Skip to content

Latest commit

 

History

History
117 lines (98 loc) · 3.71 KB

RESULTS.md

File metadata and controls

117 lines (98 loc) · 3.71 KB
Raw

Results

Aishell4 Char training results (Pruned Transducer Stateless5)

2022-06-13

Using the codes from this PR k2-fsa#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 https://huggingface.co/luomingshuang/icefall_asr_aishell4_pruned_transducer_stateless5