desh2608/icefall-asr-tedlium3-zipformer

TedLium3 Zipformer

rnnt_type=regular

The WERs are

	dev	test	comment
greedy search	6.74	6.16	--epoch 50, --avg 22, --max-duration 500
beam search (beam size 4)	6.56	5.95	--epoch 50, --avg 22, --max-duration 500
modified beam search (beam size 4)	6.54	6.00	--epoch 50, --avg 22, --max-duration 500
fast beam search (set as default)	6.91	6.28	--epoch 50, --avg 22, --max-duration 500

The training command for reproducing is given below:

export CUDA_VISIBLE_DEVICES="0,1,2,3"

./zipformer/train.py \
  --use-fp16 true \
  --world-size 4 \
  --num-epochs 50 \
  --start-epoch 0 \
  --exp-dir zipformer/exp \
  --max-duration 1000

The tensorboard training log can be found at https://tensorboard.dev/experiment/AKXbJha0S9aXyfmuvG4h5A/#scalars

The decoding command is:

epoch=50
avg=22

## greedy search
./zipformer/decode.py \
  --epoch $epoch \
  --avg $avg \
  --exp-dir zipformer/exp \
  --bpe-model ./data/lang_bpe_500/bpe.model \
  --max-duration 500

## beam search
./zipformer/decode.py \
  --epoch $epoch \
  --avg $avg \
  --exp-dir zipformer/exp \
  --bpe-model ./data/lang_bpe_500/bpe.model \
  --max-duration 500 \
  --decoding-method beam_search \
  --beam-size 4

## modified beam search
./zipformer/decode.py \
  --epoch $epoch \
  --avg $avg \
  --exp-dir zipformer/exp \
  --bpe-model ./data/lang_bpe_500/bpe.model \
  --max-duration 500 \
  --decoding-method modified_beam_search \
  --beam-size 4

## fast beam search
./zipformer/decode.py \
  --epoch $epoch \
  --avg $avg \
  --exp-dir ./zipformer/exp \
  --bpe-model ./data/lang_bpe_500/bpe.model \
  --max-duration 1500 \
  --decoding-method fast_beam_search \
  --beam 4 \
  --max-contexts 4 \
  --max-states 8

rnnt_type=modified

Using the codes from this PR https://github.com/k2-fsa/icefall/pull/1125.

The WERs are

	dev	test	comment
greedy search	6.32	5.83	--epoch 50, --avg 22, --max-duration 500
modified beam search (beam size 4)	6.16	5.79	--epoch 50, --avg 22, --max-duration 500
fast beam search (set as default)	6.30	5.89	--epoch 50, --avg 22, --max-duration 500

The training command for reproducing is given below:

export CUDA_VISIBLE_DEVICES="0,1,2,3"

./zipformer/train.py \
  --use-fp16 true \
  --world-size 4 \
  --num-epochs 50 \
  --start-epoch 0 \
  --exp-dir zipformer/exp \
  --max-duration 1000 \
  --rnnt-type modified

The tensorboard training log can be found at https://tensorboard.dev/experiment/3d4bYmbJTGiWQQaW88CVEQ/#scalars

The decoding commands are same as above.