https://github.com/tanreinama/chord2melody
Chord2Melody - Automatic Music Generation AI
https://github.com/tanreinama/chord2melody
Last synced: about 18 hours ago
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Chord2Melody - Automatic Music Generation AI
- Host: GitHub
- URL: https://github.com/tanreinama/chord2melody
- Owner: tanreinama
- License: mit
- Created: 2020-11-08T00:00:59.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2021-01-08T01:42:56.000Z (over 4 years ago)
- Last Synced: 2024-08-04T01:24:08.769Z (9 months ago)
- Language: Python
- Size: 3.32 MB
- Stars: 18
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
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- ai-game-devtools - Chord2Melody
README
# Chord2Melody - Automatic Music Generation AI
[日本語](README_ja.md)
[demonstration1](http://ailab.nama.ne.jp/#chord2melody) | [demonstration2](http://ailab.nama.ne.jp/#melody2melody)
[samples](samples/)
[paper](report/paper.pdf)
## What is Chord2Melody?
It is an AI that composes music, with MIDI output.
It is based on GPT-2. You can generate music of arbitrary length, and you can also specify the chord progression to generate music.
Or they can compose a continuation of a music they've been working on.
The output music can be used as free content without any copyright or usage restrictions.
### Pretrained Models
There are two models that have been trained: the "base_5tr" with 5 output tracks and the "base_17tr" with 17 tracks.
| Model Name | output tracks | total number of parameters |
| ------------------------------------------------------------ | ------------------------------------------------------------ | -------------------------- |
| [base_5tr](https://www.nama.ne.jp/models/chord2melody-base_5tr.tar.gz)
([backup url](http://ailab.nama.ne.jp/models/chord2melody-base_5tr.tar.bz2)) | Drums, Piano, Guitar, Bass, Strings | 86167296 |
| [base_17tr](https://www.nama.ne.jp/models/chord2melody-base_17tr.tar.gz)
([backup url](http://ailab.nama.ne.jp/models/chord2melody-base_17tr.tar.bz2)) | Drums, Piano, Chromatic Percussion,
Organ, Guitar, Bass, Strings, Ensemble,
Brass, Reed, Pipe, Synth Lead, Synth Pad,
Synth Effects, Ethnic, Percussive, Sound Effects | 86941440 |
## Usage
First, clone chord2melody from GitHub.
```sh
$ git clone https://github.com/tanreinama/chord2melody
$ cd chord2melody
```
Then, download and extract the pretrained model from the link above.
```sh
$ wget https://www.nama.ne.jp/models/chord2melody-base_5tr.tar.bz2
$ tar xvfj chord2melody-base_5tr.tar.bz2
```
Launch chord2melody.py with specifying the model , a MIDI file is created.
```sh
$ python3 chord2melody.py --model base_5tr
```
There is no limit to the length of music that can be output. The total number of bars of music to be generated is specified with the "--num_bars" option.
```sh
$ python3 chord2melody.py --num_bars 48
```
### Chord to Melody
To specify the chord progression, use the "--chord" option, and use the "--chordbeat" option to specify how many chords to put in a measure.
```sh
$ python3 chord2melody.py --chord "C|C|C|C|Dm|Dm|Dm|Dm|G7|G7|G7|G7|Am|Am|Am|Am" --chordbeat 4
```
Chord" option, you can specify from [Available Chord](chordlist.txt) or "auto" connected with "|".
###Compose a continuation of a music
The program "melody2melody.py" will automatically compose a continuation of a music you have been working on. Use the "--input" option in "melody2melody.py" to specify the MIDI file you want to create a continuation of.
```sh
$ python3 melody2melody.py --input halfway.mid
```
### Specifies the fluctuation of the melody
By specifying "--top_p", you can specify the fluctuation of the song.
```sh
$ python3 chord2melody.py --top_k 25 --top_p 0
```
Put one or two numbers in "--top_p". The first number is used when there's a chord progression and the second (if specified) when the chord progression is "auto".
## Learning Methods
The data for training is from [Lakh Pianoroll Dataset](https://salu133445.github.io/lakh-pianoroll-dataset/). To train, download [lpd_5_full.tar.gz](https://drive.google.com/u/0/open?id=1tZKMhYazSWapFTUt7H6abHSo-QKH9ATC) or [lpd-17-full.tar.gz](https://drive. google.com/uc?export=download&id=1bJAC2SKhdKbKvpLL1V1l66cCgWX8eQEm) and extract it.
Next, go to the train directory and run "encode.py" to create a training data file.
The "--da" option allows you to specify data augmentation by modulation. The randomly modulated data is used to increase the training data.
```sh
$ cd train
$ python3 encode.py --dataset lpd_5 --output lpd_5_dataset
```
Run "train.py" with specify the type of dataset (lpd_5/lpd_7) in "--dataset" option and the encoded training data file in "--input" option.
```sh
$ python3 train.py --dataset lpd_5 --input lpd_5_dataset
```
### Fine Tuning
To fine-tune your own data, you must first edit the data to 5 or 17 tracks of MIDI data.
Then save the data in pypianoroll format, with the tracks in the same order as the original model.
Then you can create a training data file in encoder.py and fine tune it by specifying the original trained model in the "--restore_from" field.
```sh
$ python3 train.py --dataset lpd_5 --input lpd_5_dataset --restore_from ../base_5tr
```