Diffusion based Text-to-Music Generation with Global and Local Text based Conditioning

TL;DR

This paper introduces a diffusion-based text-to-music generation model that combines local and global text representations from T5 and CLAP, improving adherence and quality while reducing model complexity.

Contribution

It proposes a novel conditioning approach using both T5 and CLAP embeddings with pooling mechanisms, reducing parameters and enhancing performance in text-to-music generation.

Findings

Incorporating CLAP embeddings improves text adherence.

Mean pooling from T5 yields better music quality.

The method reduces model parameters while maintaining performance.

Abstract

Diffusion based Text-To-Music (TTM) models generate music corresponding to text descriptions. Typically UNet based diffusion models condition on text embeddings generated from a pre-trained large language model or from a cross-modality audio-language representation model. This work proposes a diffusion based TTM, in which the UNet is conditioned on both (i) a uni-modal language model (e.g., T5) via cross-attention and (ii) a cross-modal audio-language representation model (e.g., CLAP) via Feature-wise Linear Modulation (FiLM). The diffusion model is trained to exploit both a local text representation from the T5 and a global representation from the CLAP. Furthermore, we propose modifications that extract both global and local representations from the T5 through pooling mechanisms that we call mean pooling and self-attention pooling. This approach mitigates the need for an additional encoder (e.g., CLAP) to extract a global representation, thereby reducing the number of model parameters. Our results show that incorporating the CLAP global embeddings to the T5 local embeddings enhances text adherence (KL=1.47) compared to a baseline model solely relying on the T5 local embeddings (KL=1.54). Alternatively, extracting global text embeddings directly from the T5 local embeddings through the proposed mean pooling approach yields superior generation quality (FAD=1.89) while exhibiting marginally inferior text adherence (KL=1.51) against the model conditioned on both CLAP and T5 text embeddings (FAD=1.94 and KL=1.47). Our proposed solution is not only efficient but also compact in terms of the number of parameters required.