This project is a deep learning text-to-speech toolkit used for training and deploying neural speech synthesis models. It provides a comprehensive framework for converting written text into spoken audio, utilizing neural vocoders to transform synthesized spectrograms into high-fidelity audio waveforms.
The toolkit includes a voice cloning system that replicates specific human voices by extracting speaker embeddings from short audio samples. It also supports multi-speaker audio synthesis, allowing the generation of speech across different vocal identities using specialized model architectures.
The system covers the full speech synthesis pipeline, including tools for speech dataset curation, custom model training with performance tracking, and a command-line interface for audio generation. For network access, it provides a self-hosted HTTP server to deploy speech synthesis models as an API.