| # 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%| |