diff --git a/docs/source/models/vits.md b/docs/source/models/vits.md
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--- a/docs/source/models/vits.md
+++ b/docs/source/models/vits.md
@@ -3,10 +3,15 @@
 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.
+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.
+
 ## Important resources & papers
+- 🐸 YourTTS: https://arxiv.org/abs/2112.02418
 - VITS: https://arxiv.org/pdf/2106.06103.pdf
 - Neural Spline Flows: https://arxiv.org/abs/1906.04032
 - Variational Autoencoder: https://arxiv.org/pdf/1312.6114.pdf