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

Patrick Doetsch, Albert Zeyer, Paul Voigtlaender, Ilya Kulikov, Ralf Schlüter, Hermann Ney . 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2017 – 71 citations

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Evaluation ICASSP Interdisciplinary Approaches Model Architecture Neural Machine Translation Tools Training Techniques Variational Autoencoders

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.

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