In this work, we extend the instruction-tuned Llama-2 model with end-to-end general-purpose speech processing and reasoning abilities while maintaining the wide range of original LLM capabilities, without using any carefully curated paired data. The resulting end-to-end model, named AudioChatLlama, can utilize audio prompts as a replacement for text and sustain a conversation. Such a model also has extended cross-modal capabilities such as being able to perform spoken question answering (QA), speech translation, and audio summarization amongst many other closed and open-domain tasks. This is unlike prior approaches in speech, in which LLMs are extended to handle audio for a limited number of pre-designated tasks. On both synthesized and recorded speech QA test sets, evaluations show that our end-to-end approach is on par with or outperforms cascaded systems (speech recognizer + LLM) in terms of modeling the response to a prompt. Furthermore, unlike cascades, our approach can interchange text and audio modalities and intrinsically utilize prior context in a conversation to provide better results.
@article{arxiv.2311.06753,
title = {AudioChatLlama: Towards General-Purpose Speech Abilities for LLMs},
author = {Yassir Fathullah and Chunyang Wu and Egor Lakomkin and Ke Li and Junteng Jia and Yuan Shangguan and Jay Mahadeokar and Ozlem Kalinli and Christian Fuegen and Mike Seltzer},
journal= {arXiv preprint arXiv:2311.06753},
year = {2024}
}