https://github.com/sony/hft-transformer
Pytorch implementation of automatic music transcription method that uses a two-level hierarchical frequency-time Transformer architecture (hFT-Transformer).
https://github.com/sony/hft-transformer
Last synced: 3 months ago
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Pytorch implementation of automatic music transcription method that uses a two-level hierarchical frequency-time Transformer architecture (hFT-Transformer).
- Host: GitHub
- URL: https://github.com/sony/hft-transformer
- Owner: sony
- License: mit
- Created: 2023-06-26T08:31:19.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2023-07-11T01:33:58.000Z (over 2 years ago)
- Last Synced: 2025-04-19T16:53:02.331Z (6 months ago)
- Language: Python
- Homepage:
- Size: 35.2 KB
- Stars: 99
- Watchers: 3
- Forks: 11
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# hFT-Transformer
This repository contains the official PyTorch implementation of **"Automatic Piano Transcription with Hierarchical Frequency-Time Transformer"** presented in ISMIR2023 ([arXiv 2307.04305](https://arxiv.org/abs/2307.04305)).
## 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](requirements.txt)
## Usage
1) corpus generation (MAESTRO-V3)
```
$ ./corpus/EXE-CORPUS-MAESTRO.sh
```
2) training
```
$ ./training/EXE-TRAINING-MAESTRO.sh
```
3) evaluation
If you want to avoid training models from scratch, you can download and put the model under the `checkpoint/MAESTRO-V3` directory.
`model_016_003.pkl` is the model for MAESTRO.
```
$ wget https://github.com/sony/hFT-Transformer/releases/download/ismir2023/checkpoint.zip
$ unzip checkpoint.zip
$ ./evaluation/EXE-EVALUATION-MAESTRO.sh model_016_003.pkl test
```
If you want to evaluate the trained model using the validation set, you can change the second argument as below.
```
$ ./evaluation/EXE-EVALUATION-MAESTRO.sh model_016_003.pkl valid
```
## 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}
}
```
## Contact
- Keisuke Toyama (keisuke.toyama@sony.com)
## Reference
- PyTorch Seq2Seq (https://github.com/bentrevett/pytorch-seq2seq)