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urnn

Code for paper "Full-Capacity Unitary Recurrent Neural Networks." Based on the complex_RNN repository from github.com/amarshah/complex_RNN.

Code coming soon for other experiments.

If you find this code useful, please cite the following references:

[1] M. Arjovsky, A. Shah, and Y. Bengio, “Unitary Evolution Recurrent Neural Networks,” Proc. International Conference on Machine Learning (ICML), 2016, pp. 1120–1128.

[2] S. Wisdom, T. Powers, J.R. Hershey, J. Le Roux, and L. Atlas, "Full-Capacity Unitary Recurrent Neural Networks," Advances in Neural Information Processing Systems (NIPS), 2016.

Instructions for TIMIT prediction experiment

  1. Downsample the TIMIT dataset to 8ksamples/sec using Matlab by running downsample_audio.m from the matlab directory. Make sure you modify the paths in downsample_audio.m for your system.

  2. Download Matlab evaluation code using download_and_unzip_matlab_code.py, which should download and unzip all the required toolboxes to the matlab folder.

  3. Run the experiments using the shell scripts: run_timit_prediction_<model>.sh, which will train the model and score the resulting audio using the Matlab evaluation toolboxes.

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Code for paper "Full-Capacity Unitary Recurrent Neural Networks"

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