vits
waveglow
vits | waveglow | |
---|---|---|
6 | 2 | |
6,324 | 2,222 | |
- | 0.5% | |
0.0 | 0.0 | |
5 months ago | 7 months ago | |
Python | Python | |
MIT License | BSD 3-clause "New" or "Revised" License |
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vits
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[D] TTS systems to download & run offline
And the voice encapsulation system VITS https://github.com/jaywalnut310/vits
- [D] What is the best open source text to speech model?
- githubで公開されている音声自動生成AI、日本のアニメキャラ2890名分の音声を学習素材に超速度で進化中
- 日本語英語中国語を読み上げできる音声自動生成AIがgithubで公開され話題に
- Adversarial Learning for End-to-End Text-to-Speech
- [R] Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech
waveglow
- [D] What is the best open source text to speech model?
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XQC Falls for dono thinkng its Adept
I had tried tacotron2 + waveglow and it's quite easy to get very good results. The hardest part is collecting clean data.
What are some alternatives?
tortoise-tts - A multi-voice TTS system trained with an emphasis on quality
tacotron2 - Tacotron 2 - PyTorch implementation with faster-than-realtime inference
tortoise-tts-fast - Fast TorToiSe inference (5x or your money back!)
tacotron - A TensorFlow implementation of Google's Tacotron speech synthesis with pre-trained model (unofficial)
radtts - Provides training, inference and voice conversion recipes for RADTTS and RADTTS++: Flow-based TTS models with Robust Alignment Learning, Diverse Synthesis, and Generative Modeling and Fine-Grained Control over of Low Dimensional (F0 and Energy) Speech Attributes.
Parallel-Tacotron2 - PyTorch Implementation of Google's Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modeling
STYLER - Official repository of STYLER: Style Factor Modeling with Rapidity and Robustness via Speech Decomposition for Expressive and Controllable Neural Text to Speech, INTERSPEECH 2021
vall-e - An unofficial PyTorch implementation of the audio LM VALL-E
hifi-gan - HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis
flowtron - Flowtron is an auto-regressive flow-based generative network for text to speech synthesis with control over speech variation and style transfer