tensorflow_end2end_speech_recognition：基于TensorFlow的端到端语音识别实现（CTC...

## TensorFlow Implementation of End-to-End Speech Recognition ### Requirements - TensorFlow >= 1.3.0 - tqdm >= 4.14.0 - python-Levenshtein >= 0.12.0 - setproctitle >= 1.1.10 - seaborn >= 0.7.1 ### Corpus #### [TIMIT](https://catalog.ldc.upenn.edu/LDC93S1) - Phone (39, 48, 61 phones) - character #### [LibriSpeech](http://www.openslr.org/12/) - Phone (under implementation) - Character - Word #### [CSJ (Corpus of Spontaneous Japanese)](http://pj.ninjal.ac.jp/corpus_center/csj/en/) - Phone (under implementation) - Japanese kana character (about 150 classes) - Japanese kanji characters (about 3000 classes) These corpuses will be added in the future. - Switchboard - WSJ - [AMI](http://groups.inf.ed.ac.uk/ami/corpus/) This repository does'nt include pre-processing and pre-processing is based on [this repo](https://github.com/hirofumi0810/asr_preprocessing). If you want to do pre-processing, please look at this repo. ### Model #### Encoder - BLSTM - LSTM - BGRU - GRU - VGG-BLSTM - VGG-LSTM - Multi-task BLSTM - you can set another CTC layer to the aubitrary layer. - Multi-task LSTM - VGG #### Connectionist Temporal Classification (CTC) [\[Graves+ 2006\]](http://dl.acm.org/citation.cfm?id=1143891) - Greedy decoder - Beam Search decoder - Beam Search decoder w/ CharLM (under implementation) ##### Options - Frame-stacking [\[Sak+ 2015\]](https://arxiv.org/abs/1507.06947) - Multi-GPUs training (synchronous) - Splicing - Down sampling (under implementation) #### Attention Mechanism ##### Decoder - Greedy decoder - Beam search decoder (under implementation) ##### Attention type - Bahdanau's content-based attention - Bahdanau's normed content-based attention (under implementation) - location-based attention - Hybrid attention - Luong's dot attention - Luong's scaled dot attention (under implementation) - Luong's general attention - Luong's concat attention - Baidu's attention (under implementation) ###### Options - Sharpning - Temperature regularization in the softmax layer (Output posteriors) - Joint CTC-Attention [\[Kim 2016\]](https://arxiv.org/abs/1609.06773.) - Coverage (under implementation) ### Usage Please refer to docs in each corpuse - TIMIT - LibriSpeech - CSJ ### Lisense MIT ### Contact [email protected]