TTS is a library for advanced text-to-speech generation built on deep learning research and designed to balance ease of training, speed, and output quality.
The library addresses the challenge of generating natural-sounding speech from text by combining multiple neural network components. It uses text-to-spectrogram models such as Tacotron, Tacotron2, and Glow-TTS to convert text into acoustic representations, then applies vocoder models like MelGAN and Multiband-MelGAN to synthesize audio waveforms from those representations. The toolkit includes a speaker encoder for computing speaker embeddings, enabling multi-speaker synthesis. Dataset curation tools help measure and improve training data quality before model training begins.
The library suits projects requiring multilingual speech synthesis, as it supports over twenty languages and ships with pretrained models ready for inference. It works well for both research and production use, with model conversion capabilities to PyTorch, TensorFlow 2.0, and TFLite formats. The modular code structure allows experimentation with new model architectures without extensive refactoring. Teams with GPU resources can leverage efficient multi-GPU training. The project provides a demo server, command-line tools for synthesis, and Jupyter notebooks for benchmarking, reducing the barrier to getting started.
The project maintains active public discussion channels including a dedicated Discourse forum for usage questions and a GitHub issue tracker for bug reports and feature requests. Development activity shows consistent engagement with both bug fixes and feature additions across the codebase. The library receives contributions from external developers and remains actively maintained with documentation including tutorials, model release notes, and contribution guidelines.
