Despite recent progress in text-to-audio (TTA) generation, we show that the
state-of-the-art models, such as AudioLDM, trained on datasets with an
imbalanced class distribution, such as AudioCaps, are biased in their
generation performance. Specifically, they excel in generating common audio
classes while underperforming in the rare ones, thus degrading the overall
generation performance. We refer to this problem as long-tailed text-to-audio
generation. To address this issue, we propose a simple retrieval-augmented
approach for TTA models. Specifically, given an input text prompt, we first
leverage a Contrastive Language Audio Pretraining (CLAP) model to retrieve
relevant text-audio pairs. The features of the retrieved audio-text data are
then used as additional conditions to guide the learning of TTA models. We
enhance AudioLDM with our proposed approach and denote the resulting augmented
system as Re-AudioLDM. On the AudioCaps dataset, Re-AudioLDM achieves a
state-of-the-art Frechet Audio Distance (FAD) of 1.37, outperforming the
existing approaches by a large margin. Furthermore, we show that Re-AudioLDM
can generate realistic audio for complex scenes, rare audio classes, and even
unseen audio types, indicating its potential in TTA tasks.

我们提出了一种用于文本到音频生成的简单的检索增强方法，通过使用检索到的音频文本数据对 TTA 模型的学习进行指导，从而改善了 AudioLDM 模型在长尾数据集上的性能，在 AudioCaps 数据集上，我们的改进模型 Re-AudioLDM 以巨大的优势超越了现有方法，能够生成逼真的音频并具备在复杂场景、罕见音频类别甚至未见过音频类型的潜力。