Temporal Sequence Modelling. We analyze how TANGO and AudioLDM models perform audio
generation when the text prompt contains multiple sequential events. Consider the following ex-
amples: A toy train running as a young boy talks followed by plastic clanking then a child laughing contains
three separate sequential events, whereas Rolling thunder with lightning strikes contains only one. We
segregate the AudioCaps test set using the presence of temporal identiﬁers – while, before, after,
then, followed – into two subsets, one with multiple events and the other with single event. We show
the objective evaluation results for audio generation on these subsets in Table 3. TANGO achieves the
best FD and FAD scores for both multiple events and single event instances. The best KL divergence
score is achieved by the AudioLDM-M-Full-FT model. We conjecture that the larger corpus from
the four training datasets in AudioLDM could be more helpful in improving the reference-based KL
Text-to-Audio Generation using Instruction-Tuned LLM and Latent Diffusion Model