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Magenta has six pretrained performance rnn models that generate sequences. Provide an interface similar to https://magenta.tensorflow.org/demos/performance_rnn/index.html
This project is a WIP.

To run app:

1. export FLASK_APP=app.py
2. flask run

Documentation for Magenta:
https://github.com/magenta/magenta/tree/master/magenta/models/performance_rnn

Pretrained Models Methodology Details: https://arxiv.org/pdf/1808.03715v1.pdf The paper details that current state of art music generation techniques doesn't capture the expressive timing (tempo) and dynamics (how much louder/quieter the music gets) information. They created an LSTM based recurrent network model that jointly predicts notes and also expressive timing and dynamic.

Keywords: beam search, LSTM, teacher forcing

Twimlai: https://www.youtube.com/watch?v=8AARfVVrdXc Some uses of the Performance RNN: https://www.youtube.com/watch?v=Ye0Geuo1Iu8 Good Practices for Deployment: https://flask.palletsprojects.com/en/1.1.x/deploying/#deployment Flask Tutorial https://rollout.io/blog/python-feature-flag-guide/ working with midi files: https://www.twilio.com/blog/working-with-midi-data-in-python-using-mido MIDI Editor: https://solmire.com/midieditor/editor.php (to speed up/slow down midi files) MIDI Trimmer: http://midi.mathewvp.com/midiTrim.php (to make the midi files into shorter clips) MIDI Timing Explanation: https://sites.uci.edu/camp2014/2014/05/19/timing-in-midi-files/

Types of Models all translates position and velocity to complexity, pitch and tempo Performance - Basic performance model that

Todo: Test model selection in form <-- done --> Embed Bokeh into HTML Play sequence upon model selection Trim more 30 sec clips (Erik Satie Trois Gymnopedies , moonlight sonata) Try out num steps argument <-- done --> Find a midi player for flask