Editing VITS (section)

=== VITS (2021) ===

VITS employs a conditional variational autoencoder (VAE) framework combined with several advanced techniques:

'''Core Components:'''
* '''Posterior Encoder:''' Processes linear-scale spectrograms during training to learn latent representations
* '''Prior Encoder:''' Contains a text encoder and normalizing flows to model the prior distribution of latent variables
* '''Decoder:''' Based on HiFi-GAN V1 generator, converts latent variables to raw waveforms
* '''Discriminator:''' Multi-period discriminator from HiFi-GAN for adversarial training

'''Key Innovations:'''
* '''Monotonic Alignment Search (MAS):''' Automatically learns alignments between text and speech without external annotations by finding alignments that maximize the likelihood of target speech.
* '''Stochastic Duration Predictor:''' Uses normalizing flows to model the distribution of phoneme durations, enabling synthesis of speech with diverse rhythms from the same text input.
* '''Adversarial Training:''' Improves waveform quality through generator-discriminator competition

The model addresses the one-to-many relationship in speech synthesis, where a single text input can be spoken in multiple ways with different pitches, rhythms, and prosodic patterns.