Home Projects MMAudio
MMAudio
Python

MMAudio

[CVPR 2025] MMAudio: Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis

by hkchengrex · GitHub
Stars
Forks
License
Created
Last commit
Category
Language
audioaudio-synthesiscomputer-visionMITPython
View on GitHub
In plain words

Generate audio that matches video or text inputs, creating synchronized soundtracks for your projects.

You maintain this project?

Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.

Claim this page →
MMAudio — GitHub preview card
📈 Star history
2.24k2.23k
2026-07-072026-08-31
📈 Track MMAudio

Get an email alert on its next release or when it starts trending — never miss the moment.

Free · no card · unsubscribe anytime
Get email alerts →
📄 About

[CVPR 2025] MMAudio: Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis

MMAudio has 2.2k stars on GitHub. It has been forked 261 times. MMAudio is written mainly in Python. It has been in active development since 2024. MMAudio is available under the MIT license. Its main topics are audio, audio-synthesis, computer-vision, deep-learning.

Frequently asked questions

What is MMAudio?

[CVPR 2025] MMAudio: Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis

Is MMAudio open source?

MMAudio is an open-source project. It is released under the MIT license.

Is MMAudio free?

Yes. MMAudio is free and open source — you can use, modify and self-host it.

What license does MMAudio use?

MMAudio is available under the MIT license.

What language is MMAudio written in?

MMAudio is written mainly in Python.

🏅 Maintainer of this project?
olud.ai badge — MMAudio

Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.

[![olud.ai](https://olud.ai/badge.php?tool=hkchengrex-mmaudio)](https://olud.ai/project/hkchengrex-mmaudio.html)
More badge options →
🧬 Shares DNA with🧬 View the DNA map →

Measured from GitHub topics shared by both projects, weighted by how rare each topic is.