TTS is a library for advanced Text-to-Speech generation. It's built on the latest research, was designed to achieve the best trade-off among ease-of-training, speed, and quality. TTS comes with pre-trained models, tools for measuring dataset quality, and is already used in 20+ languages for products and research projects. Released models in PyTorch, Tensorflow and TFLite. Tools to curate Text2Speech datasets underdataset_analysis. Demo server for model testing. Notebooks for extensive model benchmarking. Modular (but not too much) code base enabling easy testing for new ideas. Text2Spec models (Tacotron, Tacotron2, Glow-TTS, SpeedySpeech). Speaker Encoder to compute speaker embeddings efficiently. Vocoder models (MelGAN, Multiband-MelGAN, GAN-TTS, ParallelWaveGAN, WaveGrad, WaveRNN). If you are only interested in synthesizing speech with the released TTS models, installing from PyPI is the easiest option.

Features

  • High performance Deep Learning models for Text2Speech tasks
  • Fast and efficient model training
  • Detailed training logs on console and Tensorboard
  • Support for multi-speaker TTS
  • Efficient Multi-GPUs training
  • Ability to convert PyTorch models to Tensorflow 2.0 and TFLite for inference

Project Samples

Project Activity

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License

Mozilla Public License 1.0 (MPL)

Follow TTS

TTS Web Site

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Registered

2021-10-18