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YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone
论文
论文
发布时间2021-12-04
发表arXiv:2112.02418
作者:Edresson Casanova,Arnaldo Candido Junior,Christopher Shulby,Moacir Antonelli Ponti,Julian Weber,Eren Gölge
详细介绍
YourTTS brings the power of a multilingual approach to the task of zero-shot multi-speaker TTS. Our method builds upon the VITS model and adds several novel modifications for zero-shot multi-speaker and multilingual training. We achieved state-of-the-art (SOTA) results in zero-shot multi-speaker TTS and results comparable to SOTA in zero-shot voice conversion on the VCTK dataset. Additionally, our approach achieves promising results in a target language with a single-speaker dataset, opening possibilities for zero-shot multi-speaker TTS and zero-shot voice conversion systems in low-resource languages. Finally, it is possible to fine-tune the YourTTS model with less than 1 minute of speech and achieve state-of-the-art results in voice similarity and with reasonable quality. This is important to allow synthesis for speakers with a very different voice or recording characteristics from those seen during training.
代码仓库 (3)
edresson/yourtts
coqui-ai/TTSPyTorch
daniilrobnikov/vits2PyTorch
