VITS2: Improving Quality and Efficiency of Single-Stage Text-to-Speech
  with Adversarial Learning and Architecture Design
Jungil Kong Jihoon Park Beomjeong Kim Jeongmin Kim Dohee Kong Sangjin Kim
Single-stage text-to-speech models have been actively studied recently, and
their results have outperformed two-stage pipeline systems. Although the
previous single-stage model has made great progress, there is room for
improvement in terms of its intermittent unnaturalness, computational
efficiency, and strong dependence on phoneme conversion. In this work, we
introduce VITS2, a single-stage text-to-speech model that efficiently
synthesizes a more natural speech by improving several aspects of the previous
work. We propose improved structures and training mechanisms and present that
the proposed methods are effective in improving naturalness, similarity of
speech characteristics in a multi-speaker model, and efficiency of training and
inference. Furthermore, we demonstrate that the strong dependence on phoneme
conversion in previous works can be significantly reduced with our method,
which allows a fully end-to-end single-stage approach.