Kimi Audio 7B

🚀 Kimi-Audio

Kimi-Audio is an open - source audio foundation model that excels in audio understanding, generation, and conversation. It provides a unified framework to handle a wide range of audio processing tasks.

🚀 Quick Start

For more details about the model, please refer to our GitHub Repository and Technical Report.

🤗 Kimi-Audio-7B | 🤗 Kimi-Audio-7B-Instruct | 📑 Paper

✨ Features

• Universal Capabilities: Handles diverse tasks such as 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.

💡 Usage Tip

Kimi-Audio-7B is a base model without fine - tuning, so it cannot be used directly. The base model is quite flexible, and 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:

@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. 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.