Results

Aishell training result (Stateless Transducer)

Zipformer (Non-streaming)

It's reworked Zipformer with Pruned RNNT loss. Caution: It uses --context-size=1.

normal-scaled model, number of model parameters: 73412551, i.e., 73.41 M

	test	dev	comment
greedy search	4.67	4.37	--epoch 55 --avg 17
modified beam search	4.40	4.13	--epoch 55 --avg 17
fast beam search	4.60	4.31	--epoch 55 --avg 17

Command for training is:

./prepare.sh 

export CUDA_VISIBLE_DEVICES="0,1"

./zipformer/train.py \
  --world-size 2 \
  --num-epochs 60 \
  --start-epoch 1 \
  --use-fp16 1 \
  --context-size 1 \
  --enable-musan 0 \
  --exp-dir zipformer/exp \
  --max-duration 1000 \
  --enable-musan 0 \
  --base-lr 0.045 \
  --lr-batches 7500 \
  --lr-epochs 18 \
  --spec-aug-time-warp-factor 20

Command for decoding is:

for m in greedy_search modified_beam_search fast_beam_search ; do
  ./zipformer/decode.py \
    --epoch 55 \
    --avg 17 \
    --exp-dir ./zipformer/exp \
    --lang-dir data/lang_char \
    --context-size 1 \
    --decoding-method $m
done

Pretrained models, training logs, decoding logs, tensorboard and decoding results are available at https://huggingface.co/zrjin/icefall-asr-aishell-zipformer-2023-10-24

small-scaled model, number of model parameters: 30167139, i.e., 30.17 M

	test	dev	comment
greedy search	4.97	4.67	--epoch 55 --avg 21
modified beam search	4.67	4.40	--epoch 55 --avg 21
fast beam search	4.85	4.61	--epoch 55 --avg 21

Command for training is:

export CUDA_VISIBLE_DEVICES="0,1"

./zipformer/train.py \
  --world-size 2 \
  --num-epochs 60 \
  --start-epoch 1 \
  --use-fp16 1 \
  --context-size 1 \
  --exp-dir zipformer/exp-small \
  --enable-musan 0 \
  --base-lr 0.045 \
  --lr-batches 7500 \
  --lr-epochs 18 \
  --spec-aug-time-warp-factor 20 \
  --num-encoder-layers 2,2,2,2,2,2 \
  --feedforward-dim 512,768,768,768,768,768 \
  --encoder-dim 192,256,256,256,256,256 \
  --encoder-unmasked-dim 192,192,192,192,192,192 \
  --max-duration 1200

Command for decoding is:

for m in greedy_search modified_beam_search fast_beam_search ; do
  ./zipformer/decode.py \
    --epoch 55 \
    --avg 21 \
    --exp-dir ./zipformer/exp-small \
    --lang-dir data/lang_char \
    --context-size 1 \
    --decoding-method $m \
    --num-encoder-layers 2,2,2,2,2,2 \
    --feedforward-dim 512,768,768,768,768,768 \
    --encoder-dim 192,256,256,256,256,256 \
    --encoder-unmasked-dim 192,192,192,192,192,192
done

Pretrained models, training logs, decoding logs, tensorboard and decoding results are available at https://huggingface.co/zrjin/icefall-asr-aishell-zipformer-small-2023-10-24/

large-scaled model, number of model parameters: 157285130, i.e., 157.29 M

	test	dev	comment
greedy search	4.49	4.22	--epoch 56 --avg 23
modified beam search	4.28	4.03	--epoch 56 --avg 23
fast beam search	4.44	4.18	--epoch 56 --avg 23

Command for training is:

export CUDA_VISIBLE_DEVICES="0,1"

./zipformer/train.py \
  --world-size 2 \
  --num-epochs 60 \
  --use-fp16 1 \
  --context-size 1 \
  --exp-dir ./zipformer/exp-large \
  --enable-musan 0 \
  --lr-batches 7500 \
  --lr-epochs 18 \
  --spec-aug-time-warp-factor 20 \
  --num-encoder-layers 2,2,4,5,4,2 \
  --feedforward-dim 512,768,1536,2048,1536,768 \
  --encoder-dim 192,256,512,768,512,256 \
  --encoder-unmasked-dim 192,192,256,320,256,192 \
  --max-duration 800

Command for decoding is:

for m in greedy_search modified_beam_search fast_beam_search ; do
  ./zipformer/decode.py \
    --epoch 56 \
    --avg 23 \
    --exp-dir ./zipformer/exp-large \
    --lang-dir data/lang_char \
    --context-size 1 \
    --decoding-method $m \
    --num-encoder-layers 2,2,4,5,4,2 \
    --feedforward-dim 512,768,1536,2048,1536,768 \
    --encoder-dim 192,256,512,768,512,256 \
    --encoder-unmasked-dim 192,192,256,320,256,192 
done

Pretrained models, training logs, decoding logs, tensorboard and decoding results are available at https://huggingface.co/zrjin/icefall-asr-aishell-zipformer-large-2023-10-24/

Pruned transducer stateless 7 streaming

./pruned_transducer_stateless7_streaming

It's Streaming version of Zipformer1 with Pruned RNNT loss.

	test	dev	comment
greedy search	6.95	6.29	--epoch 44 --avg 15 --max-duration 600
modified beam search	6.51	5.90	--epoch 44 --avg 15 --max-duration 600
fast beam search	6.73	6.09	--epoch 44 --avg 15 --max-duration 600

Training command is:

./prepare.sh

export CUDA_VISIBLE_DEVICES="0,1"

./pruned_transducer_stateless7_streaming/train.py \
    --world-size 2 \
    --num-epochs 50 \
    --use-fp16 1 \
    --context-size 1 \
    --max-duration 800 \
    --exp-dir ./pruned_transducer_stateless7_streaming/exp \
    --enable-musan 0 \
    --spec-aug-time-warp-factor 20

Caution: It uses --context-size=1.

The decoding command is:

for m in greedy_search modified_beam_search fast_beam_search ; do
  ./pruned_transducer_stateless7_streaming/decode.py \
    --epoch 44 \
    --avg 15 \
    --exp-dir ./pruned_transducer_stateless7_streaming/exp \
    --lang-dir data/lang_char \
    --context-size 1 \
    --decoding-method $m
done

Pretrained models, training logs, decoding logs, tensorboard and decoding results are available at https://huggingface.co/zrjin/icefall-asr-aishell-zipformer-pruned-transducer-stateless7-streaming-2023-10-16/

Pruned transducer stateless 7

./pruned_transducer_stateless7

It's Zipformer with Pruned RNNT loss.

	test	dev	comment
greedy search	5.02	4.61	--epoch 42 --avg 6 --max-duration 600
modified beam search	4.81	4.4	--epoch 42 --avg 6 --max-duration 600
fast beam search	4.91	4.52	--epoch 42 --avg 6 --max-duration 600

Training command is:

./prepare.sh

export CUDA_VISIBLE_DEVICES="0,1"

./pruned_transducer_stateless7/train.py \
  --world-size 2 \
  --num-epochs 50 \
  --start-epoch 1 \
  --use-fp16 1 \
  --exp-dir pruned_transducer_stateless7/exp \
  --context-size 1 \
  --max-duration 300

Caution: It uses --context-size=1.

The tensorboard log is available at https://tensorboard.dev/experiment/MHYo3ApfQxaCdYLr38cQOQ

The decoding command is:

for m in greedy_search modified_beam_search fast_beam_search ; do
  ./pruned_transducer_stateless7/decode.py \
    --epoch 42 \
    --avg 6 \
    --exp-dir ./pruned_transducer_stateless7/exp \
    --lang-dir data/lang_char \
    --max-duration 300 \
    --context-size 1 \
    --decoding-method $m

done

Pretrained models, training logs, decoding logs, and decoding results are available at https://huggingface.co/marcoyang/icefall-asr-aishell-zipformer-pruned-transducer-stateless7-2023-03-21

Pruned transducer stateless 7 (zipformer)

See k2-fsa#986

./pruned_transducer_stateless7_bbpe

Note: The modeling units are byte level BPEs

The best results I have gotten are:

Vocab size	Greedy search(dev & test)	Modified beam search(dev & test)	Fast beam search (dev & test)	Fast beam search LG (dev & test)	comments
500	4.31 & 4.59	4.25 & 4.54	4.27 & 4.55	4.07 & 4.38	--epoch 48 --avg 29

The training command:

export CUDA_VISIBLE_DEVICES="4,5,6,7"

./pruned_transducer_stateless7_bbpe/train.py \
  --world-size 4 \
  --num-epochs 50 \
  --start-epoch 1 \
  --use-fp16 1 \
  --max-duration 800 \
  --bpe-model data/lang_bbpe_500/bbpe.model \
  --exp-dir pruned_transducer_stateless7_bbpe/exp \
  --lr-epochs 6 \
  --master-port 12535

The decoding command:

for m in greedy_search modified_beam_search fast_beam_search fast_beam_search_LG; do
    ./pruned_transducer_stateless7_bbpe/decode.py \
      --epoch 48 \
      --avg 29 \
      --exp-dir ./pruned_transducer_stateless7_bbpe/exp \
      --max-sym-per-frame 1 \
      --ngram-lm-scale 0.25 \
      --ilme-scale 0.2 \
      --bpe-model data/lang_bbpe_500/bbpe.model \
      --max-duration 2000 \
      --decoding-method $m
done

The pretrained model is available at: https://huggingface.co/pkufool/icefall_asr_aishell_pruned_transducer_stateless7_bbpe

Pruned transducer stateless 3

See k2-fsa#436

./pruned_transducer_stateless3

It uses pruned RNN-T.

	test	dev	comment
greedy search	5.39	5.09	--epoch 29 --avg 5 --max-duration 600
modified beam search	5.05	4.79	--epoch 29 --avg 5 --max-duration 600
modified beam search + RNNLM shallow fusion	4.73	4.53	--epoch 29 --avg 5 --max-duration 600
modified beam search + LODR	4.57	4.37	--epoch 29 --avg 5 --max-duration 600
fast beam search	5.13	4.91	--epoch 29 --avg 5 --max-duration 600

Training command is:

./prepare.sh
./prepare_aidatatang_200zh.sh

export CUDA_VISIBLE_DEVICES="4,5,6,7"

./pruned_transducer_stateless3/train.py \
  --exp-dir ./pruned_transducer_stateless3/exp-context-size-1 \
  --world-size 4 \
  --max-duration 200 \
  --datatang-prob 0.5 \
  --start-epoch 1 \
  --num-epochs 30 \
  --use-fp16 1 \
  --num-encoder-layers 12 \
  --dim-feedforward 2048 \
  --nhead 8 \
  --encoder-dim 512 \
  --context-size 1 \
  --decoder-dim 512 \
  --joiner-dim 512 \
  --master-port 12356

Caution: It uses --context-size=1.

The tensorboard log is available at https://tensorboard.dev/experiment/OKKacljwR6ik7rbDr5gMqQ

The decoding command is:

for epoch in 29; do
  for avg in 5; do
    for m in greedy_search modified_beam_search fast_beam_search; do
      ./pruned_transducer_stateless3/decode.py \
        --exp-dir ./pruned_transducer_stateless3/exp-context-size-1 \
        --epoch $epoch \
        --avg $avg \
        --use-averaged-model 1 \
        --max-duration 600 \
        --decoding-method $m \
        --num-encoder-layers 12 \
        --dim-feedforward 2048 \
        --nhead 8 \
        --context-size 1 \
        --encoder-dim 512 \
        --decoder-dim 512 \
        --joiner-dim 512
    done
  done
done

We provide the option of shallow fusion with a RNN language model. The pre-trained language model is available at https://huggingface.co/marcoyang/icefall-aishell-rnn-lm. To decode with the language model, please use the following command:

# download pre-trained model
git lfs install
git clone https://huggingface.co/csukuangfj/icefall-aishell-pruned-transducer-stateless3-2022-06-20

aishell_exp=icefall-aishell-pruned-transducer-stateless3-2022-06-20/

pushd ${aishell_exp}/exp
ln -s pretrained-epoch-29-avg-5-torch-1.10.0.pt epoch-99.pt
popd

# download RNN LM
git lfs install
git clone https://huggingface.co/marcoyang/icefall-aishell-rnn-lm
rnnlm_dir=icefall-aishell-rnn-lm

# RNNLM shallow fusion
for lm_scale in $(seq 0.26 0.02 0.34); do
  python ./pruned_transducer_stateless3/decode.py \
      --epoch 99 \
      --avg 1 \
      --lang-dir ${aishell_exp}/data/lang_char \
      --exp-dir ${aishell_exp}/exp \
      --use-averaged-model False \
      --decoding-method modified_beam_search_lm_shallow_fusion \
      --use-shallow-fusion 1 \
      --lm-type rnn \
      --lm-exp-dir ${rnnlm_dir}/exp \
      --lm-epoch 99 \
      --lm-scale $lm_scale \
      --lm-avg 1 \
      --rnn-lm-embedding-dim 2048 \
      --rnn-lm-hidden-dim 2048 \
      --rnn-lm-num-layers 2 \
      --lm-vocab-size 4336
done

# RNNLM Low-order density ratio (LODR) with a 2-gram

cp ${rnnlm_dir}/2gram.fst.txt ${aishell_exp}/data/lang_char/2gram.fst.txt

for lm_scale in 0.48; do
  for LODR_scale in -0.28; do
    python ./pruned_transducer_stateless3/decode.py \
        --epoch 99 \
        --avg 1 \
        --lang-dir ${aishell_exp}/data/lang_char \
        --exp-dir ${aishell_exp}/exp \
        --use-averaged-model False \
        --decoding-method modified_beam_search_LODR \
        --use-shallow-fusion 1 \
        --lm-type rnn \
        --lm-exp-dir ${rnnlm_dir}/exp \
        --lm-epoch 99 \
        --lm-scale $lm_scale \
        --lm-avg 1 \
        --rnn-lm-embedding-dim 2048 \
        --rnn-lm-hidden-dim 2048 \
        --rnn-lm-num-layers 2 \
        --lm-vocab-size 4336 \
        --tokens-ngram 2 \
        --backoff-id 4336 \
        --ngram-lm-scale $LODR_scale
  done
done

Pretrained models, training logs, decoding logs, and decoding results are available at https://huggingface.co/csukuangfj/icefall-aishell-pruned-transducer-stateless3-2022-06-20

We have a tutorial in sherpa about how to use the pre-trained model for non-streaming ASR. See https://k2-fsa.github.io/sherpa/offline_asr/conformer/aishell.html

Pruned transducer stateless 2

See k2-fsa#536

./pruned_transducer_stateless2

It uses pruned RNN-T.

	test	dev	comment
greedy search	5.20	4.78	--epoch 72 --avg 14 --max-duration 200
modified beam search	5.07	4.63	--epoch 72 --avg 14 --max-duration 200
fast beam search	5.13	4.70	--epoch 72 --avg 14 --max-duration 200

Training command is:

./prepare.sh

export CUDA_VISIBLE_DEVICES="0,1"

./pruned_transducer_stateless2/train.py \
        --world-size 2 \
        --num-epochs 90 \
        --start-epoch 0 \
        --exp-dir pruned_transducer_stateless2/exp \
        --max-duration 200 \

The tensorboard log is available at https://tensorboard.dev/experiment/QI3PVzrGRrebxpbWUPwmkA/

The decoding command is:

for m in greedy_search modified_beam_search fast_beam_search ; do
  ./pruned_transducer_stateless2/decode.py \
    --epoch 72 \
    --avg 14 \
    --exp-dir ./pruned_transducer_stateless2/exp \
    --lang-dir data/lang_char \
    --max-duration 200 \
    --decoding-method $m

done

Pretrained models, training logs, decoding logs, and decoding results are available at https://huggingface.co/teapoly/icefall-aishell-pruned-transducer-stateless2-2022-08-18

2022-03-01

./transducer_stateless_modified-2

It uses optimized_transducer for computing RNN-T loss.

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 k2-fsa#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 k2-fsa#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 k2-fsa#10 (comment) 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, k2-fsa#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%

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RESULTS.md

RESULTS.md

Results

Aishell training result (Stateless Transducer)

Zipformer (Non-streaming)

normal-scaled model, number of model parameters: 73412551, i.e., 73.41 M

small-scaled model, number of model parameters: 30167139, i.e., 30.17 M

large-scaled model, number of model parameters: 157285130, i.e., 157.29 M

Pruned transducer stateless 7 streaming

Pruned transducer stateless 7

Pruned transducer stateless 7 (zipformer)

Pruned transducer stateless 3

Pruned transducer stateless 2

2022-03-01

2022-03-01

2022-2-19

Aishell training results (Transducer-stateless)

2022-02-18

Aishell training results (Conformer-MMI)

2021-12-04

Aishell training results (Conformer-CTC)

2021-11-16

Aishell training results (Tdnn-Lstm)

2021-09-13

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RESULTS.md

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RESULTS.md

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Results

Aishell training result (Stateless Transducer)

Zipformer (Non-streaming)

normal-scaled model, number of model parameters: 73412551, i.e., 73.41 M

small-scaled model, number of model parameters: 30167139, i.e., 30.17 M

large-scaled model, number of model parameters: 157285130, i.e., 157.29 M

Pruned transducer stateless 7 streaming

Pruned transducer stateless 7

Pruned transducer stateless 7 (zipformer)

Pruned transducer stateless 3

Pruned transducer stateless 2

2022-03-01

2022-03-01

2022-2-19

Aishell training results (Transducer-stateless)

2022-02-18

Aishell training results (Conformer-MMI)

2021-12-04

Aishell training results (Conformer-CTC)

2021-11-16

Aishell training results (Tdnn-Lstm)

2021-09-13