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Results

Pruned transducer stateless 7 (zipformer)

See k2-fsa#1033

./pruned_transducer_stateless7_bbpe

Note: The modeling units are byte level BPEs

The best results I have gotten are:

Vocab size greedy (dev & test) modified beam search (dev & test)
500 6.88 & 6.98 6.87 & 6.94 --epoch 35 --avg 26

The training command:

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

./pruned_transducer_stateless7_bbpe/train.py \
  --world-size 4 \
  --start-epoch 1 \
  --num-epochs 35 \
  --use-fp16 1 \
  --max-duration 800 \
  --bbpe-model data/lang_bbpe_500/bbpe.model \
  --exp-dir pruned_transducer_stateless7_bbpe/exp \
  --master-port 12535

The decoding command:

 ./pruned_transducer_stateless7_bbpe/decode.py \
   --epoch 35 \
   --avg 26 \
   --exp-dir ./pruned_transducer_stateless7_bbpe/exp \
   --max-sym-per-frame 1 \
   --bpe-model data/lang_bbpe_500/bbpe.model \
   --max-duration 2000 \
   --decoding-method greedy_search  # modified_beam_search

The pretrained model is available at: https://huggingface.co/pkufool/icefall_asr_tal_csasr_pruned_transducer_stateless7_bbpe

TAL_CSASR Mix Chars and BPEs training results (Pruned Transducer Stateless5)

2022-06-22

Using the codes from this PR k2-fsa#428.

The WERs are

decoding-method epoch(iter) avg dev test
greedy_search 30 24 7.49 7.58
modified_beam_search 30 24 7.33 7.38
fast_beam_search 30 24 7.32 7.42
greedy_search(use-averaged-model=True) 30 24 7.30 7.39
modified_beam_search(use-averaged-model=True) 30 24 7.15 7.22
fast_beam_search(use-averaged-model=True) 30 24 7.18 7.27
greedy_search 348000 30 7.46 7.54
modified_beam_search 348000 30 7.24 7.36
fast_beam_search 348000 30 7.25 7.39

The results (CER(%) and WER(%)) for Chinese CER and English WER respectivly (zh: Chinese, en: English):

decoding-method epoch(iter) avg dev dev_zh dev_en test test_zh test_en
greedy_search(use-averaged-model=True) 30 24 7.30 6.48 19.19 7.39 6.66 19.13
modified_beam_search(use-averaged-model=True) 30 24 7.15 6.35 18.95 7.22 6.50 18.70
fast_beam_search(use-averaged-model=True) 30 24 7.18 6.39 18.90 7.27 6.55 18.77

The training command for reproducing is given below:

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

./pruned_transducer_stateless5/train.py \
  --world-size 6 \
  --num-epochs 30 \
  --start-epoch 1 \
  --exp-dir pruned_transducer_stateless5/exp \
  --lang-dir data/lang_char \
  --max-duration 90

The tensorboard training log can be found at https://tensorboard.dev/experiment/KaACzXOVR0OM6cy0qbN5hw/#scalars

The decoding command is:

epoch=30
avg=24
use_average_model=True

## 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 $use_average_model

## 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 $use_average_model

## 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 $use_average_model

A pre-trained model and decoding logs can be found at https://huggingface.co/luomingshuang/icefall_asr_tal-csasr_pruned_transducer_stateless5