icefall/egs/wenetspeech/ASR/RESULTS.md

Results

WenetSpeech char-based training results (Pruned Transducer 2)

2022-05-06

Using the codes from this PR https://github.com/k2-fsa/icefall/pull/349 and the Lhotse v1.1.

When training with the L subset, the WERs are

	dev	test-net	test-meeting	comment
greedy search	8.06	9.16	14.07	--epoch 6, --avg 3, --max-duration 100
modified beam search (beam size 4)	7.97	9.18	13.91	--epoch 6, --avg 3, --max-duration 100
fast beam search (set as default)	8.13	9.12	14.33	--epoch 6, --avg 3, --max-duration 1500

The training command for reproducing is given below:

export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"

./pruned_transducer_stateless2/train.py \
  --lang-dir data/lang_char \
  --exp-dir pruned_transducer_stateless2/exp \
  --world-size 8 \
  --num-epochs 10 \
  --start-epoch 0 \
  --max-duration 180 \
  --valid-interval 3000 \
  --model-warm-step 3000 \
  --save-every-n 8000 \
  --training-subset L

The tensorboard training log can be found at https://tensorboard.dev/experiment/VpA8b7SZQ7CEjZs9WZ5HNA/#scalars

The decoding command is:

epoch=6
avg=3

## greedy search
./pruned_transducer_stateless2/decode.py \
        --epoch 6 \
        --avg 3 \
        --exp-dir ./pruned_transducer_stateless2/exp \
        --lang-dir data/lang_char \
        --max-duration 100 \
        --decoding-method greedy_search

## modified beam search
./pruned_transducer_stateless2/decode.py \
        --epoch 6 \
        --avg 3 \
        --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 6 \
        --avg 3 \
        --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

When training with the M subset, the WERs are

	dev	test-net	test-meeting	comment
greedy search	10.40	11.31	19.64	--epoch 29, --avg 11, --max-duration 100
modified beam search (beam size 4)	9.85	11.04	18.20	--epoch 29, --avg 11, --max-duration 100
fast beam search (set as default)	10.18	11.10	19.32	--epoch 29, --avg 11, --max-duration 1500

When training with the S subset, the WERs are

	dev	test-net	test-meeting	comment
greedy search	19.92	25.20	35.35	--epoch 29, --avg 24, --max-duration 100
modified beam search (beam size 4)	18.62	23.88	33.80	--epoch 29, --avg 24, --max-duration 100
fast beam search (set as default)	19.31	24.41	34.87	--epoch 29, --avg 24, --max-duration 1500

A pre-trained model and decoding logs can be found at https://huggingface.co/luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless2