DiffSinger is the official open-source implementation of the AAAI 2022 paper Singing Voice Synthesis via Shallow Diffusion Mechanism. It generates sung vocals from lyrics and musical scores using diffusion models, and the same repository also provides DiffSpeech for text-to-speech. The project has earned more than 4,800 stars on GitHub, with releases continuing in 2026.
DiffSinger tackles singing voice synthesis, where a model must follow a melody precisely while sounding like natural, expressive singing. Its shallow diffusion mechanism starts the denoising process from a coarse mel-spectrogram prediction, which makes generation both faster and higher quality than naive diffusion. The repository includes training and inference pipelines, an end-to-end and a cascade workflow, a MIDI-based version, and interactive demos hosted on Hugging Face Spaces. Acceleration plug-ins such as PNDM can speed up sampling further. Documentation covers popular singing datasets such as Opencpop, making the project a common starting point for research into AI singing and voice synthesis.
The repository also documents the surrounding workflow in depth, including data preprocessing for singing corpora and auxiliary variance features such as pitch and energy prediction. Together these pieces give researchers what they need to train DiffSinger on their own recorded singing datasets rather than only running the provided checkpoints.
DiffSinger is free and open source under the MIT license. The code, documentation and community demos are available at no cost; you supply your own compute for training and inference.
DiffSinger is for speech and music researchers, students and creators experimenting with AI singing voices. It is well suited to academic work on singing voice synthesis and to developers prototyping virtual singers who want a proven, fully open PyTorch implementation to build on.