  In the domain of audio processing, Transfer Learning has facilitated the rise
of Self-Supervised Learning and Zero-Shot Learning techniques. These approaches
have led to the development of versatile models capable of tackling a wide
array of tasks, while delivering state-of-the-art performance. However, current
models inherently lack the capacity to produce the requisite language for
open-ended tasks, such as Audio Captioning or Audio Question & Answering. We
introduce Pengi, a novel Audio Language Model that leverages Transfer Learning
by framing all audio tasks as text-generation tasks. It takes as input, an
audio recording, and text, and generates free-form text as output. The input
audio is represented as a sequence of continuous embeddings by an audio
encoder. A text encoder does the same for the corresponding text input. Both
sequences are combined as a prefix to prompt a pre-trained frozen language
model. The unified architecture of Pengi enables open-ended tasks and
close-ended tasks without any additional fine-tuning or task-specific
extensions. When evaluated on 22 downstream tasks, our approach yields
state-of-the-art performance in several of them. Our results show that
connecting language models with audio models is a major step towards
general-purpose audio understanding
