SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities

• URL: http://arxiv.org/abs/2305.11000v2
• Date: Fri, 19 May 2023 14:41:16 GMT
• Title: SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities
• Authors: Dong Zhang, Shimin Li, Xin Zhang, Jun Zhan, Pengyu Wang, Yaqian Zhou, Xipeng Qiu
• Abstract summary: SpeechGPT is a large language model with intrinsic cross-modal conversational abilities. We employ a three-stage training strategy that includes modality-adaptation pre-training, cross-modal instruction fine-tuning, and chain-of-modality instruction fine-tuning.
• Score: 39.07096632751864
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 ChatGPT. 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-model content. With discrete speech representations, we first construct SpeechInstruct, a large-scale cross-modal speech instruction dataset. Additionally, we employ a three-stage training strategy that includes modality-adaptation pre-training, cross-modal instruction fine-tuning, and chain-of-modality instruction fine-tuning. The experimental results demonstrate that SpeechGPT has an impressive capacity to follow multi-modal human instructions and highlight the potential of handling multiple modalities with one model. Demos are shown in https://0nutation.github.io/SpeechGPT.github.io/.

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