In our recent paper we propose the YourTTS model. 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.
Visit our website for audio samples.
All of our experiments were implemented at Coqui TTS.
All the released checkpoints are licensed under CC BY-NC-ND 4.0
| Model | URL |
|---|---|
| Speaker Encoder | link |
| Exp 1. YourTTS-EN(VCTK) | link |
| Exp 1. YourTTS-EN(VCTK) + SCL | link |
| Exp 2. YourTTS-EN(VCTK)-PT | link |
| Exp 2. YourTTS-EN(VCTK)-PT + SCL | link |
| Exp 3. YourTTS-EN(VCTK)-PT-FR | link |
| Exp 3. YourTTS-EN(VCTK)-PT-FR SCL | link |
| Exp 4. YourTTS-EN(VCTK+LibriTTS)-PT-FR SCL | link |
To replicability we make the audios used to calculate the MOS available here. In addition, we provide the Mean Opinion scores for each audio here.
To recompute our MOS results follow the instructions here. To predict the test sequences and compute the SECS results, please use the Jupyter Notebooks available here.