Fine-tuning is a popular method for adapting text-to-speech (TTS) models to new speakers. However this approach has some challenges. Usually fine-tuning requires several hours of high quality speech per speaker. There is also that fine-tuning will negatively affect the quality of speech synthesis for previously learnt speakers. In this paper we propose an alternative approach for TTS adaptation based on using parameter-efficient adapter modules. In the proposed approach, a few small adapter modules are added to the original network. The original weights are frozen, and only the adapters are fine-tuned on speech for new speaker. The parameter-efficient fine-tuning approach will produce a new model with high level of parameter sharing with original model. Our experiments on LibriTTS, HiFi-TTS and VCTK datasets validate the effectiveness of adapter-based method through objective and subjective metrics.
翻译:微调是调整文本到语音模式以适应新发言者的一种流行方法,但这一方法有一些挑战。微调通常要求每个发言者讲几小时高质量的话。还有,微调将对以前学过发言者的语音合成质量产生消极影响。在本文中,我们提出了基于使用节能调适器模块的TTS适应替代方法。在拟议方法中,在原始网络中添加了几个小的调适器模块。原有的重量被冻结,只有调适器对新发言者的演讲进行微调。节能微调法将产生一个新的模型,与原模型分享高水平参数。我们在LibriTTS、HIFi-TTS和VCTK数据集方面的实验通过客观和主观的衡量标准验证了基于适应器方法的有效性。