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Results

LibriSpeech BPE training results (Pruned Transducer 2)

pruned_transducer_stateless2 This is with a reworked version of the conformer encoder, with many changes.

Training on fulll librispeech

using commit 34aad74a2c849542dd5f6359c9e6b527e8782fd6. See https://github.com/k2-fsa/icefall/pull/288

The WERs are:

	test-clean	test-other	comment
greedy search (max sym per frame 1)	2.62	6.37	--epoch 25 --avg 8 --max-duration 600
fast beam search	2.61	6.17	--epoch 25 --avg 8 --max-duration 600 --decoding-method fast_beam_search
modified beam search	2.59	6.19	--epoch 25 --avg 8 --max-duration 600 --decoding-method modified_beam_search
greedy search (max sym per frame 1)	2.70	6.04	--epoch 34 --avg 10 --max-duration 600
fast beam search	2.66	6.00	--epoch 34 --avg 10 --max-duration 600 --decoding-method fast_beam_search
greedy search (max sym per frame 1)	2.62	6.03	--epoch 38 --avg 10 --max-duration 600
fast beam search	2.57	5.95	--epoch 38 --avg 10 --max-duration 600 --decoding-method fast_beam_search

The train and decode commands are: python3 ./pruned_transducer_stateless2/train.py --exp-dir=pruned_transducer_stateless2/exp --world-size 8 --num-epochs 26 --full-libri 1 --max-duration 300 and: python3 ./pruned_transducer_stateless2/decode.py --exp-dir pruned_transducer_stateless2/exp --epoch 25 --avg 8 --bpe-model ./data/lang_bpe_500/bpe.model --max-duration 600

The Tensorboard log is at https://tensorboard.dev/experiment/Xoz0oABMTWewo1slNFXkyA (apologies, log starts only from epoch 3).

Training on train-clean-100:

Trained with 1 job: python3 ./pruned_transducer_stateless2/train.py --exp-dir=pruned_transducer_stateless2/exp_100h_ws1 --world-size 1 --num-epochs 40 --full-libri 0 --max-duration 300 and decoded with: python3 ./pruned_transducer_stateless2/decode.py --exp-dir pruned_transducer_stateless2/exp_100h_ws1 --epoch 19 --avg 8 --bpe-model ./data/lang_bpe_500/bpe.model --max-duration 600.

The Tensorboard log is at https://tensorboard.dev/experiment/AhnhooUBRPqTnaggoqo7lg (learning rate schedule is not visible due to a since-fixed bug).

	test-clean	test-other	comment
greedy search (max sym per frame 1)	7.12	18.42	--epoch 19 --avg 8
greedy search (max sym per frame 1)	6.71	17.77	--epoch 29 --avg 8
greedy search (max sym per frame 1)	6.64	17.19	--epoch 39 --avg 10
fast beam search	6.58	17.27	--epoch 29 --avg 8 --decoding-method fast_beam_search
fast beam search	6.53	16.82	--epoch 39 --avg 10 --decoding-method fast_beam_search

Trained with 2 jobs: python3 ./pruned_transducer_stateless2/train.py --exp-dir=pruned_transducer_stateless2/exp_100h_ws2 --world-size 2 --num-epochs 40 --full-libri 0 --max-duration 300 and decoded with: python3 ./pruned_transducer_stateless2/decode.py --exp-dir pruned_transducer_stateless2/exp_100h_ws2 --epoch 19 --avg 8 --bpe-model ./data/lang_bpe_500/bpe.model --max-duration 600.

The Tensorboard log is at https://tensorboard.dev/experiment/dvOC9wsrSdWrAIdsebJILg/ (learning rate schedule is not visible due to a since-fixed bug).

	test-clean	test-other	comment
greedy search (max sym per frame 1)	7.05	18.77	--epoch 19 --avg 8
greedy search (max sym per frame 1)	6.82	18.14	--epoch 29 --avg 8
greedy search (max sym per frame 1)	6.81	17.66	--epoch 30 --avg 10

Trained with 4 jobs: python3 ./pruned_transducer_stateless2/train.py --exp-dir=pruned_transducer_stateless2/exp_100h_ws4 --world-size 4 --num-epochs 40 --full-libri 0 --max-duration 300 and decoded with: python3 ./pruned_transducer_stateless2/decode.py --exp-dir pruned_transducer_stateless2/exp_100h_ws4 --epoch 19 --avg 8 --bpe-model ./data/lang_bpe_500/bpe.model --max-duration 600.

The Tensorboard log is at https://tensorboard.dev/experiment/a3T0TyC0R5aLj5bmFbRErA/ (learning rate schedule is not visible due to a since-fixed bug).

	test-clean	test-other	comment
greedy search (max sym per frame 1)	7.31	19.55	--epoch 19 --avg 8
greedy search (max sym per frame 1)	7.08	18.59	--epoch 29 --avg 8
greedy search (max sym per frame 1)	6.86	18.29	--epoch 30 --avg 10

Trained with 1 job, with --use-fp16=True --max-duration=300 i.e. with half-precision floats (but without increasing max-duration), after merging https://github.com/k2-fsa/icefall/pull/305. Train command was python3 ./pruned_transducer_stateless2/train.py --exp-dir=pruned_transducer_stateless2/exp_100h_fp16 --world-size 1 --num-epochs 40 --full-libri 0 --max-duration 300 --use-fp16 True

The Tensorboard log is at https://tensorboard.dev/experiment/DAtGG9lpQJCROUDwPNxwpA

	test-clean	test-other	comment
greedy search (max sym per frame 1)	7.10	18.57	--epoch 19 --avg 8
greedy search (max sym per frame 1)	6.81	17.84	--epoch 29 --avg 8
greedy search (max sym per frame 1)	6.63	17.39	--epoch 30 --avg 10

Trained with 1 job, with --use-fp16=True --max-duration=500, i.e. with half-precision floats and max-duration increased from 300 to 500, after merging https://github.com/k2-fsa/icefall/pull/305. Train command was python3 ./pruned_transducer_stateless2/train.py --exp-dir=pruned_transducer_stateless2/exp_100h_fp16 --world-size 1 --num-epochs 40 --full-libri 0 --max-duration 500 --use-fp16 True

The Tensorboard log is at https://tensorboard.dev/experiment/Km7QBHYnSLWs4qQnAJWsaA

	test-clean	test-other	comment
greedy search (max sym per frame 1)	7.10	18.79	--epoch 19 --avg 8
greedy search (max sym per frame 1)	6.92	18.16	--epoch 29 --avg 8
greedy search (max sym per frame 1)	6.89	17.75	--epoch 30 --avg 10

LibriSpeech BPE training results (Pruned Transducer)

Conformer encoder + non-current decoder. The decoder contains only an embedding layer, a Conv1d (with kernel size 2) and a linear layer (to transform tensor dim).

2022-03-12

pruned_transducer_stateless

Using commit 1603744469d167d848e074f2ea98c587153205fa. See https://github.com/k2-fsa/icefall/pull/248

The WERs are:

	test-clean	test-other	comment
greedy search (max sym per frame 1)	2.62	6.37	--epoch 42 --avg 11 --max-duration 100
greedy search (max sym per frame 2)	2.62	6.37	--epoch 42 --avg 11 --max-duration 100
greedy search (max sym per frame 3)	2.62	6.37	--epoch 42 --avg 11 --max-duration 100
modified beam search (beam size 4)	2.56	6.27	--epoch 42 --avg 11 --max-duration 100
beam search (beam size 4)	2.57	6.27	--epoch 42 --avg 11 --max-duration 100

The decoding time for test-clean and test-other is given below: (A V100 GPU with 32 GB RAM is used for decoding. Note: Not all GPU RAM is used during decoding.)

decoding method	test-clean (seconds)	test-other (seconds)
greedy search (--max-sym-per-frame=1)	160	159
greedy search (--max-sym-per-frame=2)	184	177
greedy search (--max-sym-per-frame=3)	210	213
modified beam search (--beam-size 4)	273	269
beam search (--beam-size 4)	2741	2221

We recommend you to use modified_beam_search.

Training command:

cd egs/librispeech/ASR/
./prepare.sh

export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"

. path.sh

./pruned_transducer_stateless/train.py \
  --world-size 8 \
  --num-epochs 60 \
  --start-epoch 0 \
  --exp-dir pruned_transducer_stateless/exp \
  --full-libri 1 \
  --max-duration 300 \
  --prune-range 5 \
  --lr-factor 5 \
  --lm-scale 0.25

The tensorboard training log can be found at https://tensorboard.dev/experiment/WKRFY5fYSzaVBHahenpNlA/

The command for decoding is:

epoch=42
avg=11
sym=1

# greedy search

./pruned_transducer_stateless/decode.py \
  --epoch $epoch \
  --avg $avg \
  --exp-dir ./pruned_transducer_stateless/exp \
  --max-duration 100 \
  --decoding-method greedy_search \
  --beam-size 4 \
  --max-sym-per-frame $sym

# modified beam search
./pruned_transducer_stateless/decode.py \
  --epoch $epoch \
  --avg $avg \
  --exp-dir ./pruned_transducer_stateless/exp \
  --max-duration 100 \
  --decoding-method modified_beam_search \
  --beam-size 4

# beam search
# (not recommended)
./pruned_transducer_stateless/decode.py \
  --epoch $epoch \
  --avg $avg \
  --exp-dir ./pruned_transducer_stateless/exp \
  --max-duration 100 \
  --decoding-method beam_search \
  --beam-size 4

You can find a pre-trained model, decoding logs, and decoding results at https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless-2022-03-12

2022-02-18

pruned_transducer_stateless

The WERs are

	test-clean	test-other	comment
greedy search	2.85	6.98	--epoch 28 --avg 15 --max-duration 100

The training command for reproducing is given below:

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

./pruned_transducer_stateless/train.py \
  --world-size 4 \
  --num-epochs 30 \
  --start-epoch 0 \
  --exp-dir pruned_transducer_stateless/exp \
  --full-libri 1 \
  --max-duration 300 \
  --prune-range 5 \
  --lr-factor 5 \
  --lm-scale 0.25 \

The tensorboard training log can be found at https://tensorboard.dev/experiment/ejG7VpakRYePNNj6AbDEUw/#scalars

The decoding command is:

epoch=28
avg=15

## greedy search
./pruned_transducer_stateless/decode.py \
  --epoch $epoch \
  --avg $avg \
  --exp-dir pruned_transducer_stateless/exp \
  --max-duration 100

LibriSpeech BPE training results (Transducer)

Conformer encoder + embedding decoder

Conformer encoder + non-recurrent decoder. The decoder contains only an embedding layer and a Conv1d (with kernel size 2).

See

2022-03-01

Using commit 2332ba312d7ce72f08c7bac1e3312f7e3dd722dc.

It uses GigaSpeech as extra training data. 20% of the time it selects a batch from L subset of GigaSpeech and 80% of the time it selects a batch from LibriSpeech.

The WERs are

	test-clean	test-other	comment
greedy search (max sym per frame 1)	2.64	6.55	--epoch 39 --avg 15 --max-duration 100
modified beam search (beam size 4)	2.61	6.46	--epoch 39 --avg 15 --max-duration 100

The training command for reproducing is given below:

cd egs/librispeech/ASR/
./prepare.sh
./prepare_giga_speech.sh

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

./transducer_stateless_multi_datasets/train.py \
  --world-size 4 \
  --num-epochs 40 \
  --start-epoch 0 \
  --exp-dir transducer_stateless_multi_datasets/exp-full-2 \
  --full-libri 1 \
  --max-duration 300 \
  --lr-factor 5 \
  --bpe-model data/lang_bpe_500/bpe.model \
  --modified-transducer-prob 0.25 \
  --giga-prob 0.2

The tensorboard training log can be found at https://tensorboard.dev/experiment/xmo5oCgrRVelH9dCeOkYBg/

The decoding command is:

epoch=39
avg=15
sym=1

# greedy search
./transducer_stateless_multi_datasets/decode.py \
  --epoch $epoch \
  --avg $avg \
  --exp-dir transducer_stateless_multi_datasets/exp-full-2 \
  --bpe-model ./data/lang_bpe_500/bpe.model \
  --max-duration 100 \
  --context-size 2 \
  --max-sym-per-frame $sym

# modified beam search
./transducer_stateless_multi_datasets/decode.py \
  --epoch $epoch \
  --avg $avg \
  --exp-dir transducer_stateless_multi_datasets/exp-full-2 \
  --bpe-model ./data/lang_bpe_500/bpe.model \
  --max-duration 100 \
  --context-size 2 \
  --decoding-method modified_beam_search \
  --beam-size 4

You can find a pretrained model by visiting https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-multi-datasets-bpe-500-2022-03-01

2022-02-07

Using commit a8150021e01d34ecbd6198fe03a57eacf47a16f2.

The WERs are

	test-clean	test-other	comment
greedy search (max sym per frame 1)	2.67	6.67	--epoch 63 --avg 19 --max-duration 100
greedy search (max sym per frame 2)	2.67	6.67	--epoch 63 --avg 19 --max-duration 100
greedy search (max sym per frame 3)	2.67	6.67	--epoch 63 --avg 19 --max-duration 100
modified beam search (beam size 4)	2.67	6.57	--epoch 63 --avg 19 --max-duration 100

The training command for reproducing is given below:

cd egs/librispeech/ASR/
./prepare.sh
export CUDA_VISIBLE_DEVICES="0,1,2,3"
./transducer_stateless/train.py \
  --world-size 4 \
  --num-epochs 76 \
  --start-epoch 0 \
  --exp-dir transducer_stateless/exp-full \
  --full-libri 1 \
  --max-duration 300 \
  --lr-factor 5 \
  --bpe-model data/lang_bpe_500/bpe.model \
  --modified-transducer-prob 0.25

The tensorboard training log can be found at https://tensorboard.dev/experiment/qgvWkbF2R46FYA6ZMNmOjA/#scalars

The decoding command is:

epoch=63
avg=19

## greedy search
for sym in 1 2 3; do
  ./transducer_stateless/decode.py \
    --epoch $epoch \
    --avg $avg \
    --exp-dir transducer_stateless/exp-full \
    --bpe-model ./data/lang_bpe_500/bpe.model \
    --max-duration 100 \
    --max-sym-per-frame $sym
done

## modified beam search

./transducer_stateless/decode.py \
  --epoch $epoch \
  --avg $avg \
  --exp-dir transducer_stateless/exp-full \
  --bpe-model ./data/lang_bpe_500/bpe.model \
  --max-duration 100 \
  --context-size 2 \
  --decoding-method modified_beam_search \
  --beam-size 4

You can find a pretrained model by visiting https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2022-02-07

Conformer encoder + LSTM decoder

Using commit 8187d6236c2926500da5ee854f758e621df803cc.

Conformer encoder + LSTM decoder.

The best WER is

	test-clean	test-other
WER	3.07	7.51

using --epoch 34 --avg 11 with greedy search.

The training command to reproduce the above WER is:

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

./transducer/train.py \
  --world-size 4 \
  --num-epochs 35 \
  --start-epoch 0 \
  --exp-dir transducer/exp-lr-2.5-full \
  --full-libri 1 \
  --max-duration 180 \
  --lr-factor 2.5

The decoding command is:

epoch=34
avg=11

./transducer/decode.py \
  --epoch $epoch \
  --avg $avg \
  --exp-dir transducer/exp-lr-2.5-full \
  --bpe-model ./data/lang_bpe_500/bpe.model \
  --max-duration 100

You can find the tensorboard log at: https://tensorboard.dev/experiment/D7NQc3xqTpyVmWi5FnWjrA

LibriSpeech BPE training results (Conformer-CTC)

2021-11-09

The best WER, as of 2021-11-09, for the librispeech test dataset is below (using HLG decoding + n-gram LM rescoring + attention decoder rescoring):

	test-clean	test-other
WER	2.42	5.73

Scale values used in n-gram LM rescoring and attention rescoring for the best WERs are:

ngram_lm_scale	attention_scale
2.0	2.0

To reproduce the above result, use the following commands for training:

cd egs/librispeech/ASR/conformer_ctc
./prepare.sh
export CUDA_VISIBLE_DEVICES="0,1,2,3"
./conformer_ctc/train.py \
  --exp-dir conformer_ctc/exp_500_att0.8 \
  --lang-dir data/lang_bpe_500 \
  --att-rate 0.8 \
  --full-libri 1 \
  --max-duration 200 \
  --concatenate-cuts 0 \
  --world-size 4 \
  --bucketing-sampler 1 \
  --start-epoch 0 \
  --num-epochs 90
# Note: It trains for 90 epochs, but the best WER is at epoch-77.pt

and the following command for decoding

./conformer_ctc/decode.py \
  --exp-dir conformer_ctc/exp_500_att0.8 \
  --lang-dir data/lang_bpe_500 \
  --max-duration 30 \
  --concatenate-cuts 0 \
  --bucketing-sampler 1 \
  --num-paths 1000 \
  --epoch 77 \
  --avg 55 \
  --method attention-decoder \
  --nbest-scale 0.5

You can find the pre-trained model by visiting https://huggingface.co/csukuangfj/icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09

The tensorboard log for training is available at https://tensorboard.dev/experiment/hZDWrZfaSqOMqtW0NEfXKg/#scalars

2021-08-19

(Wei Kang): Result of https://github.com/k2-fsa/icefall/pull/13

TensorBoard log is available at https://tensorboard.dev/experiment/GnRzq8WWQW62dK4bklXBTg/#scalars

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

The best decoding results (WER) are listed below, we got this results by averaging models from epoch 15 to 34, and using attention-decoder decoder with num_paths equals to 100.

	test-clean	test-other
WER	2.57%	5.94%

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 WER above are also listed below.

	lm_scale	attention_scale
test-clean	1.3	1.2
test-other	1.2	1.1

You can use the following commands to reproduce our results:

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

# It was using ef233486, you may not need to switch to it
# git checkout ef233486

cd egs/librispeech/ASR
./prepare.sh

export CUDA_VISIBLE_DEVICES="0,1,2,3"
python conformer_ctc/train.py --bucketing-sampler True \
                              --concatenate-cuts False \
                              --max-duration 200 \
                              --full-libri True \
                              --world-size 4 \
                              --lang-dir data/lang_bpe_5000

python conformer_ctc/decode.py --nbest-scale 0.5 \
                               --epoch 34 \
                               --avg 20 \
                               --method attention-decoder \
                               --max-duration 20 \
                               --num-paths 100 \
                               --lang-dir data/lang_bpe_5000

LibriSpeech training results (Tdnn-Lstm)

2021-08-24

(Wei Kang): Result of phone based Tdnn-Lstm model.

Icefall version: caa0b9e942

Pretrained model is available at https://huggingface.co/pkufool/icefall_asr_librispeech_tdnn-lstm_ctc

The best decoding results (WER) are listed below, we got this results by averaging models from epoch 19 to 14, and using whole-lattice-rescoring decoding method.

	test-clean	test-other
WER	6.59%	17.69%

We searched the lm_score_scale for best results, the scales that produced the WER above are also listed below.

	lm_scale
test-clean	0.8
test-other	0.9

21 KiB Raw Blame History

Results

LibriSpeech BPE training results (Pruned Transducer 2)

Training on fulll librispeech

Training on train-clean-100:

LibriSpeech BPE training results (Pruned Transducer)

2022-03-12

2022-02-18

LibriSpeech BPE training results (Transducer)

Conformer encoder + embedding decoder

2022-03-01

2022-02-07

Conformer encoder + LSTM decoder

LibriSpeech BPE training results (Conformer-CTC)

2021-11-09

2021-08-19

LibriSpeech training results (Tdnn-Lstm)

2021-08-24

21 KiB

Raw Blame History