本文介绍了一种名为VITS2的改进方法，通过对抗学习和使用Transformer块进行speaker-conditioned文本编码，解决单阶段文本转语音的不连贯、低效等问题，提升了音频质量和处理多说话人特性的能力。
论文：VITS2: Improving Quality and Efficiency of Single-Stage Text-to-Speech with Adversarial Learning and Architecture Design
演示：https://vits-2.github.io/demo/
论文：https://arxiv.org/abs/2307.16430
目前仍然存在的问题:
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intermittent unnaturalness
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low efficiency of the duration predictor
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complex input format to alleviate the limitations of alignment and duration modeling (use of blank token)
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insufficient speaker similarity in the multi-speaker model
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slow training, and strong dependence on the phoneme conversion.
提出的方法：
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a stochastic duration predictor trained through adversarial learning
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normalizing flows improved by utilizing the transformer block
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a speaker-conditioned text encoder to model multiple speakers’ characteristics better.
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