readme: Note: This recipe is trained with the codes from this PR https://github.com/k2-fsa/icefall/pull/355
  And the SpecAugment codes from this PR https://github.com/lhotse-speech/lhotse/pull/604.
  # Pre-trained Transducer-Stateless2 models for the Aidatatang_200zh dataset with icefall.
  The model was trained on full [Aidatatang_200zh](https://www.openslr.org/62) with the scripts in [icefall](https://github.com/k2-fsa/icefall) based on the latest version k2.  
  ## 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 
  lhotse: https://github.com/lhotse-speech/lhotse
  * 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/aidatatang_200zh/ASR
  bash ./prepare.sh
  ```
  * Training
  ```
  export CUDA_VISIBLE_DEVICES="0,1"
  ./pruned_transducer_stateless2/train.py \
                    --world-size 2 \
                    --num-epochs 30 \
                    --start-epoch 0 \
                    --exp-dir pruned_transducer_stateless2/exp \
                    --lang-dir data/lang_char \
                    --max-duration 250
  ```
  ## Evaluation results
  The decoding results (WER%) on Aidatatang_200zh(dev and test) are listed below, we got this result by averaging models from epoch 11 to 29.
  The WERs are
  |                                    |     dev    |    test    | comment                                  |
  |------------------------------------|------------|------------|------------------------------------------|
  |          greedy search             | 5.53       | 6.59       | --epoch 29, --avg 19, --max-duration 100 |
  | modified beam search (beam size 4) | 5.28       | 6.32       | --epoch 29, --avg 19, --max-duration 100 |
  | fast beam search (set as default)  | 5.29       | 6.33       | --epoch 29, --avg 19, --max-duration 1500|
  