README.md

Compose & Embellish: A Transformer-based Piano Generation System

Official PyTorch implementation of the paper:

• Shih-Lun Wu and Yi-Hsuan Yang
  Compose & Embellish: Well-Structured Piano Performance Generation via A Two-Stage Approach
  Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2023
  Paper | Audio demo (Google Drive) | Model weights

Changelog

• [24-07-29] Added transformers GPT-2 implementation for Embellish model, which doesn't depend on fast-transformers package.

Prerequisites

• Python 3.8 and CUDA 10.2 recommended
• Install dependencies

  pip install -r requirements.txt
  
  # Optional, only required if you are using the config `stage02_embellish/config/pop1k7_default.yaml`
  pip install git+https://github.com/cifkao/fast-transformers.git@39e726864d1a279c9719d33a95868a4ea2fb5ac5
  

• Download trained models from HuggingFace Hub (make sure you're in repository root directory)

  git clone https://huggingface.co/slseanwu/compose-and-embellish-pop1k7
  

Generate piano performances (with our trained models)

• Stage 1: generate lead sheets (i.e., melody + chord progression)

  python3 stage01_compose/inference.py \
    stage01_compose/config/pop1k7_finetune.yaml \
    generation/stage01 \
    20
  

  You'll have 20 lead sheets under generation/stage01 after this step.

• Stage 2: generate full performances conditioned on Stage 1 lead sheets, using GPT-2 backbone

  python3 stage02_embellish/inference_gpt2.py \
    stage02_embellish/config/pop1k7_gpt2.yaml \
    generation/stage01 \
    generation/stage02_gpt2
  

  The samp_**_2stage_samp**.mid files under generation/stage02 are the final results.

  • (Optional) Use Performer (from fast-transformers) backbone for stage 2 instead

    python3 stage02_embellish/inference.py \
      stage02_embellish/config/pop1k7_default.yaml \
      generation/stage01 \
      generation/stage02_performer
    

Train (finetune) models on AILabs.tw Pop1K7 dataset

• Stage 1: lead sheet (i.e. "Compose") model

  python3 stage01_compose/train.py stage01_compose/config/pop1k7_finetune.yaml
  

• Stage 2: performance (i.e. "Embellish") model w/ GPT-2 backbone

  python3 stage02_embellish/train.py stage02_embellish/config/pop1k7_gpt2.yaml
  

  • (Optional) use Performer backbone instead, which allows longer context window

    python3 stage02_embellish/train.py stage02_embellish/config/pop1k7_default.yaml
    

Note that these two training stages may be run in parallel.

Train on custom datasets

If you'd like to experiment with your own datasets, we suggest that you

• read our dataloaders (stage 1, stage 2) and .pkl files of our processed datasets (stage 1, stage 2) to understand what the models receive as inputs
• refer to CP Transformer repo for a general guide on converting audio/MIDI files to event-based representations
• use musical structure analyzer to get required structure markings for our stage 1 models.

Acknowledgements

We would like to thank the following people for their open-source implementations that paved the way for our work:

• Performer (fast-transformers): Angelos Katharopoulos (@angeloskath) and Ondřej Cífka (@cifkao)
• Transformer w/ relative positional encoding: Zhilin Yang (@kimiyoung)
• Musical structure analysis: Shuqi Dai (@Dsqvival)
• LakhMIDI melody identification: Thomas Melistas (@gulnazaki)
• Skyline melody extraction: Wen-Yi Hsiao (@wayne391) and Yi-Hui Chou (@sophia1488)

BibTex

If this repo helps with your research, please consider citing:

@inproceedings{wu2023compembellish,
  title={{Compose \& Embellish}: Well-Structured Piano Performance Generation via A Two-Stage Approach},
  author={Wu, Shih-Lun and Yang, Yi-Hsuan},
  booktitle={Proc. Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2023},
  url={https://arxiv.org/pdf/2209.08212.pdf}
}