KO
|
EN
gitlite — search
Search
#javascript
#python
#hacktoberfest
#react
#ai
#typescript
#llm
#go
#golang
#android
#machine-learning
#rust
#deep-learning
#linux
asr-repr-analysis
★ 15
Open GitHub ↗
No description available.
Download README (.md)
Explore Similar Repositories
defer-render-hoc
:
defer expensive react renders until the next two rAF's
OldLinuxSrc
:
Linux0.01 0.11 and 0.12 source code
Asynchronous
:
Implementation-agnostic asynchronous code
rtl-sdr
:
RTL-SDR *very* experimental branch. Modded R820T/R820T2/R860 driver. Nohw-mod
BaiduMapSpider
:
百度地图POI数据抓取
// repository documentation
Was this content helpful?
★ 0
(0 ratings)
Select Rating:
★
★
★
★
★
Submit Feedback
Recent Feedback
×
Download README
Do you want to download the
README.md
file for
asr-repr-analysis
?
Download (.md)
# 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).
