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End-to-End Attention-Based Large Vocabulary Speech Recognition
| Date | Stars |
|---|---|
| 2026-07-31 | 265 |
| 2026-08-02 | 265 |
| 2026-08-06 | 265 |
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# Attention-based Speech Recognizer The reference implementation for the papers End-to-End Attention-based Large Vocabulary Speech Recognition. _Dzmitry Bahdanau, Jan Chorowski, Dmitriy Serdyuk, Philemon Brakel, Yoshua Bengio_ ([arxiv draft](http://arxiv.org/pdf/1508.04395), ICASSP 2016) and Task Loss Estimation for Sequence Prediction. _Dzmitry Bahdanau, Dmitriy Serdyuk, Philémon Brakel, Nan Rosemary Ke, Jan Chorowski, Aaron Courville, Yoshua Bengio_ ([arxiv draft](http://arxiv.org/pdf/1511.06456), submitted to ICLR 2016). # This code is no longer maintained This codebase is based on outdated techonologies (Theano, Blocks, etc) and is no longer maintained. We recommend you to look for more modern speech recognition implemenations (see e.g. https://github.com/Alexander-H-Liu/End-to-end-ASR-Pytorch). ### How to use - install all the dependencies (see the list below) - set your environment variables by calling `source env.sh` Then, please proceed to [`exp/wsj`](exp/wsj/README.md) for the instructions how to replicate our results on Wall Street Journal (WSJ) dataset (available at the Linguistic Data Consortium as LDC93S6B and LDC94S13B). ### Dependencies - Python packages: pykwalify, toposort, pyyaml, numpy, pandas, pyfst, picklable-itertools; - [kaldi](https://github.com/kaldi-asr/kaldi); - [kaldi-python](https://github.com/dmitriy-serdyuk/kaldi-python). Given that you have the dataset in HDF5 format, the models can be trained without Kaldi and PyFst. ### Installation - Compile Kaldi. It should be compiled with `--shared` option, it means that Kaldi should be configured like ``` ./configure --shared ``` we need Kaldi to be compiled in shared mode to be able to use kaldi-python. We don't train anything with Kaldi, so there is no need to compile it with cuda, so if you have any problems with Kaldi+CUDA, feel free to turn it off: ``` ./configure --shared --use-cuda=no ``` After this step you should have openfst installed at `$KALDI_ROOT/tools/openfst`. - Install python packages. You can use pip for that: ``` pip install pykwalify toposort pyyaml numpy pandas pyfst ``` - Install kaldi-python. Clone the repository and run ``` python setup.py install ``` kaldi-python will be compiled and installed to your system, you can check that everything went right by running ``` python -c "import kaldi_io" ``` ### Subtrees The repository contains custom modified versions of Theano, Blocks, Fuel, picklable-itertools, Blocks-extras as [subtrees] (http://blogs.atlassian.com/2013/05/alternatives-to-git-submodule-git-subtree/). In order to ensure that these specific versions are used, we recommend to **uninstall regular installations of these packages if you have them installed** in addition to sourcing `env.sh`. ### License MIT
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