## Results ### Aishell4 Char training results (Pruned Transducer Stateless5) #### 2022-06-13 Using the codes from this PR https://github.com/k2-fsa/icefall/pull/399. When use-averaged-model=False, the CERs are | | test | comment | |------------------------------------|------------|------------------------------------------| | greedy search | 30.05 | --epoch 30, --avg 25, --max-duration 800 | | modified beam search (beam size 4) | 29.16 | --epoch 30, --avg 25, --max-duration 800 | | fast beam search (set as default) | 29.20 | --epoch 30, --avg 25, --max-duration 1500| When use-averaged-model=True, the CERs are | | test | comment | |------------------------------------|------------|----------------------------------------------------------------------| | greedy search | 29.89 | --iter 36000, --avg 8, --max-duration 800 --use-averaged-model=True | | modified beam search (beam size 4) | 28.91 | --iter 36000, --avg 8, --max-duration 800 --use-averaged-model=True | | fast beam search (set as default) | 29.08 | --iter 36000, --avg 8, --max-duration 1500 --use-averaged-model=True | The training command for reproducing is given below: ``` export CUDA_VISIBLE_DEVICES="0,1,2,3" ./pruned_transducer_stateless5/train.py \ --world-size 4 \ --num-epochs 30 \ --start-epoch 1 \ --exp-dir pruned_transducer_stateless5/exp \ --lang-dir data/lang_char \ --max-duration 220 \ --save-every-n 4000 ``` The tensorboard training log can be found at https://tensorboard.dev/experiment/tjaVRKERS8C10SzhpBcxSQ/#scalars When use-averaged-model=False, the decoding command is: ``` epoch=30 avg=25 ## greedy search ./pruned_transducer_stateless5/decode.py \ --epoch $epoch \ --avg $avg \ --exp-dir pruned_transducer_stateless5/exp \ --lang-dir ./data/lang_char \ --max-duration 800 ## modified beam search ./pruned_transducer_stateless5/decode.py \ --epoch $epoch \ --avg $avg \ --exp-dir pruned_transducer_stateless5/exp \ --lang-dir ./data/lang_char \ --max-duration 800 \ --decoding-method modified_beam_search \ --beam-size 4 ## fast beam search ./pruned_transducer_stateless5/decode.py \ --epoch $epoch \ --avg $avg \ --exp-dir ./pruned_transducer_stateless5/exp \ --lang-dir ./data/lang_char \ --max-duration 1500 \ --decoding-method fast_beam_search \ --beam 4 \ --max-contexts 4 \ --max-states 8 ``` When use-averaged-model=True, the decoding command is: ``` iter=36000 avg=8 ## greedy search ./pruned_transducer_stateless5/decode.py \ --epoch $epoch \ --avg $avg \ --exp-dir pruned_transducer_stateless5/exp \ --lang-dir ./data/lang_char \ --max-duration 800 \ --use-averaged-model True ## modified beam search ./pruned_transducer_stateless5/decode.py \ --epoch $epoch \ --avg $avg \ --exp-dir pruned_transducer_stateless5/exp \ --lang-dir ./data/lang_char \ --max-duration 800 \ --decoding-method modified_beam_search \ --beam-size 4 \ --use-averaged-model True ## fast beam search ./pruned_transducer_stateless5/decode.py \ --epoch $epoch \ --avg $avg \ --exp-dir ./pruned_transducer_stateless5/exp \ --lang-dir ./data/lang_char \ --max-duration 1500 \ --decoding-method fast_beam_search \ --beam 4 \ --max-contexts 4 \ --max-states 8 \ --use-averaged-model True ``` A pre-trained model and decoding logs can be found at