#Variational Inference

Discover an innovative end-to-end TTS method that improves upon traditional two-stage systems using variational inference and adversarial learning. This approach enhances generative capabilities, resulting in natural-sounding speech. A stochastic duration predictor supports varied speech rhythms and tones from text. Human evaluations on the LJ Speech dataset demonstrate its superior performance, achieving MOS scores close to real human speech. Access the interactive demo for audio examples or explore available pretrained models.

This project offers an end-to-end method for converting singing voices through variational inference and adversarial learning, leveraging the VITS model. Designed for deep learning beginners, it focuses on hands-on practice with essential Python and PyTorch knowledge. It supports training with multiple speakers, creating distinctive voices by mixing, and handling light accompaniment. While needing at least 6GB VRAM for training, it delivers strong performance with features like noise immunity and advanced sound quality enhancement. Real-time voice conversion is not supported, but the project provides comprehensive instruction for training and inference, aiding learners in optimizing model operations.