Voicebox implements a novel Text-to-Speech (TTS) model introduced by MetaAI, achieving state-of-the-art results. This repository provides a PyTorch implementation of the Voicebox architecture, leveraging rotary embeddings for enhanced performance. The model addresses challenges in TTS generation through adaptive normalization, building upon advancements in fields like flow matching and neural vocoders. It aims to provide an accessible platform for researchers to explore and build upon this cutting-edge technology.
This project facilitates experimentation with a recent SOTA TTS model. It incorporates modern techniques like rotary embeddings and adaptive normalization. The code provides a clear and organized structure for training and inference. Integration with Spear-TTS is a key feature, and ongoing work includes improvements to training stability, efficiency, and integration with other audio processing frameworks.
- Core Functionality: Text-to-speech synthesis using the Voicebox architecture.
- Supported Platforms: PyTorch, Python.
- Integration: Integration with Spear-TTS for text encoding.
- Performance: Designed for high-quality speech synthesis with focus on efficiency.
- Developer Experience: Includes clear usage examples and documentation; actively being improved.
The project is under active development with ongoing improvements and bug fixes. The core components (basic loss, flow matching) have been implemented and tested. There are several tasks outlined for enhancements in areas like training stability, audio encoding, and integration with other libraries. Community contributions are welcomed and actively incorporated.
This repository is valuable for researchers and developers interested in advanced TTS techniques. It offers a practical implementation of the Voicebox model, enabling experimentation with state-of-the-art speech synthesis. The project provides a foundation for understanding and adapting cutting-edge TTS research, particularly for those seeking to leverage rotary embeddings and adaptive normalization principles.