RETURNN - RWTH extensible training framework for universal recurrent neural networks, is a Theano/TensorFlow-based implementation of modern recurrent neural network architectures. It is optimized for fast and reliable training of recurrent neural networks in a multi-GPU environment.
The high-level features and goals of RETURNN are:
All items are important for research, decoding speed is esp. important for production.
See our Interspeech 2020 tutorial "Efficient and Flexible Implementation of Machine Learning for ASR and MT" video (slides) with an introduction of the core concepts.
More specific features include:
Here is the video recording of a RETURNN overview talk (slides, exercise sheet; hosted by eBay).
There are many example demos which work on artificially generated data, i.e. they should work as-is.
There are some real-world examples such as setups for speech recognition on the Switchboard or LibriSpeech corpus.
Some benchmark setups against other frameworks can be found here. The results are in the RETURNN paper 2016. Performance benchmarks of our LSTM kernel vs CuDNN and other TensorFlow kernels are in TensorFlow LSTM benchmark.