VITS (Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech ) is an End-to-End (encoder -> vocoder together) TTS model that takes advantage of SOTA DL techniques like GANs, VAE, Normalizing Flows. It does not require external alignment annotations and learns the text-to-audio alignment using MAS, as explained in the paper. The model architecture is a combination of GlowTTS encoder and HiFiGAN vocoder. It is a feed-forward model with x67.12 real-time factor on a GPU.

YourTTS is a multi-speaker and multi-lingual TTS model that can perform voice conversion and zero-shot speaker adaptation. It can also learn a new language or voice with a ~ 1 minute long audio clip. This is a big open gate for training TTS models in low-resources languages. YourTTS uses VITS as the backbone architecture coupled with a speaker encoder model.