MMAudio Model

Introduction

The MMAudio repository hosts a model designed for high-quality video-to-audio synthesis, as detailed in the paper "Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis" (arXiv: 2412.15322). This model is developed by hkchengrex and can be accessed through Hugging Face.

Architecture

The model employs a multimodal joint training architecture to convert video inputs into audio outputs. This approach focuses on maintaining high-quality output through sophisticated training techniques.

Training

The training of the MMAudio model involves multimodal joint training methodologies, which allow the model to accurately translate video content into corresponding audio signals. Detailed training processes and parameters can be found in the associated GitHub repository.

Guide: Running Locally

1. Clone the Repository
   Begin by cloning the MMAudio GitHub repository:

   git clone https://github.com/hkchengrex/MMAudio.git
   cd MMAudio
   

2. Install Dependencies
   Install the necessary Python packages:

   pip install -r requirements.txt
   

3. Run the Model
   Execute the model with your video input:

   python run_model.py --input your_video.mp4
   

4. Cloud GPUs
   For optimal performance, especially with large video files, it is recommended to use cloud GPUs such as those offered by AWS, Google Cloud, or Azure.

License

The MMAudio model is licensed under the MIT License, allowing for open use with minimal restrictions.