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icefall/egs/aishell/ASR/RESULTS.md
Fangjun Kuang 50d2281524
Add modified transducer loss for AIShell dataset (#219) 
* Add modified transducer for aishell.

* Minor fixes.

* Add extra data in transducer training.

The extra data is from http://www.openslr.org/62/

* Update export.py and pretrained.py

* Update CI to install pretrained models with aishell.

* Update results.

* Update results.

* Update README.

* Use symlinks to avoid copies.
2022-03-02 16:02:38 +08:00

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Results

Aishell training result(Transducer-stateless)

2022-03-01

./transducer_stateless_modified-2

Stateless transducer + modified transducer + using aidatatang_200zh as extra training data.

test comment
greedy search 4.94 --epoch 89, --avg 38, --max-duration 100, --max-sym-per-frame 1
modified beam search 4.68 --epoch 89, --avg 38, --max-duration 100 --beam-size 4

The training commands are:

cd egs/aishell/ASR
./prepare.sh --stop-stage 6
./prepare_aidatatang_200zh.sh

export CUDA_VISIBLE_DEVICES="0,1,2"

./transducer_stateless_modified-2/train.py \
  --world-size 3 \
  --num-epochs 90 \
  --start-epoch 0 \
  --exp-dir transducer_stateless_modified-2/exp-2 \
  --max-duration 250 \
  --lr-factor 2.0 \
  --context-size 2 \
  --modified-transducer-prob 0.25 \
  --datatang-prob 0.2

The tensorboard log is available at https://tensorboard.dev/experiment/oG72ZlWaSGua6fXkcGRRjA/

The commands for decoding are

# greedy search
for epoch in 89; do
  for avg in 38; do
  ./transducer_stateless_modified-2/decode.py \
    --epoch $epoch \
    --avg $avg \
    --exp-dir transducer_stateless_modified-2/exp-2 \
    --max-duration 100 \
    --context-size 2 \
    --decoding-method greedy_search \
    --max-sym-per-frame 1
  done
done

# modified beam search
for epoch in 89; do
  for avg in 38; do
    ./transducer_stateless_modified-2/decode.py \
    --epoch $epoch \
    --avg $avg \
    --exp-dir transducer_stateless_modified-2/exp-2 \
    --max-duration 100 \
    --context-size 2 \
    --decoding-method modified_beam_search \
    --beam-size 4
  done
done

You can find a pre-trained model, decoding logs, and decoding results at https://huggingface.co/csukuangfj/icefall-aishell-transducer-stateless-modified-2-2022-03-01

2022-03-01

./transducer_stateless_modified

Stateless transducer + modified transducer.

test comment
greedy search 5.22 --epoch 64, --avg 33, --max-duration 100, --max-sym-per-frame 1
modified beam search 5.02 --epoch 64, --avg 33, --max-duration 100 --beam-size 4

The training commands are:

cd egs/aishell/ASR
./prepare.sh --stop-stage 6

export CUDA_VISIBLE_DEVICES="0,1,2"

./transducer_stateless_modified/train.py \
  --world-size 3 \
  --num-epochs 90 \
  --start-epoch 0 \
  --exp-dir transducer_stateless_modified/exp-4 \
  --max-duration 250 \
  --lr-factor 2.0 \
  --context-size 2 \
  --modified-transducer-prob 0.25

The tensorboard log is available at https://tensorboard.dev/experiment/C27M8YxRQCa1t2XglTqlWg/

The commands for decoding are

# greedy search
for epoch in 64; do
  for avg in 33; do
  ./transducer_stateless_modified/decode.py \
    --epoch $epoch \
    --avg $avg \
    --exp-dir transducer_stateless_modified/exp-4 \
    --max-duration 100 \
    --context-size 2 \
    --decoding-method greedy_search \
    --max-sym-per-frame 1
  done
done

# modified beam search
for epoch in 64; do
  for avg in 33; do
    ./transducer_stateless_modified/decode.py \
    --epoch $epoch \
    --avg $avg \
    --exp-dir transducer_stateless_modified/exp-4 \
    --max-duration 100 \
    --context-size 2 \
    --decoding-method modified_beam_search \
    --beam-size 4
  done
done

You can find a pre-trained model, decoding logs, and decoding results at https://huggingface.co/csukuangfj/icefall-aishell-transducer-stateless-modified-2022-03-01

2022-2-19

(Duo Ma): The tensorboard log for training is available at https://tensorboard.dev/experiment/25PmX3MxSVGTdvIdhOwllw/#scalars You can find a pretrained model by visiting https://huggingface.co/shuanguanma/icefall_aishell_transducer_stateless_context_size2_epoch60_2022_2_19

test comment
greedy search 5.4 --epoch 59, --avg 10, --max-duration 100
beam search 5.05 --epoch 59, --avg 10, --max-duration 100

You can use the following commands to reproduce our results:

export CUDA_VISIBLE_DEVICES="0,1,2,3"
python3 ./transducer_stateless/train.py \
      --world-size 4 \
      --num-epochs 60 \
      --start-epoch 0 \
      --exp-dir exp/transducer_stateless_context_size2 \
      --max-duration 100 \
      --lr-factor 2.5 \
      --context-size 2

lang_dir=data/lang_char
dir=exp/transducer_stateless_context_size2
python3 ./transducer_stateless/decode.py \
       --epoch 59 \
       --avg 10 \
       --exp-dir $dir \
       --lang-dir $lang_dir \
       --decoding-method greedy_search \
       --context-size 2 \
       --max-sym-per-frame 3

lang_dir=data/lang_char
dir=exp/transducer_stateless_context_size2
python3 ./transducer_stateless/decode.py \
       --epoch 59 \
       --avg 10 \
       --exp-dir $dir \
       --lang-dir $lang_dir \
       --decoding-method beam_search \
       --context-size 2 \
       --max-sym-per-frame 3

Aishell training results (Transducer-stateless)

2022-02-18

(Pingfeng Luo) : The tensorboard log for training is available at https://tensorboard.dev/experiment/k3QL6QMhRbCwCKYKM9po9w/ And pretrained model is available at https://huggingface.co/pfluo/icefall-aishell-transducer-stateless-char-2021-12-29

test
CER 5.05%

You can use the following commands to reproduce our results:

export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7,8"
./transducer_stateless/train.py \
  --bucketing-sampler True \
  --world-size 8 \
  --lang-dir data/lang_char \
  --num-epochs 60 \
  --start-epoch 0 \
  --exp-dir transducer_stateless/exp_rnnt_k2 \
  --max-duration 80 \
  --lr-factor 3

./transducer_stateless/decode.py \
  --epoch 59 \
  --avg 10 \
  --lang-dir data/lang_char \
  --exp-dir transducer_stateless/exp_rnnt_k2 \
  --max-duration 100 \
  --decoding-method beam_search \
  --beam-size 4

Aishell training results (Conformer-MMI)

2021-12-04

(Pingfeng Luo): Result of https://github.com/k2-fsa/icefall/pull/140

The tensorboard log for training is available at https://tensorboard.dev/experiment/PSRYVbptRGynqpPRSykp1g

And pretrained model is available at https://huggingface.co/pfluo/icefall_aishell_mmi_model

The best decoding results (CER) are listed below, we got this results by averaging models from epoch 61 to 85, and using attention-decoder decoder with num_paths equals to 100.

test
CER 4.94%
lm_scale attention_scale
test 1.1 0.3

You can use the following commands to reproduce our results:

git clone https://github.com/k2-fsa/icefall
cd icefall

cd egs/aishell/ASR
./prepare.sh

export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7,8"
python conformer_mmi/train.py --bucketing-sampler True \
                              --max-duration 200 \
                              --start-epoch 0 \
                              --num-epochs 90 \
                              --world-size 8

python conformer_mmi/decode.py --nbest-scale 0.5 \
                               --epoch 85 \
                               --avg 25 \
                               --method attention-decoder \
                               --max-duration 20 \
                               --num-paths 100

Aishell training results (Conformer-CTC)

2021-11-16

(Wei Kang): Result of https://github.com/k2-fsa/icefall/pull/30

Pretrained model is available at https://huggingface.co/pkufool/icefall_asr_aishell_conformer_ctc

The best decoding results (CER) are listed below, we got this results by averaging models from epoch 60 to 84, and using attention-decoder decoder with num_paths equals to 100.

test
CER 4.26%

To get more unique paths, we scaled the lattice.scores with 0.5 (see https://github.com/k2-fsa/icefall/pull/10#discussion_r690951662 for more details), we searched the lm_score_scale and attention_score_scale for best results, the scales that produced the CER above are also listed below.

lm_scale attention_scale
test 0.3 0.9

You can use the following commands to reproduce our results:

git clone https://github.com/k2-fsa/icefall
cd icefall

cd egs/aishell/ASR
./prepare.sh

export CUDA_VISIBLE_DEVICES="0,1,2,3"
python conformer_ctc/train.py --bucketing-sampler True \
                              --max-duration 200 \
                              --start-epoch 0 \
                              --num-epochs 90 \
                              --world-size 4

python conformer_ctc/decode.py --nbest-scale 0.5 \
                               --epoch 84 \
                               --avg 25 \
                               --method attention-decoder \
                               --max-duration 20 \
                               --num-paths 100

Aishell training results (Tdnn-Lstm)

2021-09-13

(Wei Kang): Result of phone based Tdnn-Lstm model, https://github.com/k2-fsa/icefall/pull/30

Pretrained model is available at https://huggingface.co/pkufool/icefall_asr_aishell_conformer_ctc_lstm_ctc

The best decoding results (CER) are listed below, we got this results by averaging models from epoch 19 to 8, and using 1best decoding method.

test
CER 10.16%