Create README.md

README.md

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+# Pre-trained VisualNet-CTC models for the GRID 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/visualnet_ctc_asr) for more details of this model.
+## How to use
+See (https://github.com/k2-fsa/icefall/blob/master/egs/grid/AVSR/visualnet_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 visualnet_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 16 to 25, the decoding method is `1best`.
+||TEST|
+|--|--|
+|WER|15.68%|