bachsformer

KO | EN

gitlite — search

#react #ai #llm #android #machine-learning #deep-learning #linux #cli #kubernetes #vue #security #ios #openai #open-source

(★ 45)

A Bach music generator with Artificial Intelligence. This model is made by a VQ-VAE + Transformer (decoder-only). Sequences of midi 1 quarter length are compressed into 16 codebooks via VQ-VAE and a transformer learns how to generate the codebooks sequence to obtain a midi score.

bachsformer 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# bachsformer
![bachsformer](https://user-images.githubusercontent.com/66723651/207885919-bccaca01-c8ac-4a56-909b-cb9bad1e01de.png)

## Bach music generation
Many fantastic attempts have been made to generate music through artificial intelligence. Usually this task is addressed to processors, who obtain outstanding results in the generation of sequences, where the source vocabulary is constituted by the tokenization of the midi symbols. This approach is characterized by the construction of the source vocabulary, as it is not midi symbols that constitute it, but is mediated by a mid-level representation. First of all the original midi sequence is broken into quarters (1/4 of a musical bar). In doing so the time dimension is frozen. Each slice is then reconstructed from a VQ-VAE, composed of a sequence of 16 codebooks, that I called Ludovico-VAE (to pay homage to the great Ludwig Van Beethoven). Once the VQ-VAE has been trained, a decoder-only transformer (the Bachsformer) is trained on the sequences of codebooks that recompose the training-set.
So the transformer learn how to generate coherent sequences of 192 codebooks indexes, which the Decoder of VQ-VAE will use to recompose the final midi score.
You can think at is as variation-performer based on the chosen artist voucabolary (J.S.Bach in this case), which is discretize w.r.t. a subdivision of musical tempo.

This repo comes with 2 pre-trained models, one for vq-vae and one for transformer. Please note that training is a very poor one, only few epochs for both the models.

Listen to this example of generated output!

https://user-images.githubusercontent.com/66723651/207885731-5edd503a-6d79-4a9f-8548-0ab4068ba4d1.mp4

## Credits
The implementation of transformer is taken by this awesome GPT implementation provided by @karpathy
https://github.com/karpathy/minGPT
Also thanks to @michelemancusi https://github.com/michelemancusi for its precious contribution to the idea!

## Installation

Clone the repo via 
```bash
git clone https://github.com/pier-maker92/bachsformer.git
```
Create a conda environment from .yml provided via
```bash
conda env create -f bachsformer.yml
```

## Generation
You can generate via generate.py script
```bash
python generate.py
```

## Dataset
The dataset provided for pre-trained models consist of 32 Golberg Variations from J.S. Bach. Midi files for training are placed inside data/midi folder. Feel free to try different/larger dataset but bear in mind that the midi files have to be perfectly quantized!

## Training
You have to train vq-vae first and then you will able to train the trasnformer on the codebooks indexes sequence
train vq-vae
```bash
python train_vq_vae.py
```
train bachsformer!
```bash
python train_transformer.py
```

## Contributing

Pull requests are welcome. For major changes, please open an issue first
to discuss what you would like to change.

Please make sure to update tests as appropriate.
For any question/feedback contact me at pierfrancesco.melucci@gmail.com

## License

MIT

bachsformer

bachsformer 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

bachsformer

bachsformer 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README