Полная документация ниже — автоматически из репозитория magenta/magenta (ветка main). Оригинальные материалы проекта распространяются на условиях его лицензии.

Status

This repository is currently inactive (archived, read only) and serves only as a supplement some of our papers. We have transitioned to using individual repositories for new projects. For our current work, see the Magenta website and Magenta GitHub Organization.

Magenta

Magenta is a research project exploring the role of machine learning
in the process of creating art and music. Primarily this
involves developing new deep learning and reinforcement learning
algorithms for generating songs, images, drawings, and other materials. But it’s also
an exploration in building smart tools and interfaces that allow
artists and musicians to extend (not replace!) their processes using
these models. Magenta was started by some researchers and engineers
from the Google Brain team,
but many others have contributed significantly to the project. We use
TensorFlow and release our models and
tools in open source on this GitHub. If you’d like to learn more
about Magenta, check out our blog,
where we post technical details. You can also join our discussion
group.

This is the home for our Python TensorFlow library. To use our models in the browser with TensorFlow.js, head to the Magenta.js repository.

Getting Started

Take a look at our colab notebooks for various models, including one on getting started.
Magenta.js is also a good resource for models and demos that run in the browser.
This and more, including blog posts and Ableton Live plugins, can be found at https://magenta.tensorflow.org.

Magenta Repo

• Installation
• Using Magenta
• Development Environment (Advanced)

Installation

Magenta maintains a pip package for easy
installation. We recommend using Anaconda to install it, but it can work in any
standard Python environment. We support Python 3 (>= 3.5). These instructions
will assume you are using Anaconda.

Automated Install (w/ Anaconda)

If you are running Mac OS X or Ubuntu, you can try using our automated
installation script. Just paste the following command into your terminal.

curl https://raw.githubusercontent.com/tensorflow/magenta/main/magenta/tools/magenta-install.sh > /tmp/magenta-install.sh
bash /tmp/magenta-install.sh

After the script completes, open a new terminal window so the environment
variable changes take effect.

The Magenta libraries are now available for use within Python programs and
Jupyter notebooks, and the Magenta scripts are installed in your path!

Note that you will need to run source activate magenta to use Magenta every
time you open a new terminal window.

Manual Install (w/o Anaconda)

If the automated script fails for any reason, or you’d prefer to install by
hand, do the following steps.

Install the Magenta pip package:

pip install magenta

NOTE: In order to install the rtmidi package that we depend on, you may need to install headers for some sound libraries. On Ubuntu Linux, this command should install the necessary packages:

sudo apt-get install build-essential libasound2-dev libjack-dev portaudio19-dev

On Fedora Linux, use

sudo dnf group install "C Development Tools and Libraries"
sudo dnf install SAASound-devel jack-audio-connection-kit-devel portaudio-devel

The Magenta libraries are now available for use within Python programs and
Jupyter notebooks, and the Magenta scripts are installed in your path!

Using Magenta

You can now train our various models and use them to generate music, audio, and images. You can
find instructions for each of the models by exploring the models directory.

Development Environment

If you want to develop on Magenta, you’ll need to set up the full Development Environment.

First, clone this repository:

git clone https://github.com/tensorflow/magenta.git

Next, install the dependencies by changing to the base directory and executing the setup command:

pip install -e .

You can now edit the files and run scripts by calling Python as usual. For example, this is how you would run the melody_rnn_generate script from the base directory:

python magenta/models/melody_rnn/melody_rnn_generate --config=...

You can also install the (potentially modified) package with:

pip install .

Before creating a pull request, please also test your changes with:

pip install pytest-pylint
pytest

PIP Release

To build a new version for pip, bump the version and then run:

python setup.py test
python setup.py bdist_wheel --universal
twine upload dist/magenta-N.N.N-py2.py3-none-any.whl