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TundraandTabor/
FUNS

Name: FUNS
Availability: InStock

music

2502.18008

mit

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🎵 NotaGen: Advancing Musicality in Symbolic Music Generation with Large Language Model Training Paradigms

📖 Overview

NotaGen is a symbolic music generation model that explores the potential of producing high-quality classical sheet music. Inspired by the success of Large Language Models (LLMs), NotaGen adopts a three-stage training paradigm:

🧠 Pre-training on 1.6M musical pieces
🎯 Fine-tuning on ~9K classical compositions with period-composer-instrumentation prompts
🚀 Reinforcement Learning using our novel CLaMP-DPO method (no human annotations or pre-defined rewards required.)

Check our demo page and enjoy music composed by NotaGen!

⚙️ Environment Setup

bash

conda create --name notagen python=3.10
conda activate notagen
conda install pytorch==2.3.0 pytorch-cuda=11.8 -c pytorch -c nvidia
pip install accelerate
pip install optimum
pip install -r requirements.txt

🏋️ NotaGen Model Weights

Pre-training

We provide pre-trained weights of different scales:

Models	Parameters	Patch-level Decoder Layers	Character-level Decoder Layers	Hidden Size	Patch Length (Context Length)
NotaGen-small	110M	12	3	768	2048
NotaGen-medium	244M	16	3	1024	2048
NotaGen-large	516M	20	6	1280	1024

Fine-tuning

We fine-tuned NotaGen-large on a corpus of approximately 9k classical pieces. You can download the weights here.

Reinforcement-Learning

After pre-training and fine-tuning, we optimized NotaGen-large with 3 iterations of CLaMP-DPO. You can download the weights here.

🌟 NotaGen-X

Inspired by Deepseek-R1, we further optimized the training procedures of NotaGen and released a better version --- NotaGen-X. Compared to the version in the paper, NotaGen-X incorporates the following improvements:

We introduced a post-training stage between pre-training and fine-tuning, refining the model with a classical-style subset of the pre-training dataset.
We removed the key augmentation in the Fine-tune stage, making the instrument range of the generated compositions more reasonable.
After RL, we utilized the resulting checkpoint to gather a new set of post-training data. Starting from the pre-trained checkpoint, we conducted another round of post-training, fine-tuning, and reinforcement learning.

For implementation of pre-training, fine-tuning and reinforcement learning on NotaGen, please view our github page.

📚 Citation

If you find NotaGen or CLaMP-DPO useful in your work, please cite our paper.

bibtex

@misc{wang2025notagenadvancingmusicalitysymbolic,
      title={NotaGen: Advancing Musicality in Symbolic Music Generation with Large Language Model Training Paradigms}, 
      author={Yashan Wang and Shangda Wu and Jianhuai Hu and Xingjian Du and Yueqi Peng and Yongxin Huang and Shuai Fan and Xiaobing Li and Feng Yu and Maosong Sun},
      year={2025},
      eprint={2502.18008},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2502.18008}, 
}

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