icefall/egs/aishell2/ASR/RESULTS.md

Results

Aishell2 char-based training results (Pruned Transducer 5)

2022-07-11

Using the codes from this commit https://github.com/k2-fsa/icefall/pull/461.

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

	dev-ios	test-ios	comment
greedy search	5.57	5.89	--epoch 10, --avg 2, --max-duration 100
modified beam search (beam size 4)	5.32	5.56	--epoch 10, --avg 2, --max-duration 100
fast beam search (set as default)	5.5	5.78	--epoch 10, --avg 2, --max-duration 1500

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

The decoding command is:

for method in greedy_search modified_beam_search fast_beam_search; do
  ./pruned_transducer_stateless5/decode.py \
    --epoch 25 \
    --avg 5 \
    --exp-dir /result \
    --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 \
    --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

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