Pianist Transformer Base

--- license: apache-2.0 pipeline_tag: audio-to-audio tags: - piano - music - art - midi --- # Pianist Transformer This is the foundational **base model** for the Pianist Transformer, pre-trained on a massive **10-billion-token** corpus of diverse piano MIDI data. It has learned a deep, generalized understanding of musical structure, harmony, and rhythm. This model is **not fine-tuned for a specific task**. It is intended to be used as a powerful starting point for fine-tuning on various downstream music-related tasks. This work is based on the paper: **Pianist Transformer: Towards Expressive Piano Performance Rendering via Scalable Self-Supervised Pre-Training**. - **Paper:** https://arxiv.org/abs/2512.02652 - **Github:** https://github.com/yhj137/PianistTransformer - **Project Page:** https://yhj137.github.io/pianist-transformer-demo/ ## Model Description - **Architecture:** 135M-parameter asymmetric Encoder-Decoder Transformer. - **Pre-training Objective:** A self-supervised masked denoising task. The model was trained to reconstruct corrupted segments of musical sequences, compelling it to internalize the underlying principles of music without explicit labels. - **Data:** Pre-trained on a 10B-token corpus aggregated from Aria-MIDI, GiantMIDI-Piano, PDMX, and other public datasets. ## How to Use This model serves as a powerful foundation for the expressive performance rendering task. We encourage you to fine-tune it on your own aligned score-performance dataset to create a high-quality, customized rendering system. The official GitHub repository provides a complete framework to facilitate this process. It includes: - **Data pre-processing code** to help you prepare your own dataset in the required format. - **The full fine-tuning script** used in our paper. **➡️ [Get Started on GitHub](https://github.com/yhj137/PianistTransformer)** ## Citation If you use this model in your work, please cite the original paper: ```bibtex @misc{you2025pianisttransformerexpressivepiano, title={Pianist Transformer: Towards Expressive Piano Performance Rendering via Scalable Self-Supervised Pre-Training}, author={Hong-Jie You and Jie-Jing Shao and Xiao-Wen Yang and Lin-Han Jia and Lan-Zhe Guo and Yu-Feng Li}, year={2025}, eprint={2512.02652}, archivePrefix={arXiv}, primaryClass={cs.SD} } ```

Source context: 17 downloads · 0 likes · Pipeline audio-to-audio · Repo yhj137/pianist-transformer-base