Funaudiollm: Voice Understanding And Generation Foundation Models For Natural Interaction Between Humans And Llms

An Keyu, Chen Qian, Deng Chong, Du Zhihao, Gao Changfeng, Gao Zhifu, Gu Yue, He Ting, Hu Hangrui, Hu Kai, Ji Shengpeng, Li Yabin, Li Zerui, Lu Heng, Luo Haoneng, Lv Xiang, Ma Bin, Ma Ziyang, Ni Chongjia, Song Changhe, Shi Jiaqi, Shi Xian, Wang Hao, Wang Wen, Wang Yuxuan, Xiao Zhangyu, Yan Zhijie, Yang Yexin, Zhang Bin, Zhang Qinglin, Zhang Shiliang, Zhao Nan, Zheng Siqi. Arxiv 2024

[Paper] [Code] [Code]    
Applications Fine Tuning Has Code Pretraining Methods Reinforcement Learning Training Techniques

This report introduces FunAudioLLM, a model family designed to enhance natural voice interactions between humans and large language models (LLMs). At its core are two innovative models: SenseVoice, which handles multilingual speech recognition, emotion recognition, and audio event detection; and CosyVoice, which facilitates natural speech generation with control over multiple languages, timbre, speaking style, and speaker identity. SenseVoice-Small delivers exceptionally low-latency ASR for 5 languages, and SenseVoice-Large supports high-precision ASR for over 50 languages, while CosyVoice excels in multi-lingual voice generation, zero-shot in-context learning, cross-lingual voice cloning, and instruction-following capabilities. The models related to SenseVoice and CosyVoice have been open-sourced on Modelscope and Huggingface, along with the corresponding training, inference, and fine-tuning codes released on GitHub. By integrating these models with LLMs, FunAudioLLM enables applications such as speech-to-speech translation, emotional voice chat, interactive podcasts, and expressive audiobook narration, thereby pushing the boundaries of voice interaction technology. Demos are available at https://fun-audio-llm.github.io, and the code can be accessed at https://github.com/FunAudioLLM.

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