readme: # Pre-trained VisualNet2-CTC models for the GRID visual dataset with icefall.
  The model was trained on full [GRID](https://zenodo.org/record/3625687#.Ybn7HagzY2w) with the scripts in [icefall](https://github.com/k2-fsa/icefall).  
  See (https://github.com/k2-fsa/icefall/tree/master/egs/grid/AVSR/visualnet2_ctc_asr) for more details of this model.
  ## How to use
  See (https://github.com/k2-fsa/icefall/blob/master/egs/grid/AVSR/visualnet2_ctc_asr/Pre-trained.md)
  ## Training procedure
  The main repositories are list below, we will update the training and decoding scripts with the update of version.  
  k2: https://github.com/k2-fsa/k2 
  icefall: https://github.com/k2-fsa/icefall 
  * Install k2 and lhotse, k2 installation guide refers to https://k2.readthedocs.io/en/latest/installation/index.html, lhotse refers to https://lhotse.readthedocs.io/en/latest/getting-started.html#installation. I think the latest version would be ok. And please also install the requirements listed in icefall.
  * Clone icefall(https://github.com/k2-fsa/icefall) and check to the commit showed above.
  ```
  git clone https://github.com/k2-fsa/icefall
  cd icefall
  ```
  * Preparing data.
  ```
  cd egs/grid/AVSR
  bash ./prepare.sh
  ```
  * Training
  ```
  export CUDA_VISIBLE_DEVICES="0"
  python visualnet2_ctc_asr/train.py --world-size 1
  ```
  ## Evaluation results
  The best decoding results (WER) on GRID TEST are listed below, we got this result by averaging models from epoch 15 to 29, the decoding method is `1best`.
  ||TEST|
  |--|--|
  |WER|13.63%|