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

Text-to-Audio Generation using Instruction-Tuned LLM and Latent Diffusion Model