin and Pan, Jiahao and Liu, Qifeng and Tan, Xu and Chen, Qifeng and Xue, Wei and Guo, Yike},
  booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
  pages={18782--18793},
  year={2025}
}
```


## License

Please follow [CC-BY-NC](./LICENSE).