  The immense scale of the recent large language models (LLM) allows many
interesting properties, such as, instruction- and chain-of-thought-based
fine-tuning, that has significantly improved zero- and few-shot performance in
many natural language processing (NLP) tasks. Inspired by such successes, we
adopt such an instruction-tuned LLM Flan-T5 as the text encoder for
text-to-audio (TTA) generation -- a task where the goal is to generate an audio
from its textual description. The prior works on TTA either pre-trained a joint
text-audio encoder or used a non-instruction-tuned model, such as, T5.
Consequently, our latent diffusion model (LDM)-based approach TANGO outperforms
the state-of-the-art AudioLDM on most metrics and stays comparable on the rest
on AudioCaps test set, despite training the LDM on a 63 times smaller dataset
and keeping the text encoder frozen. This improvement might also be attributed
to the adoption of audio pressure level-based sound mixing for training set
augmentation, whereas the prior methods take a random mix.
