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README.md
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Note: This recipe is trained with the codes from this PR https://github.com/k2-fsa/icefall/pull/261
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And the SpecAugment codes from this PR https://github.com/lhotse-speech/lhotse/pull/604.
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# Pre-trained Transducer-Stateless models for the TEDLium3 dataset with icefall.
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The model was trained on full [TEDLium3](https://www.openslr.org/51) with the scripts in [icefall](https://github.com/k2-fsa/icefall).
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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/tedlium3/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="0,1,2,3"
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./pruned_transducer_stateless/train.py \
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--world-size 4 \
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--num-epochs 30 \
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--start-epoch 0 \
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--exp-dir pruned_transducer_stateless/exp \
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--max-duration 300
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```
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## Evaluation results
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The decoding results (WER%) on TEDLium3 (dev and test) are listed below, we got this result by averaging models from epoch 17 to 29.
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The WERs are
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| | dev | test | comment |
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|------------------------------------|------------|------------|------------------------------------------|
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| greedy search | 7.27 | 6.69 | --epoch 29, --avg 13, --max-duration 100 |
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| beam search (beam size 4) | 6.70 | 6.04 | --epoch 29, --avg 13, --max-duration 100 |
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| modified beam search (beam size 4) | 6.72 | 6.12 | --epoch 29, --avg 13, --max-duration 100 |
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