https://github.com/icoxfog417/magenta_session
:musical_keyboard: Music Session with Google Magenta
https://github.com/icoxfog417/magenta_session
machine-learning music webaudio-api
Last synced: about 1 year ago
JSON representation
:musical_keyboard: Music Session with Google Magenta
- Host: GitHub
- URL: https://github.com/icoxfog417/magenta_session
- Owner: icoxfog417
- License: mit
- Created: 2017-03-13T08:21:44.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2019-10-27T12:50:41.000Z (over 6 years ago)
- Last Synced: 2025-03-21T21:22:01.324Z (about 1 year ago)
- Topics: machine-learning, music, webaudio-api
- Language: JavaScript
- Homepage: http://magenta-session.herokuapp.com/
- Size: 19.5 MB
- Stars: 81
- Watchers: 6
- Forks: 134
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# magenta session
The code to session with [magenta](https://github.com/tensorflow/magenta).
Do you have any MIDI instrument? If then, you can do call & response with magenta!
(You don't have such instruments? Of course, you can play this without it!)
**You can see the sample play from here**
* [Playing Video](https://youtu.be/owOI2CMavoE)
* [Recorded Session](https://soundcloud.com/icoxfog417/magenta-sessioned-track)
(Sorry about my poor keyboard play!)
You can deploy your own Magenta Session to Heroku by following button.
[](https://heroku.com/deploy)
## Architecture
The model is ported from [ai-duet](https://github.com/googlecreativelab/aiexperiments-ai-duet).
## How to use
1. Install `magenta_session`
2. Run `python server/server.py`
3. Access the [Server(localhost:8080)](http://localhost:8080)
4. Session Now! (please refer following image).
### Additional Usage
* [Train your own model](https://github.com/icoxfog417/magenta_session/tree/master/scripts)
* [Session with your own model](https://github.com/icoxfog417/magenta_session/tree/master/server)
## Install
`magenta_session` depends on TensorFlow and magenta.
Please refer [magenta installation guide](https://github.com/tensorflow/magenta#installation).
### Miniconda
Install the [Miniconda](https://conda.io/miniconda.html) (Miniconda3 is also ok), and create the Magenta environment.
```
conda create -n magenta numpy scipy scikit-learn matplotlib jupyter pyyaml
```
(If you use Miniconda3, please set `python=2.7` additionaly when create magenta environment. Because Magenta only works on Python2!)
Then activate the `magenta` environment, and install the dependencies.
```
source activate magenta
pip install -r requirements.txt
```
**CAUTION**
* `pyenv` user will have the trouble with `source activate magenta`. To avoid this, configure your environment by `pyenv versions`, and use `pyenv local` to set the magenta environment that you created.
* `TensorFlow` does not support Windows except the Python3.5 version (and Magenta does not work on Python3.5!). So If you want to run it on Windows, you have to use [bash on Windows](https://msdn.microsoft.com/en-us/commandline/wsl/install_guide).
### Docker
Docker is an open-source containerization software which simplifies installation across various OSes.Once you have Docker installed, you can just run:
```bash
$ docker run -it --rm -p 80:8080 asashiho/magenta_session
```
If you want to build DockerImage yourself, you can just run:
```bash
$ docker build -t magenta_session .
$ docker run -it --rm -p 80:8080 magenta_session
```
**Tips!** Docker to automatically clean up the container and remove the file system when the container exits, you can add the `--rm`
You can now play with `magenta_session` at `http:///`.
Session Now and Enjoy Music!
## Dependencies
Python
* [magenta](https://github.com/tensorflow/magenta)
* [TensorFlow](https://github.com/tensorflow/tensorflow)
* [Flask](https://github.com/pallets/flask)
JavaScript
* [Tone.js](https://github.com/Tonejs/Tone.js)
* [MidiConvert](https://github.com/Tonejs/MidiConvert)
* [jQuery](https://github.com/jquery/jquery) (It's enough to such a simple application)
CSS
* [Bulma](http://bulma.io/)