Kimi-Audio-7B

Kimi-Audio 🤗 Kimi-Audio-7B | 🤗 Kimi-Audio-7B-Instruct | 📑 Paper 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). **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 and Technical Report. 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. Citation If you find Kimi-Audio useful in your research or applications, please cite our technical report: License The model is based and modified from Qwen 2.5-7B. Code derived from Qwen2.5-7B is licensed under the Apache 2.0 License. Other parts of the code are licensed under the MIT License.