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

• Phone (39, 48, 61 phones)
• character

LibriSpeech

• Phone (under implementation)
• Character
• Word

CSJ (Corpus of Spontaneous Japanese)

• 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

This repository does'nt include pre-processing and pre-processing is based on this repo. 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]

• Greedy decoder
• Beam Search decoder
• Beam Search decoder w/ CharLM (under implementation)

Options

• Frame-stacking [Sak+ 2015]
• 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]
• Coverage (under implementation)

Usage

Please refer to docs in each corpuse

• TIMIT
• LibriSpeech
• CSJ

Lisense

MIT

Contact

[email protected]