We present RECAP (REtrieval-Augmented Audio CAPtioning), a novel and
effective audio captioning system that generates captions conditioned on an
input audio and other captions similar to the audio retrieved from a datastore.
Additionally, our proposed method can transfer to any domain without the need
for any additional fine-tuning. To generate a caption for an audio sample, we
leverage an audio-text model CLAP to retrieve captions similar to it from a
replaceable datastore, which are then used to construct a prompt. Next, we feed
this prompt to a GPT-2 decoder and introduce cross-attention layers between the
CLAP encoder and GPT-2 to condition the audio for caption generation.
Experiments on two benchmark datasets, Clotho and AudioCaps, show that RECAP
achieves competitive performance in in-domain settings and significant
improvements in out-of-domain settings. Additionally, due to its capability to
exploit a large text-captions-only datastore in a \textit{training-free}
fashion, RECAP shows unique capabilities of captioning novel audio events never
seen during training and compositional audios with multiple events. To promote
research in this space, we also release 150,000+ new weakly labeled captions
for AudioSet, AudioCaps, and Clotho.

RECAP 是一种新颖有效的音频字幕系统，通过从数据存储中检索与输入音频类似的其他字幕来生成字幕，并且无需额外的微调即可适用于任何领域。