HKUSTAudio/AudioX-MAF-MMDiT

AudioX: A Unified Framework for Anything-to-Audio Generation

AudioX is a unified framework for generating audio and music from diverse multimodal control signals, including text, video, and audio. It features a Multimodal Adaptive Fusion (MAF) module to effectively align and fuse these inputs.

• Paper: AudioX: A Unified Framework for Anything-to-Audio Generation
• Project Page: https://zeyuet.github.io/AudioX/
• Repository: https://github.com/ZeyueT/AudioX
• Demo: Hugging Face Spaces

Installation

To use AudioX, first install the required dependencies and the package from the official repository:

# Clone the repository
git clone https://github.com/ZeyueT/AudioX.git
cd AudioX

# Install dependencies
pip install git+https://github.com/ZeyueT/AudioX.git
conda install -c conda-forge ffmpeg libsndfile

Sample Usage

Below is an example of how to perform Video-to-Music generation programmatically:

import torch
import torchaudio
from einops import rearrange
from audiox import get_pretrained_model
from audiox.inference.generation import generate_diffusion_cond
from audiox.data.utils import read_video, merge_video_audio, load_and_process_audio, encode_video_with_synchformer
import os

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

# Load pretrained model
# Choose one: "HKUSTAudio/AudioX", "HKUSTAudio/AudioX-MAF", or "HKUSTAudio/AudioX-MAF-MMDiT"
model_name = "HKUSTAudio/AudioX"
model, model_config = get_pretrained_model(model_name)
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)

# Example: Video-to-Music generation
video_path = "example/V2M_sample-1.mp4"
text_prompt = "Generate music for the video" 
audio_path = None

# Prepare inputs
video_tensor = read_video(video_path, seek_time=seconds_start, duration=seconds_total, target_fps=target_fps)
if audio_path:
    audio_tensor = load_and_process_audio(audio_path, sample_rate, seconds_start, seconds_total)
else:
    # Use zero tensor when no audio is provided
    audio_tensor = torch.zeros((2, int(sample_rate * seconds_total)))

# For AudioX-MAF and AudioX-MAF-MMDiT: encode video with synchformer
video_sync_frames = None
if "MAF" in model_name:
    video_sync_frames = encode_video_with_synchformer(
        video_path, model_name, seconds_start, seconds_total, device
    )

# Create conditioning
conditioning = [{
    "video_prompt": {"video_tensors": video_tensor.unsqueeze(0), "video_sync_frames": video_sync_frames},        
    "text_prompt": text_prompt,
    "audio_prompt": audio_tensor.unsqueeze(0),
    "seconds_start": seconds_start,
    "seconds_total": seconds_total
}]
    
# Generate 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
)

# Post-process and save audio
output = rearrange(output, "b d n -> d (b n)")
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)

Citation

If you find AudioX useful in your research, please consider citing the following:

@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}
}