SALMONN: Towards Generic Hearing Abilities for Large Language Models
Authors: name: Changli Tang, name: Wenyi Yu, name: Guangzhi Sun, name: Xianzhao Chen, name: Tian Tan, name: Wei Li, name: Lu Lu, name: Zejun Ma, name: Chao Zhang

Hearing is arguably an essential ability of artificial intelligence (AI)
agents in the physical world, which refers to the perception and understanding
of general auditory information consisting of at least three types of sounds:
speech, audio events, and music. In this paper, we propose SALMONN, a speech
audio language music open neural network, built by integrating a pre-trained
text-based large language model (LLM) with speech and audio encoders into a
single multimodal model. SALMONN enables the LLM to directly process and
understand general audio inputs and achieve competitive performances on a
number of speech and audio tasks used in training, such as automatic speech
recognition and translation, auditory-information-based question answering,
emotion recognition, speaker verification, and music and audio captioning
etc. SALMONN also has a diverse set of emergent abilities unseen in
the training, which includes but is not limited to speech translation to
untrained languages, speech-based slot filling, spoken-query-based question
answering, audio-based storytelling, and speech audio co-reasoning
etc. The presence of the cross-modal emergent abilities is studied,
and a novel few-shot activation tuning approach is proposed to activate such
abilities of SALMONN. To our knowledge, SALMONN is the first model of its type
and can be regarded as a step towards AI with generic hearing abilities. An
interactive demo of SALMONN is available at
\url{https://github.com/bytedance/SALMONN}, and the training code and
model checkpoints will be released upon acceptance.