“TANGO: Text-To-Audio Generation Using Instruction-Tuned LLM and Latent Diffusion Model”, Deepanway Ghosal, Navonil Majumder, Ambuj Mehrish, Soujanya Poria2023-04-24 (AI music, GAN, instruct-tuning LLMs, T5 Transformer, autoencoder NN)⁠:

The immense scale of the recent large language models (LLM) allows many interesting properties, such as, instruction-tuning and chain-of-thought-based fine-tuning, that has improved zero & 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× 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.