Better speech synthesis through scaling
Authors: name: James Betker

In recent years, the field of image generation has been revolutionized by the
application of autoregressive transformers and DDPMs. These approaches model
the process of image generation as a step-wise probabilistic processes and
leverage large amounts of compute and data to learn the image distribution.
This methodology of improving performance need not be confined to images. This
paper describes a way to apply advances in the image generative domain to
speech synthesis. The result is TorToise -- an expressive, multi-voice
text-to-speech system.
  All model code and trained weights have been open-sourced at
https://github.com/neonbjb/tortoise-tts.