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Bert-VITS2 is a neural text-to-speech project that combines a VITS2 backbone with a multilingual BERT front-end to produce high-quality speech in multiple languages. The core idea is to use BERT-style contextual embeddings for text encoding while relying on a refined VITS2 architecture for acoustic generation and vocoding. The repository includes everything needed to train, fine-tune, and run the model, from configuration files to preprocessing scripts, spectrogram utilities, and training entrypoints for multi-GPU and multi-node setups. It provides emotional modeling through “emo embeddings,” allowing voices to be conditioned on different affective states during synthesis. Releases include optimizations for Japanese and English alignment, expanded training data, spec caching and pre-generation tools, as well as ONNX export for more lightweight inference deployments.
Features
- VITS2-based acoustic backbone combined with multilingual BERT text encoder for high-quality TTS
- Support for emotional embeddings to control voice emotion and style during synthesis
- Training pipeline with scripts for preprocessing, dataset filelists, spectrogram generation, and multi-GPU / multi-node runs
- Web UI and inference scripts (webui.py, infer.py) to test and use models interactively
- ONNX export and dedicated onnx_infer.py for lighter-weight deployment environments
- Spec cache and pre-generation utilities plus model-mixing tools for advanced fine-tuning and customization
Project Samples
