mirror of https://github.com/coqui-ai/TTS.git
1.1 KiB
1.1 KiB
VITS
VITS (Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech ) is an End-to-End (encoder -> vocoder together) TTS model that takes advantage of SOTA DL techniques like GANs, VAE, Normalizing Flows. It does not require external alignment annotations and learns the text-to-audio alignment using MAS as explained in the paper. The model architecture is a combination of GlowTTS encoder and HiFiGAN vocoder. It is a feed-forward model with x67.12 real-time factor on a GPU.
Important resources & papers
- VITS: https://arxiv.org/pdf/2106.06103.pdf
- Neural Spline Flows: https://arxiv.org/abs/1906.04032
- Variational Autoencoder: https://arxiv.org/pdf/1312.6114.pdf
- Generative Adversarial Networks: https://arxiv.org/abs/1406.2661
- HiFiGAN: https://arxiv.org/abs/2010.05646
- Normalizing Flows: https://blog.evjang.com/2018/01/nf1.html
VitsConfig
.. autoclass:: TTS.tts.configs.vits_config.VitsConfig
:members:
VitsArgs
.. autoclass:: TTS.tts.models.vits.VitsArgs
:members:
Vits Model
.. autoclass:: TTS.tts.models.vits.Vits
:members: