Sample-Efficient Diffusion for Text-To-Speech Synthesis

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 thestate-of-the-art auto-regressive model, VALL-E, while using less than 2% the training data. Our implementation is available at https://github.com/justinlovelace/SESD.