Editing VITS (section)

=== VITS2 (2023) ===

VITS2 introduced several improvements over the original model to address issues including intermittent unnaturalness, computational efficiency, and strong dependence on phoneme conversion.

'''Major Improvements:'''
* '''Adversarial Duration Predictor:''' Replaced the flow-based stochastic duration predictor with one trained through adversarial learning, using a time step-wise conditional discriminator to improve efficiency and naturalness.
* '''Enhanced Normalizing Flows:''' Added transformer blocks to normalizing flows to capture long-term dependencies when transforming distributions, addressing limitations of convolution-only approaches.
* '''Improved Alignment Search:''' Modified Monotonic Alignment Search by adding Gaussian noise to calculated probabilities, giving the model additional opportunities to explore alternative alignments during early training.
* '''Speaker-Conditioned Text Encoder:''' For multi-speaker models, conditioned the speaker vector on the third transformer block of the text encoder to better capture speaker-specific pronunciation and intonation characteristics.