The project is an application of Deep Learning model RNN-LSTM which is used to generate continuous sequences learned from previous sequences. The model is implemented in music in midi format which provides sequences as notes and as a result future notes are predicted in order to create a new set of music which contaons the essence of that specoific genre by or LSTM trained model. The model is trained for each genre of the music and was able to generate music resembling that particular genre it was trained on. With the ability to generate melodies based on a specific genre, a personalized music can be generated for each person based on their data about their genre preference.
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.
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 magentaNOTE: In order to install the rtmidi package that we depend on, you may need to install headers for some sound libraries. On Linux, this command should install the necessary packages:
sudo apt-get install build-essential libasound2-dev libjack-devThe Magenta libraries are now available for use within Python programs and Jupyter notebooks, and the Magenta scripts are installed in your path!
If you have a GPU installed and you want Magenta to use it, you will need to follow the Manual Install instructions, but with a few modifications.
First, make sure your system meets the requirements to run tensorflow with GPU support.
Next, follow the Manual Install instructions, but install the
magenta-gpu package instead of the magenta package:
pip install magenta-gpuThe only difference between the two packages is that magenta-gpu depends on
tensorflow-gpu instead of tensorflow.
Magenta should now have access to your GPU.
Open a new terminal window so the environmental variable changes take effect and enter:
source activate magenta && pip install tensorflow-gpu &&
git clone https://github.com/piyusharma4/genre-melodies.git &&
cd genre-melodies &&
pip install -r requirements.txt &&
python create_dataset.py &&
sh train_model.shIf using a CPU replace 'tensorflow-gpu' with 'tensorflow'.
See the preprocessing and visualization notebook for sample outputs and visualization scripts.
Visualizations:
Tensorboard graph of trained network:
Each genre folder created contains 10 generated melodies for each particular genre. NOTE: Each generated music has asme MIDI note C5 (can be changed as a hyperparameter).
Check out some samples using Melody_RNN which gives better sweet sounding results for most of the genres.
We can generate music using Improv_RNN by Magenta to further use a specific combination of chords, just change the configuration to chord_pitches_improv.
Some generated music using chords are uploaded in SoundCloud. <---Check this one, I've named them according to my what my mood said while listening to them.
I've also included some of generated music using different model type implementation like Chords_Pitches_RNN,Polyphonic_RNN which can be found in above Chord_Pitches_RNN_Results and Polyphonic_RNN_Results folder respectively.