VITS: Conditional Variational Autoencoder with Adversarial Learning for End to End Text to Speech VITS is an end to end speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a conditional variational autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior. This repository contains the weights for the official VITS checkpoint trained on the LJ Speech dataset. Model Details VITS ( V ariational I nference with adversarial learning for end to end T ext to S peech) is an end to end speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a conditional variational autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior. A set of spectrogram based acoustic features are predicted by the flow based module, which is formed of a Transformer based text encoder and multiple coupling layers. The spectrogram is decoded using a stack of transposed convolutional layers, much in the same style as the HiFi GAN vocoder. Motivated by the one to many nature of the TTS problem, where the same text input can be spoken in multiple ways, the model also includes a sto…
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