Authors: Wen-Yi Hsiao, Jen-Yu Liu, Yin-Cheng Yeh and Yi-Hsuan Yang
Paper (arXiv) | Audio demo (Google Drive) | Blog | Colab notebook
Official PyTorch implementation of AAAI2021 paper "Compound Word Transformer: Learning to Compose Full-Song Musicover Dynamic Directed Hypergraphs".
We presented a new variant of the Transformer that can processes multiple consecutive tokens at once at a time step. The proposed method can greatly reduce the length of the resulting sequence and therefore enhance the training and inference efficiency. We employ it to learn to compose expressive Pop piano music of full-song length (involving up to 10K individual to23 kens per song). In this repository, we open source our Ailabs.tw 1K7 dataset, and the codes for unconditional generation.
- python 3.6
- Required packages:
- madmom
- miditoolkit
- pytorch-fast-transformers
chorder is our in-house rule-based symbolic chord recognition algorithm, which is developed by our former intern - joshuachang2311. He is also a jazz pianist 🎹.
In this work, we conduct two scenario of generation:
-
unconditional generation
- To see the experimental results and discussion, please refer to here.
-
conditional generation, leadsheet to full midi (ls2midi)
- [Work in progress] We plan to open source the code associated with this part in the future.
- melody extracyion (skyline)
- objective metrics
- model
- [Work in progress] We plan to open source the code associated with this part in the future.
To prepare your own training data, please refer to documentaion for further understanding.
Or, you can start with our AIlabs.tw Pop1K7, which is available here.
The colab notebook is now available here.
Thanks our intern AdarshKumar712 for organizing the codes.
- PyTorch codes for transformer-XL is modified from kimiyoung/transformer-xl.
- Thanks Yu-Hua Chen and Hsiao-Tzu Hung for helping organize the codes.