For complete introdution and usage, please see the original repository Alexander-H-Liu/End-to-end-ASR-Pytorch.

1. SpecAugment
2. Label Smoothing
3. VGG encoder with Layer Normalization
4. Learning rate scheduler
5. Stabilizing joint acoustic and language model beam decoding by threshold

Download librispeech datset first in OpenSLR website

Modify script/train.sh, script/train_lm.sh, config/librispeech_asr.yaml, and config/librispeech_lm.yaml first. GPU is required.

bash script/train.sh <asr name> <cuda id>
bash script/train_lm.sh <lm name> <cuda id>

Modify script/test.sh and config/librispeech_test.sh first. Increase the number of --njobs can speed up decoding process, but might cause OOM.

bash script/test.sh <asr name> <cuda id>

This baseline is composed of a character-based joint CTC-attention ASR model and an RNNLM which were trained on the LibriSpeech train-clean-100. The perplexity of the LM on the dev-clean set is 2.79.