Magenta is a project from the Google Brain team that asks: Can we use machine learning to create compelling art and music? If so, how? If not, why not? We’ll use TensorFlow, and we’ll release our models and tools in open source on this GitHub. We’ll also post demos, tutorial blog postings, and technical papers. Soon we’ll begin accepting code contributions from the community at large. If you’d like to keep up on Magenta as it grows, you can read our blog and or join our discussion group.
The installation has three components. You are going to need Bazel to build packages, TensorFlow to run models, and an up-to-date version of this repository.
First, clone this repository:
git clone https://github.com/tensorflow/magenta.git
Next, install Bazel and TensorFlow. You'll need at least version 0.2.3 for Bazel and at least version 0.9 for TensorFlow. You can find instructions for the former here and the latter here. After that's done, run the tests with this command:
bazel test //magenta:all
Now that you have a working copy of Magenta, let's build your first MIDI dataset. We do this by creating a directory of MIDI files and converting them into NoteSequences. If you don't have any MIDIs handy, you can find some here from MidiWorld.
Build and run the script. Warnings may be printed by the MIDI parser if it encounteres a malformed MIDI file but these can be safely ignored. MIDI files that cannot be parsed will be skipped.
MIDI_DIRECTORY=<folder containing MIDI files. can have child folders.>
# TFRecord file that will contain NoteSequence protocol buffers.
SEQUENCES_TFRECORD=/tmp/notesequences.tfrecord
bazel run //magenta:convert_midi_dir_to_note_sequences -- \
--midi_dir=$MIDI_DIRECTORY \
--output_file=$SEQUENCES_TFRECORD \
--recursive
Note: To build and run in seperate commands, run
bazel build //magenta:convert_midi_dir_to_note_sequences
./bazel-bin/magenta/convert_midi_dir_to_note_sequences \
--midi_dir=$MIDI_DIRECTORY \
--output_file=$SEQUENCES_TFRECORD \
--recursive