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Single-stage text-to-speech models have been actively studied recently, andtheir results have outperformed two-stage pipeline systems. Although theprevious single-stage model has made great progress, there is room forimprovement in terms of its intermittent unnaturalness, computationalefficiency, and strong dependence on phoneme conversion. In this work, weintroduce VITS2, a single-stage text-to-speech model that efficientlysynthesizes a more natural speech by improving several aspects of the previouswork. We propose improved structures and training mechanisms and present thatthe proposed methods are effective in improving naturalness, similarity ofspeech characteristics in a multi-speaker model, and efficiency of training andinference. Furthermore, we demonstrate that the strong dependence on phonemeconversion in previous works can be significantly reduced with our method,which allows a fully end-to-end single-stage approach.

VITS2: Improving Quality and Efficiency of Single-Stage Text-to-Speech with Adversarial Learning and Architecture Design