--- license: mit tags: - music pipeline_tag: text-to-audio library_name: transformers --- # 🎡 NotaGen: Advancing Musicality in Symbolic Music Generation with Large Language Model Training Paradigms

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NotaGen

## πŸ“– 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 - πŸš€ **Reinforcement Learning** using our novel **CLaMP-DPO** method (no human annotations or pre-defined rewards required.) Check our [demo page](https://electricalexis.github.io/notagen-demo/) and enjoy music composed by NotaGen! ## βš™οΈ 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 ``` ## πŸ‹οΈ 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](https://huggingface.co./ElectricAlexis/NotaGen/blob/main/weights_notagen_pretrain_p_size_16_p_length_2048_p_layers_12_c_layers_3_h_size_768_lr_0.0002_batch_8.pth) | 110M | 12 | 3 | 768 | 2048 | | [NotaGen-medium](https://huggingface.co./ElectricAlexis/NotaGen/blob/main/weights_notagen_pretrain_p_size_16_p_length_2048_p_layers_16_c_layers_3_h_size_1024_lr_0.0001_batch_4.pth) | 244M | 16 | 3 | 1024 | 2048 | | [NotaGen-large](https://huggingface.co./ElectricAlexis/NotaGen/blob/main/weights_notagen_pretrain_p_size_16_p_length_1024_p_layers_20_c_layers_6_h_size_1280_lr_0.0001_batch_4.pth) | 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](https://huggingface.co./ElectricAlexis/NotaGen/blob/main/weights_notagen_pretrain-finetune_p_size_16_p_length_1024_p_layers_c_layers_6_20_h_size_1280_lr_1e-05_batch_1.pth). ### Reinforcement-Learning After pre-training and fine-tuning, we optimized NotaGen-large with 3 iterations of CLaMP-DPO. You can download the weights [here](https://huggingface.co./ElectricAlexis/NotaGen/blob/main/weights_notagen_pretrain-finetune-RL3_beta_0.1_lambda_10_p_size_16_p_length_1024_p_layers_20_c_layers_6_h_size_1280_lr_1e-06_batch_1.pth). ### 🌟 NotaGen-X Inspired by Deepseek-R1, we further optimized the training procedures of NotaGen and released a better version --- [NotaGen-X](https://huggingface.co./ElectricAlexis/NotaGen/blob/main/weights_notagenx_p_size_16_p_length_1024_p_layers_20_h_size_1280.pth). 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. ## 🎹 Local Gradio Demo We developed a local Gradio demo for NotaGen-X. You can input **"Period-Composer-Instrumentation"** as the prompt to have NotaGen generate music！

NotaGen Gradio Demo

Deploying NotaGen-X inference locally requires at least 40GB of GPU memory. For implementation details, please view [gradio/README.md](https://github.com/ElectricAlexis/NotaGen/blob/main/gradio/README.md). We are also working on developing an online demo. ## πŸ› οΈ Data Pre-processing & Post-processing For converting **ABC notation** files from / to **MusicXML** files, please view [data/README.md](https://github.com/ElectricAlexis/NotaGen/blob/main/data/README.md) for instructions. To illustrate the specific data format, we provide a small dataset of **Schubert's lieder** compositions from the [OpenScore Lieder](https://github.com/OpenScore/Lieder), which includes: - πŸ—‚οΈ Interleaved ABC folders - πŸ—‚οΈ Augmented ABC folders - πŸ“„ Data index files for training and evaluation You can download it [here](https://drive.google.com/drive/folders/1iVLkcywzXGcHFodce9nDQyEmK4UDmBtY?usp=sharing) and put it under ```data/```. In the instructions of **Fine-tuning** and **Reinforcement Learning** below, we will use this dataset as an example of our implementation. **It won't include the "period-composer-instrumentation" conditioning**, just for showing how to adapt the pretrained NotaGen to a specific music style. ## 🧠 Pre-train If you want to use your own data to pre-train a blank **NotaGen** model, please: 1. Preprocess the data and generate the data index files following the instructions in [data/README.md](https://github.com/ElectricAlexis/NotaGen/blob/main/data/README.md) 2. Modify the parameters in ```pretrain/config.py``` Use this command for pre-training: ```bash cd pretrain/ accelerate launch --multi_gpu --mixed_precision fp16 train-gen.py ``` ## 🎯 Fine-tune Here we give an example on fine-tuning **NotaGen-large** with the **Schubert's lieder** data mentioned above. **Notice:** The use of **NotaGen-large** requires at least **40GB of GPU memory** for training and inference. Alternatively, you may use **NotaGen-small** or **NotaGen-medium** and change the configuration of models in ```finetune/config.py```. ### Configuration - In ```finetune/config.py```: - Modify the ```DATA_TRAIN_INDEX_PATH``` and ```DATA_EVAL_INDEX_PATH```: ```python # Configuration for the data DATA_TRAIN_INDEX_PATH = "../data/schubert_augmented_train.jsonl" DATA_EVAL_INDEX_PATH = "../data/schubert_augmented_eval.jsonl" ``` - Download pre-trained NotaGen weights, and modify the ```PRETRAINED_PATH```: ```python PRETRAINED_PATH = "../pretrain/weights_notagen_pretrain_p_size_16_p_length_1024_p_layers_20_c_layers_6_h_size_1280_lr_0.0001_batch_4.pth" # Use NotaGen-large ``` - ```EXP_TAG``` is for differentiating the models. It will be integrated into the ckpt's name. Here we set it to ```schubert```. - You can also modify other parameters like the learning rate. ### Execution Use this command for fine-tuning: ```bash cd finetune/ CUDA_VISIBLE_DEVICES=0 python train-gen.py ``` ## πŸš€ Reinforcement Learning (CLaMP-DPO) Here we give an example on how to use **CLaMP-DPO** to enhance the model fine-tuned with **Schubert's lieder** data. ### βš™οΈ CLaMP 2 Setup Download model weights and put them under the ```clamp2/```folder: - [CLaMP 2 Model Weights](https://huggingface.co./sander-wood/clamp2/blob/main/weights_clamp2_h_size_768_lr_5e-05_batch_128_scale_1_t_length_128_t_model_FacebookAI_xlm-roberta-base_t_dropout_True_m3_True.pth) - [M3 Model Weights](https://huggingface.co./sander-wood/clamp2/blob/main/weights_m3_p_size_64_p_length_512_t_layers_3_p_layers_12_h_size_768_lr_0.0001_batch_16_mask_0.45.pth) ### πŸ” Extract Ground Truth Features Modify ```input_dir``` and ```output_dir``` in ```clamp2/extract_clamp2.py```: ```python input_dir = '../data/schubert_interleaved' # interleaved abc folder output_dir = 'feature/schubert_interleaved' # feature folder ``` Extract the features: ``` cd clamp2/ python extract_clamp2.py ``` ### πŸ”„ CLaMP-DPO Here we give an example of an iteration of **CLaMP-DPO** from the initial model fine-tuned on **Schubert's lieder** data. #### 1. Inference - Modify the ```INFERENCE_WEIGHTS_PATH``` to path of the fine-tuned weights and ```NUM_SAMPLES``` to generate in ```inference/config.py```: ```python INFERENCE_WEIGHTS_PATH = '../finetune/weights_notagen_schubert_p_size_16_p_length_1024_p_layers_20_c_layers_6_h_size_1280_lr_1e-05_batch_1.pth' NUM_SAMPLES = 1000 ``` - Inference: ``` cd inference/ python inference.py ``` This will generate an ```output/```folder with two subfolders: ```original``` and ```interleaved```. The ```original/``` subdirectory stores the raw inference outputs from the model, while the ```interleaved/``` subdirectory contains data post-processed with rest measure completion, compatible with CLaMP 2. Each of these subdirectories will contain a model-specific folder, named as a combination of the model's name and its sampling parameters. #### 2. Extract Generated Data Features Modify ```input_dir``` and ```output_dir``` in ```clamp2/extract_clamp2.py```: ```python input_dir = '../output/interleaved/weights_notagen_schubert_p_size_16_p_length_1024_p_layers_20_c_layers_6_h_size_1280_lr_1e-05_batch_1_k_9_p_0.9_temp_1.2' # interleaved abc folder output_dir = 'feature/weights_notagen_schubert_p_size_16_p_length_1024_p_layers_20_c_layers_6_h_size_1280_lr_1e-05_batch_1_k_9_p_0.9_temp_1.2' # feature folder ``` Extract the features: ``` cd clamp2/ python extract_clamp2.py ``` #### 3. Statistics on Averge CLaMP 2 Score (Optional) If you're interested in the **Average CLaMP 2 Score** of the current model, modify the parameters in ```clamp2/statistics.py```: ```python gt_feature_folder = 'feature/schubert_interleaved' output_feature_folder = 'feature/weights_notagen_schubert_p_size_16_p_length_1024_p_layers_20_c_layers_6_h_size_1280_lr_1e-05_batch_1_k_9_p_0.9_temp_1.2' ``` Then run this script: ``` cd clamp2/ python statistics.py ``` #### 4. Construct Preference Data Modify the parameters in ```RL/data.py```: ```python gt_feature_folder = '../clamp2/feature/schubert_interleaved' output_feature_folder = '../clamp2/feature/weights_notagen_schubert_p_size_16_p_length_1024_p_layers_20_c_layers_6_h_size_1280_lr_1e-05_batch_1_k_9_p_0.9_temp_1.2' output_original_abc_folder = '../output/original/weights_notagen_schubert_p_size_16_p_length_1024_p_layers_20_c_layers_6_h_size_1280_lr_1e-05_batch_1_k_9_p_0.9_temp_1.2' output_interleaved_abc_folder = '../output/interleaved/weights_notagen_schubert_p_size_16_p_length_1024_p_layers_20_c_layers_6_h_size_1280_lr_1e-05_batch_1_k_9_p_0.9_temp_1.2' data_index_path = 'schubert_RL1.json' # Data for the first iteration of RL data_select_portion = 0.1 ``` In this script, the **CLaMP 2 Score** of each generated piece will be calculated and sorted. The portion of data in the chosen and rejected sets is determined by ```data_select_portion```. Additionally, there are also three rules to exclude problematic sheets from the chosen set: - Sheets with duration alignment problems are excluded; - Sheets that may plagiarize from ground truth data (ld_sim>0.95) are excluded; - Sheets where staves for the same instrument are not grouped together are excluded. The prefence data file will be names as ```data_index_path```, which records the file paths in chosen and rejected sets. Run this script: ``` cd RL/ python data.py ``` #### 5. DPO Training Modify the parameters in ```RL/config.py```: ```python DATA_INDEX_PATH = 'schubert_RL1.json' # Preference data path PRETRAINED_PATH = '../finetune/weights_notagen_schubert_p_size_16_p_length_1024_p_layers_20_c_layers_6_h_size_1280_lr_1e-05_batch_1.pth' # The model to go through DPO optimization EXP_TAG = 'schubert-RL1' # Model tag for differentiation ``` You can also modify other parameters like ```OPTIMATION_STEPS``` and DPO hyper-parameters. Run this script: ``` cd RL/ CUDA_VISIBLE_DEVICES=0 python train.py ``` After training, a model named ```weights_notagen_schubert-RL1_beta_0.1_lambda_10_p_size_16_p_length_1024_p_layers_20_c_layers_6_h_size_1280_lr_1e-06_batch_1.pth``` will be saved under ```RL/```. For the second round of CLaMP-DPO, please go back to the first inference stage, and let the new model to generate pieces. For this small experiment on **Schubert's lieder** data, we post our **Average CLaMP 2 Score** here for the fine-tuned model and models after each iteration of CLaMP-DPO, as a reference: | CLaMP-DPO Iteration (K) | Average CLaMP 2 Score | | ---- | ---- | | 0 (fine-tuned) | 0.324 | | 1 | 0.579 | | 2 | 0.778 | If you are interested in this method, have a try on your own style-specific dataset :D ## πŸ“š 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}, } ``` ## πŸ”— Links - [CLaMP 2 Paper](https://arxiv.org/pdf/2410.13267) - [CLaMP 2 Code](https://github.com/sanderwood/clamp2)