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Results

Aishell2 char-based training results

Pruned transducer stateless 5

Using the codes from this commit k2-fsa#465.

When training with context size equals to 1, the WERs are

dev-ios test-ios comment
greedy search 5.57 5.89 --epoch 25, --avg 5, --max-duration 600
modified beam search (beam size 4) 5.32 5.56 --epoch 25, --avg 5, --max-duration 600
fast beam search (set as default) 5.5 5.78 --epoch 25, --avg 5, --max-duration 600
fast beam search nbest 5.46 5.74 --epoch 25, --avg 5, --max-duration 600
fast beam search oracle 1.92 2.2 --epoch 25, --avg 5, --max-duration 600
fast beam search nbest LG 5.59 5.93 --epoch 25, --avg 5, --max-duration 600

The training command for reproducing is given below:

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

./pruned_transducer_stateless5/train.py \
  --world-size 4 \
  --lang-dir data/lang_char \
  --num-epochs 40 \
  --start-epoch 1 \
  --exp-dir /result \
  --max-duration 300 \
  --use-fp16 0 \
  --num-encoder-layers 24 \
  --dim-feedforward 1536 \
  --nhead 8 \
  --encoder-dim 384 \
  --decoder-dim 512 \
  --joiner-dim 512 \
  --context-size 1

The decoding command is:

for method in greedy_search modified_beam_search fast_beam_search fast_beam_search_nbest  fast_beam_search_nbest_oracle fast_beam_search_nbest_LG; do
  ./pruned_transducer_stateless5/decode.py \
    --epoch 25 \
    --avg 5 \
    --exp-dir ./pruned_transducer_stateless5/exp \
    --max-duration 600 \
    --decoding-method $method \
    --max-sym-per-frame 1 \
    --num-encoder-layers 24 \
    --dim-feedforward 1536 \
    --nhead 8 \
    --encoder-dim 384 \
    --decoder-dim 512 \
    --joiner-dim 512 \
    --context-size 1 \
    --beam 20.0 \
    --max-contexts 8 \
    --max-states 64 \
    --num-paths 200 \
    --nbest-scale 0.5 \
    --context-size 1 \
    --use-averaged-model True
done

The tensorboard training log can be found at https://tensorboard.dev/experiment/RXyX4QjQQVKjBS2eQ2Qajg/#scalars

A pre-trained model and decoding logs can be found at https://huggingface.co/yuekai/icefall-asr-aishell2-pruned-transducer-stateless5-B-2022-07-12

When training with context size equals to 2, the WERs are

dev-ios test-ios comment
greedy search 5.47 5.81 --epoch 25, --avg 5, --max-duration 600
modified beam search (beam size 4) 5.38 5.61 --epoch 25, --avg 5, --max-duration 600
fast beam search (set as default) 5.36 5.61 --epoch 25, --avg 5, --max-duration 600
fast beam search nbest 5.37 5.6 --epoch 25, --avg 5, --max-duration 600
fast beam search oracle 2.04 2.2 --epoch 25, --avg 5, --max-duration 600
fast beam search nbest LG 5.59 5.82 --epoch 25, --avg 5, --max-duration 600

The tensorboard training log can be found at https://tensorboard.dev/experiment/5AxJ8LHoSre8kDAuLp4L7Q/#scalars

A pre-trained model and decoding logs can be found at https://huggingface.co/yuekai/icefall-asr-aishell2-pruned-transducer-stateless5-A-2022-07-12