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https://github.com/valentinvignal/midigenerator
Generate midi file with deep neural network :notes:
https://github.com/valentinvignal/midigenerator
deep-neural-network keras midi music21 pygame tensorflow
Last synced: about 10 hours ago
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Generate midi file with deep neural network :notes:
- Host: GitHub
- URL: https://github.com/valentinvignal/midigenerator
- Owner: ValentinVignal
- Created: 2019-06-20T14:50:14.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-11-22T05:49:33.000Z (almost 2 years ago)
- Last Synced: 2023-03-05T10:53:30.123Z (over 1 year ago)
- Topics: deep-neural-network, keras, midi, music21, pygame, tensorflow
- Language: Python
- Homepage:
- Size: 2.71 MB
- Stars: 30
- Watchers: 0
- Forks: 2
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
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README
# Midi Generator
## Introduction
This project aim to train different neural networks on midi dataset to
generate music
## Context
This repository contains the code of my Master Thesis for my Master in the School of Computing of the [National University of Singapore](https://www.comp.nus.edu.sg/) ***""Music completion with deep probabilistic models"***
The Report, Abstract and Presentation's slides are available [here](https://github.com/ValentinVignal/Dissertation-Report)
## Files
### `bayesian-opt.py`
```
python bayesian-opt.py
```
It is used to find the best hyper parameters to train a model
### `compute_data.py`
```
python compute_data.py
```
It is used to compute the `.mid` files of the dataset and create the
*numpy* arrays used to train a model.
### `generate.py`
```
python generate.py
```
Loads a trained model and generate music from it.
### `train.py`
```
python train.py
```
Creates or loads a model, train it, and also generate music at the end.
# How to use it
## Setup
First, get a midi dataset and save all the song in a folder `Dataset`
The emplacement of this folder has to be at the emplacement `../../../../../storage1/valentin/`.
To change it, go in the files `src/GlobalVariables/path.py` and modify it.
The midi files have to be in a folder.
The name of the folder is the name of the dataset
## Get the information of the dataset
To get the information of the dataset, run
```
python debug/check_dataset.py
```
Main options:
- `data` is the name of the dataset name
- `-h` or `--help` plot the options of the file
- `--notes-range` the range of the notes. Default is `0:88`
- `--instruments` the instruments separated by a `,` (ex: `Piano,Trombone`)
- `--bach` for bach dataset (4 voices of piano)
- `--mono` to specify to use mono encoding for monophonic music
- `--no-transpose` don't transpose the songs in C major or A minor
The file will print the number of available songs and the `notes-range` to specify to not loss any data
## Compute the dataset
To extract the tensors from a midi dataset and save them
```
python compute_data.py
```
The main options are:
- `data` is the name of the dataset
- `-h` or `--help` will print the options of the file
- `--notes-range` to specify the range of the notes to consider
- `--instruments` the instruments separated by a `,` (ex: `Piano,Trombone`)
- `--bach` for bach dataset (4 voices of piano)
- `--mono` to specify to use mono encoding for monophonic music
- `--no-transpose` don't transpose the songs in C major or A minor
## Train and use a model
To train, evaluation, create songs with a model, run
```
python train.py
```
The main options are:
- `-h` or `--help` to print the options of the file
- `-d` or `--data` the name of the dataset
- `--data-test` the name of the test dataset
- `-m` or `--model` to specify the model to use (`modelName,nameParam,nbSteps`)
- `-l` or `--load` to load the id of the model (`name-modelName,nameParam,nbSteps-epochs-id`)
- `--mono` is used to use a monophonic dataset
- `--no-transposed` is used to not use a transpose dataset (to C major or A minor)
- `-e` or `--epochs` number of epochs
- `--gpu` specifies the GPU to use
- `-b` or `--batch` the batch size
- `--lr` specifies the learning rate
- `--no-rpoe` is used to not used the RPoE layer (Recurrent Product of Experts)
- `--validation` the proportion of the data tu use as a validation set
- `--predict-offset` the offset of prediction (use `2` for the band player script)
- `--evaluate` to evaluate the model on the test dataset
- `--generate` do the _generate_ task
- `--generate-fill` do the _fill_ task
- `--redo-generate` do the _redo_ task
- `--noise` value of the noise in the inputs of the training data
- `-n` or `--name` to give a name to the model
- `--use-binary` use the sigmoid loss for a _note\_continue_ for monophonic music
To get the _best results_ from the report, run:
```
python train.py -d DATANAME --data-test DATATESTNAME -m MRRMVAE,1,8 --use-binary --mono --evaluate --generate --generate-fill --redo-generate
```
## Band Player
This file load a trained model and use a it to play with the user in real time
```
python controller.py
```
The main options are:
- `-h` or `--help` to print the options of the file
- `--inst` to specify what instrument sound the user wants
- `--tempo` specify the tempo
- `-l` or `--load` to load the id of the model (`name-modelName,nameParam,nbSteps-epochs-id`)
- `--played-voice` the voice played by the user in the band
- `--inst-mask` list of the mask to choose the voices the user wants to play with
(to play the first voice and with the second and last voice for a model with 5 voices: `[1,1,0,0,1]`)
- `--nb-steps-shown` the number of steps showed on the piano roll plot