Kimi-Audio Technical Report
KimiTeam, Ding Ding, Zeqian Ju, Yichong Leng, Songxiang Liu, Tong Liu, Zeyu Shang, Kai Shen, Wei Song, Xu Tan, Heyi Tang, Zhengtao Wang, Chu Wei, Yifei Xin, Xinran Xu, Jianwei Yu, Yutao Zhang, Xinyu Zhou, Y. Charles, Jun Chen, Yanru Chen, Yulun Du
2025-04-28
Summary
This paper talks about Kimi-Audio, a new open-source AI model that is really good at understanding and working with sounds, like speech, music, and other audio recordings.
What's the problem?
The problem is that most existing AI models for audio tasks either aren't as accurate as they could be or aren't available for everyone to use and build on, which limits progress and creativity in the field.
What's the solution?
The researchers designed Kimi-Audio using a special architecture based on large language models, but adapted for audio. They trained and tested it very thoroughly on a wide range of audio tasks, and made it open-source so anyone can use or improve it.
Why it matters?
This matters because it gives researchers, developers, and companies a powerful tool to create better apps and services involving sound, such as voice assistants, music analysis, and accessibility features, and it speeds up innovation by being open for everyone.
Abstract
Kimi-Audio, an open-source audio foundation model, achieves state-of-the-art performance across audio-related tasks through a novel LLM-based architecture and comprehensive training and evaluation processes.