SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities
Dong Zhang, Shimin Li, Xin Zhang, Jun Zhan, Pengyu Wang, Yaqian Zhou, Xipeng Qiu · 2023
Multi-modal large language models are regarded as a crucial step towards Artificial General Intelligence (AGI) and have garnered significant interest with the emergence of Chat-GPT.However, current speech-language models typically adopt the cascade paradigm, preventing inter-modal knowledge transfer.In this paper, we propose SpeechGPT, a large language model with intrinsic cross-modal conversational abilities, capable of perceiving and generating multi-modal content.With discrete speech representations, we construct SpeechInstruct, the first large-scale crossmodal speech instruction dataset.Additionally, we employ a three-stage training strategy that includes modality-adaptation pretraining, cross-modal instruction fine-tuning, and chain-of-modality instruction fine-tuning.The experimental results demonstrate that SpeechGPT has an impressive capacity to follow cross-modal human instructions and highlight the potential of handling multiple modalities with one model.