Muzic is a comprehensive research project from Microsoft Research Asia focused on advancing AI-driven music understanding and generation. It offers a suite of deep learning models for tasks ranging from symbolic music analysis and lyric transcription to complex music generation, including song writing, accompaniment, and text-to-music synthesis, targeting researchers and developers in the AI music domain.

How It Works

Muzic leverages large-scale pre-training and advanced deep learning architectures, including Transformers and diffusion models, to achieve state-of-the-art performance across various music AI tasks. Key innovations include contrastive learning for cross-modal music-language understanding (CLaMP) and specialized models for music structure modeling (Museformer) and versatile track generation (GETMusic).

Quick Start & Requirements

• Install via pip install -r requirements.txt.
• Requires Linux (tested on Ubuntu 16.04.6 LTS), CUDA 10, and Python 3.6.12.
• Detailed instructions for individual models are in their respective sub-folders.
• Music samples are available at https://ai-muzic.github.io/.

Highlighted Details

• CLaMP received the Best Student Paper Award at ISMIR 2023.
• Introduces MusicAgent, an AI agent for versatile music processing using LLMs.
• Offers MuseCoco for text-to-symbolic music generation and GETMusic for universal track generation.
• Features models for lyric-to-melody, rap generation, and high-fidelity singing voice synthesis.

Maintenance & Community

The project is driven by Microsoft Research Asia with contributions from external collaborators. It adheres to the Microsoft Open Source Code of Conduct. Interested individuals can contact Xu Tan (tanxu2012@gmail.com) for research opportunities.

Licensing & Compatibility

The project's licensing is not explicitly stated in the README, but it is a Microsoft Research project, implying potential use restrictions. Contributions require agreeing to a Contributor License Agreement (CLA).

Limitations & Caveats

The project specifies older CUDA (10) and Python (3.6) versions, which may require environment management for compatibility with modern systems. The README does not detail specific hardware requirements beyond CUDA, and the breadth of models suggests significant computational resources may be needed.