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---
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license: cc-by-nc-4.0
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---
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# AudioX
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## 🎧 AudioX: Diffusion Transformer for Anything-to-Audio Generation
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[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.
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### Links
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- **[Paper](https://arxiv.org/abs/2503.10522)**: Explore the research behind AudioX.
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- **[Project](https://zeyuet.github.io/AudioX/)**: Visit the official project page for more information and updates.
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## Clone the repository
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```bash
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GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/Zeyue7/AudioX
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cd AudioX
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conda create -n AudioX python=3.8.20
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conda activate AudioX
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pip install git+https://github.com/ZeyueT/AudioX.git
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conda install -c conda-forge ffmpeg libsndfile
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```
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## Usage
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```py
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import torch
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import torchaudio
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from einops import rearrange
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from stable_audio_tools import get_pretrained_model
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from stable_audio_tools.inference.generation import generate_diffusion_cond
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from stable_audio_tools.data.utils import read_video, merge_video_audio
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from stable_audio_tools.data.utils import load_and_process_audio
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import os
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device = "cuda" if torch.cuda.is_available() else "cpu"
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# Download model
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model, model_config = get_pretrained_model("Zeyue7/AudioX")
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sample_rate = model_config["sample_rate"]
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sample_size = model_config["sample_size"]
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target_fps = model_config["video_fps"]
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seconds_start = 0
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seconds_total = 10
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model = model.to(device)
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# for video-to-music generation
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video_path = "video.mp4"
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text_prompt = "Generate music for the video"
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audio_path = None
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video_tensor = read_video(video_path, seek_time=0, duration=seconds_total, target_fps=target_fps)
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audio_tensor = load_and_process_audio(audio_path, sample_rate, seconds_start, seconds_total)
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conditioning = [{
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"video_prompt": [video_tensor.unsqueeze(0)],
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"text_prompt": text_prompt,
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"audio_prompt": audio_tensor.unsqueeze(0),
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"seconds_start": seconds_start,
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"seconds_total": seconds_total
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}]
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# Generate stereo audio
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output = generate_diffusion_cond(
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model,
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steps=250,
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cfg_scale=7,
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conditioning=conditioning,
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sample_size=sample_size,
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sigma_min=0.3,
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sigma_max=500,
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sampler_type="dpmpp-3m-sde",
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device=device
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)
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# Rearrange audio batch to a single sequence
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output = rearrange(output, "b d n -> d (b n)")
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# Peak normalize, clip, convert to int16, and save to file
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output = output.to(torch.float32).div(torch.max(torch.abs(output))).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
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torchaudio.save("output.wav", output, sample_rate)
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if video_path is not None and os.path.exists(video_path):
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merge_video_audio(video_path, "output.wav", "output.mp4", 0, seconds_total)
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```
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## Citation
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If you find our work useful, please consider citing:
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```
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@article{tian2025audiox,
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title={AudioX: Diffusion Transformer for Anything-to-Audio Generation},
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author={Tian, Zeyue and Jin, Yizhu and Liu, Zhaoyang and Yuan, Ruibin and Tan, Xu and Chen, Qifeng and Xue, Wei and Guo, Yike},
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journal={arXiv preprint arXiv:2503.10522},
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year={2025}
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}
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``` |
