Update README.md

README.md

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-**Pre-trained Tdnn-Lstm-CTC model for timit with icefall**
+# 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%|