<<<<<<< HEAD Note: This recipe is trained with the codes from this PR https://github.com/k2-fsa/icefall/pull/261 And the SpecAugment codes from this PR https://github.com/lhotse-speech/lhotse/pull/604. # Pre-trained Transducer-Stateless models for the TEDLium3 dataset with icefall. The model was trained on full [TEDLium3](https://www.openslr.org/51) with the scripts in [icefall](https://github.com/k2-fsa/icefall). ## 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/tedlium3/ASR bash ./prepare.sh ``` * Training ``` export CUDA_VISIBLE_DEVICES="0,1,2,3" ./pruned_transducer_stateless/train.py \ --world-size 4 \ --num-epochs 30 \ --start-epoch 0 \ --exp-dir pruned_transducer_stateless/exp \ --max-duration 300 ``` ## Evaluation results The decoding results (WER%) on TEDLium3 (dev and test) are listed below, we got this result by averaging models from epoch 17 to 29. The WERs are | | dev | test | comment | |------------------------------------|------------|------------|------------------------------------------| | greedy search | 7.27 | 6.69 | --epoch 29, --avg 13, --max-duration 100 | | beam search (beam size 4) | 6.70 | 6.04 | --epoch 29, --avg 13, --max-duration 100 | | modified beam search (beam size 4) | 6.77 | 6.14 | --epoch 29, --avg 13, --max-duration 100 |