README.md

hFT-Transformer

This repository forks "Automatic Piano Transcription with Hierarchical Frequency-Time Transformer" presented in ISMIR2023 (arXiv 2307.04305). and implements an inference script to transcribe directories of piano files.

Usage

To do training and evaluation, see the original repo.

To do inference, first download model_016_003.pkl:

$ wget https://github.com/sony/hFT-Transformer/releases/download/ismir2023/checkpoint.zip
$ unzip checkpoint.zip

Then, put the files you want to transcribe in a directory, <input_dir>. They can be .wav or .mp3 files.

python evaluation/transcribe_new_files.py \
    -input_dir_to_transcribe <input_dir> \
    -output_dir <output_dir> \
    -f_config corpus/MAESTRO-V3/dataset/config.json \
    -model_file evaluation/checkpoint/MAESTRO-V3/model_016_003.pkl

NOTE: Inference here uses the model that the original authors trained for MAESTRO. We haven't yet evaluated it on different datasets yet and thus don't know how transferrable it is, we just wrote scripts to run it. Evaluation to come.

Development Environment

• OS
  • Ubuntu 18.04
• memory
  • 32GB
• GPU
  • corpus generation, evaluation
    • NVIDIA GeForce RTX 2080 Ti
  • training
    • NVIDIA A100
• Python
  • 3.6.9
• Required Python libraries
  • requirements.txt

Citation

Keisuke Toyama, Taketo Akama, Yukara Ikemiya, Yuhta Takida, Wei-Hsiang Liao, and Yuki Mitsufuji, "Automatic Piano Transcription with Hierarchical Frequency-Time Transformer," in Proceedings of the 24th International Society for Music Information Retrieval Conference, 2023.

@inproceedings{toyama2023,
    author={Keisuke Toyama and Taketo Akama and Yukara Ikemiya and Yuhta Takida and Wei-Hsiang Liao and Yuki Mitsufuji},
    title={Automatic Piano Transcription with Hierarchical Frequency-Time Transformer},
    booktitle={Proceedings of the 24th International Society for Music Information Retrieval Conference},
    year={2023}
}