📝 SummarXiv

Abstract

Controllable text-to-audio generation (TTA) has attracted much attention recently. Although existing works can achieve fine-grained controllability based on timestamp information, sound event categories are limited to a fixed set. Moreover, since only simulated data is used for training, the generated audio quality and generalization performance on real data are limited. To tackle this issue, we propose PicoAudio2, improving temporal-controllable TTA via a new data processing pipeline and model architecture. Specifically, we use a grounding model to annotate event timestamps of real audio-text datasets to curate temporally-strong real data, in addition to simulation data from existing works. The model is trained on the combination of real and simulation data. Moreover, following PicoAudio, we encode timestamp information into a timestamp matrix to provide extra fine-grained time-aligned information to the model, on top of the coarse-grained textual description. Experiments show that PicoAudio2 exhibits superior performance in terms of temporal controllability and audio quality.