Using the codes from this PR k2-fsa#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 https://huggingface.co/luomingshuang/icefall_asr_aidatatang-200zh_pruned_transducer_stateless2