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# Pre-trained TDNN-LSTM-CTC models for the TIMIT dataset with icefall. |
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The model was trained on full [TIMIT](https://data.deepai.org/timit.zip) with the scripts in [icefall](https://github.com/k2-fsa/icefall). |
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See (https://github.com/k2-fsa/icefall/tree/master/egs/timit/ASR/tdnn_lstm_ctc) for more details of this model. |
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## How to use |
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See (https://github.com/k2-fsa/icefall/blob/master/egs/timit/ASR/tdnn_lstm_ctc/Pre-trained.md) |
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## Training procedure |
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The main repositories are list below, we will update the training and decoding scripts with the update of version. |
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k2: https://github.com/k2-fsa/k2 |
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icefall: https://github.com/k2-fsa/icefall |
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lhotse: https://github.com/lhotse-speech/lhotse |
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* 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. |
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* Clone icefall(https://github.com/k2-fsa/icefall) and check to the commit showed above. |
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``` |
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git clone https://github.com/k2-fsa/icefall |
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cd icefall |
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``` |
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* Preparing data. |
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``` |
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cd egs/timit/ASR |
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bash ./prepare.sh |
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``` |
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* Training |
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``` |
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export CUDA_VISIBLE_DEVICES="0" |
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python tdnn_lstm_ctc/train.py --bucketing-sampler True \ |
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--concatenate-cuts False \ |
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--max-duration 200 \ |
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--world-size 1 |
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``` |
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## Evaluation results |
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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`. |
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||TEST| |
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|--|--| |
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|PER|19.71%| |