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Description:

returnn 1.20240908.14935

GitHub repository.
RETURNN paper 2016,
RETURNN paper 2018.
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:

Simplicity

Writing config / code is simple & straight-forward (setting up experiment, defining model)
Debugging in case of problems is simple
Reading config / code is simple (defined model, training, decoding all becomes clear)

Flexibility

Allow for many different kinds of experiments / models

Efficiency

Training speed
Decoding speed

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:

Mini-batch training of feed-forward neural networks
Sequence-chunking based batch training for recurrent neural networks
Long short-term memory recurrent neural networks
including our own fast CUDA kernel
Multidimensional LSTM (GPU only, there is no CPU version)
Memory management for large data sets
Work distribution across multiple devices
Flexible and fast architecture which allows all kinds of encoder-attention-decoder models

See documentation.
See basic usage
and technological overview.
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.
There is also a wiki.
Questions can also be asked on
StackOverflow using the RETURNN tag.