This work introduces Sample-Efficient Speech Diffusion (SESD), an algorithm for effective speech synthesis in modest data regimes through latent diffusion. It is based on a novel diffusion architecture, that we call U-Audio Transformer (U-AT), that efficiently scales to long sequences and operates in the latent space of a pre-trained audio autoencoder. Conditioned on character-aware language model representations, SESD achieves impressive results despite training on less than 1k hours of speech - far less than current state-of-the-art systems. In fact, it synthesizes more intelligible speech than the state-of-the-art auto-regressive model, VALL-E, while using less than 2% the training data.
@article{arxiv.2409.03717,
title = {Sample-Efficient Diffusion for Text-To-Speech Synthesis},
author = {Justin Lovelace and Soham Ray and Kwangyoun Kim and Kilian Q. Weinberger and Felix Wu},
journal= {arXiv preprint arXiv:2409.03717},
year = {2024}
}