BLAT: Bootstrapping Language-Audio Pre-training based on AudioSet Tag-guided Synthetic Data

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 BLAT: Bootstrapping Language-Audio pre-training based on Tag-guided synthetic data. We 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. Evaluation on a series of downstream tasks indicates that BLAT achieves SOTA zero-shot classification performance on most datasets and significant performance improvement when fine-tuned on downstream tasks, suggesting the effectiveness of our synthetic data.