Muzic is a research project on AI music understanding and generation that combines deep learning techniques to enable both analysis and creation of musical content.
The project addresses the challenge of building systems that can comprehend and generate music across multiple dimensions. It tackles music understanding through symbolic music analysis, automatic lyrics transcription, and contrastive language-music pre-training. For generation, it covers song writing with lyric-to-melody and melody-to-lyric conversion, lyric generation, music structure and form generation, multi-track accompaniment generation, text-to-music synthesis, and singing voice synthesis. The approach integrates these capabilities into an AI agent framework that can coordinate multiple music tasks.
This project suits researchers and practitioners working on music AI systems who need reference implementations and pre-trained models across the full spectrum of music understanding and generation tasks. It is particularly valuable for those building systems that require coordination between lyrics, melody, structure, and multi-track audio. The breadth of included models means adopters can leverage existing components rather than building from scratch, though the project is positioned as research work rather than a production-ready framework.
The project maintains active development across multiple research directions simultaneously, with ongoing work spanning symbolic music understanding, various generation tasks including text-to-music and singing voice synthesis, and agent-based music creation systems. The codebase includes implementations of numerous published research models with associated sample outputs demonstrating the capabilities of each component.
