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JunityZhan / Understanding-VITS

In this repository, you will learn how code works in VITS(Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech) in Jupyter Notebooks, including normalizing data, training process, inference process, and model's details.
GNU General Public License v3.0
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Hi ,you can add these figures in your readme or anywhere you find appropriate #2

Closed p0p4k closed 1 year ago

p0p4k commented 1 year ago

image image

p0p4k commented 1 year ago

also this https://github.com/coqui-ai/TTS/discussions/2025 <-- for MAS explanation

JunityZhan commented 1 year ago

Thank you very much, I added them in the end of models.ipynb. MAS is indeed the most interesting part in this model.