RETURNN: The RWTH Extensible Training Framework for Universal Recurrent Neural Networks

Patrick Doetsch, Albert Zeyer, Paul Voigtlaender, Ilia Kulikov, Ralf Schl├╝ter, Hermann Ney
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), New Orleans, USA, March 2017

In this work we release our extensible and easily configurable neural network training software. It provides a rich set of functional layers with a particular focus on efficient training of recurrent neural network topologies on multiple GPUs. The source of the software package is public and freely available for academic research purposes and can be used as a framework or as a standalone tool which supports a flexible configuration. The software allows to train state-of-the-art deep bidirectional long short-term memory (LSTM) models on both one dimensional data like speech or two dimensional data like handwritten text and was used to develop successful submission systems in several evaluation campaigns.

» Show BibTeX
@inproceedings{doetsch2017returnn, title={RETURNN: the RWTH extensible training framework for universal recurrent neural networks}, author={Doetsch, Patrick and Zeyer, Albert and Voigtlaender, Paul and Kulikov, Ilya and Schl{\"u}ter, Ralf and Ney, Hermann}, booktitle={IEEE International Conference on Acoustics, Speech, and Signal Processing}, year={2017}, month=mar, pages={5345--5349} }

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