## Results ### Aidatatang_200zh Char training results (Pruned Transducer Stateless2) #### 2022-05-16 Using the codes from this PR https://github.com/k2-fsa/icefall/pull/375. The WERs are | | dev | test | comment | |------------------------------------|------------|------------|------------------------------------------| | greedy search | 5.53 | 6.59 | --epoch 29, --avg 19, --max-duration 100 | | modified beam search (beam size 4) | 5.27 | 6.33 | --epoch 29, --avg 19, --max-duration 100 | | fast beam search (set as default) | 5.30 | 6.34 | --epoch 29, --avg 19, --max-duration 1500| The training command for reproducing is given below: ``` export CUDA_VISIBLE_DEVICES="0,1" ./pruned_transducer_stateless2/train.py \ --world-size 2 \ --num-epochs 30 \ --start-epoch 0 \ --exp-dir pruned_transducer_stateless2/exp \ --lang-dir data/lang_char \ --max-duration 250 \ --save-every-n 1000 ``` The tensorboard training log can be found at https://tensorboard.dev/experiment/xS7kgYf2RwyDpQAOdS8rAA/#scalars The decoding command is: ``` epoch=29 avg=19 ## greedy search ./pruned_transducer_stateless2/decode.py \ --epoch $epoch \ --avg $avg \ --exp-dir pruned_transducer_stateless2/exp \ --lang-dir ./data/lang_char \ --max-duration 100 ## modified beam search ./pruned_transducer_stateless2/decode.py \ --epoch $epoch \ --avg $avg \ --exp-dir pruned_transducer_stateless2/exp \ --lang-dir ./data/lang_char \ --max-duration 100 \ --decoding-method modified_beam_search \ --beam-size 4 ## fast beam search ./pruned_transducer_stateless2/decode.py \ --epoch $epoch \ --avg $avg \ --exp-dir ./pruned_transducer_stateless2/exp \ --lang-dir ./data/lang_char \ --max-duration 1500 \ --decoding-method fast_beam_search \ --beam 4 \ --max-contexts 4 \ --max-states 8 ``` A pre-trained model and decoding logs can be found at