Autosubs is a library for Automatic Speech Recognition using a modified version of Listen-Attend-Spell. The biggest changes are the addition of three bi-directional LSTM layers to the beginning of the network, dropout between all LSTM layers, and the addition of scaling to the dot-product attention mechanism. The WSJ training process entailed pretraining the decoder for ~15 epochs, and then training hundreds of epochs for between 24-48 hours on 1 Tesla V100.

The full conda environment is exported in autosubs_env.yml You can install this running: conda env create -f autosubs_env.yml

• Set your configuration in src/config.yaml
  • All hyperparameters and general execution parameters are exposed here for tuning.
• Training is started by running: python runner.py
• Inference is started by running: python inference.py

• Datasets belong in the data folder
• For KNNW, these include:
  • knnw-720p.tar.gz
  • knnw_en_mono.wav
  • knnw_en_sub.srt
  • knnw_en.log_spectrogram.npy
  • knnw_en_sub.csv

• Models can be trained/evaluated from checkpoint by placing weights in the checkpoints directory, and setting config.yaml's checkpoint path to the weights file.