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https://github.com/YatingMusic/remi
"Pop Music Transformer: Beat-based Modeling and Generation of Expressive Pop Piano Compositions", ACM Multimedia 2020
https://github.com/YatingMusic/remi
music-generation tensorflow transformer
Last synced: about 2 months ago
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"Pop Music Transformer: Beat-based Modeling and Generation of Expressive Pop Piano Compositions", ACM Multimedia 2020
- Host: GitHub
- URL: https://github.com/YatingMusic/remi
- Owner: YatingMusic
- License: gpl-3.0
- Created: 2020-01-31T15:21:46.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2022-11-15T14:34:43.000Z (almost 2 years ago)
- Last Synced: 2024-04-06T19:34:26.734Z (6 months ago)
- Topics: music-generation, tensorflow, transformer
- Language: Python
- Homepage:
- Size: 45.9 KB
- Stars: 519
- Watchers: 14
- Forks: 84
- Open Issues: 24
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# REMI
Authors: [Yu-Siang Huang](https://remyhuang.github.io/), [Yi-Hsuan Yang](http://mac.citi.sinica.edu.tw/~yang/)
[**Paper (arXiv)**](https://arxiv.org/abs/2002.00212) | [**Blog**](https://ailabs.tw/human-interaction/pop-music-transformer/) | [**Audio demo (Google Drive)**](https://drive.google.com/open?id=1LzPBjHPip4S0CBOLquk5CNapvXSfys54) | [**Online interactive demo**](https://vibertthio.com/transformer/)
REMI, which stands for `REvamped MIDI-derived events`, is a new event representation we propose for converting MIDI scores into text-like discrete tokens. Compared to the MIDI-like event representation adopted in exising Transformer-based music composition models, REMI provides sequence models a metrical context for modeling the rhythmic patterns of music. Using REMI as the event representation, we train a Transformer-XL model to generate minute-long Pop piano music with expressive, coherent and clear structure of rhythm and harmony, without needing any post-processing to refine the result. The model also provides controllability of local tempo changes and chord progression.
## Citation
```
@inproceedings{10.1145/3394171.3413671,
author = {Huang, Yu-Siang and Yang, Yi-Hsuan},
title = {Pop Music Transformer: Beat-Based Modeling and Generation of Expressive Pop Piano Compositions},
year = {2020},
isbn = {9781450379885},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3394171.3413671},
doi = {10.1145/3394171.3413671},
pages = {1180–1188},
numpages = {9},
location = {Seattle, WA, USA},
series = {MM '20}
}
```
## Getting Started
### Install Dependencies
* python 3.6 (recommend using [Anaconda](https://www.anaconda.com/distribution/))
* tensorflow-gpu 1.14.0 (`pip install tensorflow-gpu==1.14.0`)
* [miditoolkit](https://github.com/YatingMusic/miditoolkit) (`pip install miditoolkit`)
### Download Pre-trained Checkpoints
We provide two pre-trained checkpoints for generating samples.
* `REMI-tempo-checkpoint` [(428 MB)](https://drive.google.com/open?id=1gxuTSkF51NP04JZgTE46Pg4KQsbHQKGo)
* `REMI-tempo-chord-checkpoint` [(429 MB)](https://drive.google.com/open?id=1nAKjaeahlzpVAX0F9wjQEG_hL4UosSbo)
### Obtain the MIDI Data
We provide the MIDI files including local tempo changes and estimated chord. [(5 MB)](https://drive.google.com/open?id=1JUDHGrVYGyHtjkfI2vgR1xb2oU8unlI3)
* `data/train`: 775 files used for training models
* `data/evaluation`: 100 files (prompts) used for the continuation experiments
## Generate Samples
See `main.py` as an example:
```python
from model import PopMusicTransformer
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
def main():
# declare model
model = PopMusicTransformer(
checkpoint='REMI-tempo-checkpoint',
is_training=False)
# generate from scratch
model.generate(
n_target_bar=16,
temperature=1.2,
topk=5,
output_path='./result/from_scratch.midi',
prompt=None)
# generate continuation
model.generate(
n_target_bar=16,
temperature=1.2,
topk=5,
output_path='./result/continuation.midi',
prompt='./data/evaluation/000.midi')
# close model
model.close()
if __name__ == '__main__':
main()
```
## Convert MIDI to REMI
You can find out how to convert the MIDI messages into REMI events in the `midi2remi.ipynb`.
## FAQ
#### 1. How to synthesize the audio files (e.g., mp3)?
We strongly recommend using DAW (e.g., Logic Pro) to open/play the generated MIDI files. Or, you can use [FluidSynth](https://github.com/FluidSynth/fluidsynth) with a [SoundFont](https://sites.google.com/site/soundfonts4u/). However, it may not be able to correctly handle the tempo changes (see [fluidsynth/issues/141](https://github.com/FluidSynth/fluidsynth/issues/141)).
#### 2. What is the function of the inputs "temperature" and "topk"?
It is the temperature-controlled stochastic sampling methods are used for generating text from a trained language model. You can find out more details in the reference paper [CTRL: 4.1 Sampling](https://einstein.ai/presentations/ctrl.pdf).
> It is worth noting that the sampling method used for generation is very critical to the quality of the output, which is a research topic worthy of further exploration.
#### 3. How to finetune with my personal MIDI data?
Please see [issue/Training on custom MIDI corpus](https://github.com/YatingMusic/remi/issues/2)
## Acknowledgement
- The content of `modules.py` comes from the [kimiyoung/transformer-xl](https://github.com/kimiyoung/transformer-xl) repository.
- Thanks [@vibertthio](https://github.com/vibertthio) for the awesome online interactive demo.