This library is work in progress, use it at your own risk. I'm open to feedback!
It is build using python on Lasagne-Theano for deep learning and Essentia for feature extraction. Currently, MIRdl is for easily doing music classification using any deep learning architecture available on Lasagne or Theano.
Installation
Requires having Lasagne-Theano (http://lasagne.readthedocs.org/en/latest/user/installation.html) and Essentia (http://essentia.upf.edu/documentation/installing.html) installed.
Important folders
- ./data/datasets: the library expects to have the dataset divided by folders that represent the tag to be predicted.
- ./data/preloaded: this directory contains the pickle files storing the datasets in a format readable for the library. The name of the pickle file contains all the parameters used for computing it.
- ./data/results: this directory stores the following files: .result (with training and test results), .training (having the training evolution, readable with utils.py!), .param (storing all the deep learning parameters used for each concrete experiment) and the .npz (where the best trained deep learning model is stored).
Important scripts
- runMIRdl.py: where the network architecture is selected, you can also set the input and training parameters.
- buildArchitecture.py: where the Lasagne-Theano network architecture is set.
- load_datasets.py: where audios are loaded, formatted and normalized to be fed into the net.
- MIRdl.py: main part of the library where the training happens.
- utils.py: it allows visualizing the training results (./data/results).
Steps for using MIRdl
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0.0) Install.
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0.1) Understand this tutorial: http://lasagne.readthedocs.org/en/latest/user/tutorial.html. This library is based on it!
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1) Download a dataset. Copy it in ./data/datasets. The library expects to have the dataset divided by folders that represent the tag to be predicted. For example, for the GTZAN dataset (http://marsyasweb.appspot.com/download/data_sets/) the library expects:
./data/datasets/GTZAN/blues
./data/datasets/GTZAN/classical
(...)
./data/datasets/GTZAN/rock
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2) Adapt the load_datasets.py function to work using your dataset. We recommend you to use first the GTZAN dataset (already implemented) to understand how it works.
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3) Set the runMIRdl.py parameters and the deep learning architecture in buildArchitecture.py.
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4) Run runMIRdl.py.
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5) [Optional] Visualize what the net has learned with utils.py.
Future features
- Autoencoders support.
- AcousticBrainz.org support.