NotaGen
N
Notagen
Overview :
NotaGen is an innovative symbolic music generation model that enhances music generation quality through three stages: pre-training, fine-tuning, and reinforcement learning. Utilizing large language model technology, it can generate high-quality classical music scores, bringing new possibilities to music creation. The model's main advantages include efficient generation, diverse styles, and high-quality output. It is applicable in music creation, education, and research, with broad application prospects.
Target Users :
This product is suitable for music creators, music educators, music researchers, and developers interested in music generation technology. It can help creators quickly generate high-quality musical scores, provide educators with teaching materials, provide researchers with research tools, and provide developers with a powerful technological foundation.
Total Visits: 492.1M
Top Region: US(19.34%)
Website Views : 113.7K
Use Cases
Music creators use NotaGen to quickly generate classical-style music scores, saving creation time.
Music educators utilize music scores generated by NotaGen as teaching materials to enrich course content.
Researchers use NotaGen to explore the potential and applications of symbolic music generation technology.
Features
Supports the pre-training stage, using large-scale music datasets for basic model training.
Provides fine-tuning functionality, optimized for specific styles of classical music.
Employs the CLaMP-DPO reinforcement learning method to optimize generation results without manual annotation.
Supports multiple model sizes, including NotaGen-small, NotaGen-medium, and NotaGen-large.
Provides a Gradio demo, allowing users to generate music via a web interface by inputting conditions.
Supports local deployment and online Colab notebook usage, allowing users to quickly get started.
Provides data pre-processing and post-processing tools to facilitate user data preparation and usage.
Supports the generation of various music styles, controlling generated content through conditional prompts.
How to Use
1. Install environment: Set up the Python environment and install the necessary libraries according to the README guide.
2. Download pre-trained model weights: Choose the NotaGen-small, medium, or large model according to your needs.
3. Fine-tune the model: Fine-tune the model using your own dataset to optimize the generation effect of a specific style.
4. Reinforcement learning optimization: Further improve the quality of generated music scores through the CLaMP-DPO method.
5. Use the Gradio demo: Run the local Gradio service or use the Colab notebook to generate music by inputting conditions.
6. Data processing: Use the provided tools to convert ABC notation files to MusicXML format.
7. Custom generation: Generate music scores with different styles and instrument combinations by modifying conditional prompts.
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