## Introduction We present Kimi-Audio, an open-source audio foundation model excelling in **audio understanding, generation, and conversation**. This repository hosts the model checkpoints for Kimi-Audio-7B. Kimi-Audio is designed as a universal audio foundation model capable of handling a wide variety of audio processing tasks within a single unified framework. Key features include: * **Universal Capabilities:** Handles diverse tasks like speech recognition (ASR), audio question answering (AQA), audio captioning (AAC), speech emotion recognition (SER), sound event/scene classification (SEC/ASC) and end-to-end speech conversation. * **State-of-the-Art Performance:** Achieves SOTA results on numerous audio benchmarks (see our [Technical Report](https://raw.githubusercontent.com/MoonshotAI/Kimi-Audio/master/assets/kimia_report.pdf)). * **Large-Scale Pre-training:** Pre-trained on over 13 million hours of diverse audio data (speech, music, sounds) and text data. * **Novel Architecture:** Employs a hybrid audio input (continuous acoustic + discrete semantic tokens) and an LLM core with parallel heads for text and audio token generation. * **Efficient Inference:** Features a chunk-wise streaming detokenizer based on flow matching for low-latency audio generation. For more details, please refer to our [GitHub Repository](https://github.com/MoonshotAI/Kimi-Audio) and [Technical Report](https://raw.githubusercontent.com/MoonshotAI/Kimi-Audio/master/assets/kimia_report.pdf). ## Note Kimi-Audio-7B is a base model without fine-tuning. So it cannot be used directly. The base model is quite flexible, you can fine-tune it on any possible downstream tasks. If you are looking for an out-of-the-box model, please refer to [Kimi-Audio-7B-Instruct](https://huggingface.co/moonshotai/Kimi-Audio-7B-Instruct). ## Citation If you find Kimi-Audio useful in your research or applications, please cite our technical report: ```bibtex @misc{kimi_audio_2024, title={Kimi-Audio Technical Report}, author={Kimi Team}, year={2024}, eprint={arXiv:placeholder}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## License The model is based and modified from [Qwen 2.5-7B](https://github.com/QwenLM/Qwen2.5). Code derived from Qwen2.5-7B is licensed under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0). Other parts of the code are licensed under the [MIT License](https://opensource.org/licenses/MIT).