  Compared with ample visual-text pre-training research, few works explore
audio-text pre-training, mostly due to the lack of sufficient parallel
audio-text data. Most existing methods incorporate the visual modality as a
pivot for audio-text pre-training, which inevitably induces data noise. In this
paper, we propose to utilize audio captioning to generate text directly from
audio, without the aid of the visual modality so that potential noise from
modality mismatch is eliminated. Furthermore, we propose caption generation
under the guidance of AudioSet tags, leading to more accurate captions. With
the above two improvements, we curate high-quality, large-scale parallel
audio-text data, based on which we perform audio-text pre-training. We
comprehensively demonstrate the performance of the pre-trained model on a
series of downstream audio-related tasks, including single-modality tasks like
audio classification and tagging, as well as cross-modal tasks consisting of
audio-text retrieval and audio-based text generation. Experimental results
indicate that our approach achieves state-of-the-art zero-shot classification
performance on most datasets, suggesting the effectiveness of our synthetic
data. The audio encoder also serves as an efficient pattern recognition model
by fine-tuning it on audio-related tasks. Synthetic data and pre-trained models
are available online.
