readme: # Pre-trained TDNN-LSTM-CTC models for the TIMIT dataset with icefall.
  The model was trained on full [TIMIT](https://data.deepai.org/timit.zip) with the scripts in [icefall](https://github.com/k2-fsa/icefall).  
  See (https://github.com/k2-fsa/icefall/tree/master/egs/timit/ASR/tdnn_lstm_ctc) for more details of this model.
  ## How to use
  See (https://github.com/k2-fsa/icefall/blob/master/egs/timit/ASR/tdnn_lstm_ctc/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 
  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/timit/ASR
  bash ./prepare.sh
  ```
  * Training
  ```
  export CUDA_VISIBLE_DEVICES="0"
  python tdnn_lstm_ctc/train.py --bucketing-sampler True \
                                  --concatenate-cuts False \
                                  --max-duration 200 \
                                  --world-size 1
  ```
  ## Evaluation results
  The best decoding results (PER, equals to WER) on TIMIT TEST are listed below, we got this result by averaging models from epoch 16 to 25, the lm_scale is 0.08, the decoding method is `whole-lattice-rescoring`.
  ||TEST|
  |--|--|
  |PER|19.71%|