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asr-repr-analysis
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# Analyzing ASR Representations This repository contains code for our paper on analyzing speech representations in end-to-end automatic speech recognition models: "Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems", Yonatan Belinkov and James Glass, NIPS 2017. ## Requirements * [lmdb](https://github.com/eladhoffer/lmdb.torch) * [tds](https://github.com/torch/tds) * [nngraph](https://github.com/torch/nngraph) ## Instructions 1. First prepare a dataset in LMDB format according to the instructions in [deepspeech.torch](https://github.com/SeanNaren/deepspeech.torch/wiki/Data-Preparation-And-Running). We provide a custom `MakeLMDBTimes.lua` file to process a dataset with time segmentation such as TIMIT. 1. Run `train.lua` with the following arguments: * `loadPath`: DeepSpeech-2 model trained with [deepspeech.torch](https://github.com/SeanNaren/deepspeech.torch) * `trainingSetLMDBPath`, `validationSetLMDBPath`, `testSetLMDBPath`: top folders for the LMDB training/validation/test sets * `reprLayer`: representation layer name (input, cnn1, cnn2, rnn1, rnn2, etc.) * `predFile`: file to save predictions See `train.lua` for more options, such as controlling convolution strides, using a window of features around the frame or predicting phone classes. ## Citing If you use this code, please consider citing our paper: ```bib @InProceedings{belinkov:2017:nips, author = {Belinkov, Yonatan and Glass, James}, title = {Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems}, booktitle = {Advances in Neural Information Processing Systems (NIPS)}, month = {December}, year = {2017} } ``` ### Acknowledgements This project uses code from [deepspeech.torch](https://github.com/SeanNaren/deepspeech.torch).
