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Results

TedLium3 BPE training results (Zipformer)

2023-06-15 (Regular transducer)

Using the codes from this PR k2-fsa#1125.

Number of model parameters: 65549011, i.e., 65.5 M

The WERs are

dev test comment
greedy search 6.74 6.16 --epoch 50, --avg 22, --max-duration 500
beam search (beam size 4) 6.56 5.95 --epoch 50, --avg 22, --max-duration 500
modified beam search (beam size 4) 6.54 6.00 --epoch 50, --avg 22, --max-duration 500
fast beam search (set as default) 6.91 6.28 --epoch 50, --avg 22, --max-duration 500

The training command for reproducing is given below:

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

./zipformer/train.py \
  --use-fp16 true \
  --world-size 4 \
  --num-epochs 50 \
  --start-epoch 0 \
  --exp-dir zipformer/exp \
  --max-duration 1000

The tensorboard training log can be found at https://tensorboard.dev/experiment/AKXbJha0S9aXyfmuvG4h5A/#scalars

The decoding command is:

epoch=50
avg=22

## greedy search
./zipformer/decode.py \
  --epoch $epoch \
  --avg $avg \
  --exp-dir zipformer/exp \
  --bpe-model ./data/lang_bpe_500/bpe.model \
  --max-duration 500

## beam search
./zipformer/decode.py \
  --epoch $epoch \
  --avg $avg \
  --exp-dir zipformer/exp \
  --bpe-model ./data/lang_bpe_500/bpe.model \
  --max-duration 500 \
  --decoding-method beam_search \
  --beam-size 4

## modified beam search
./zipformer/decode.py \
  --epoch $epoch \
  --avg $avg \
  --exp-dir zipformer/exp \
  --bpe-model ./data/lang_bpe_500/bpe.model \
  --max-duration 500 \
  --decoding-method modified_beam_search \
  --beam-size 4

## fast beam search
./zipformer/decode.py \
  --epoch $epoch \
  --avg $avg \
  --exp-dir ./zipformer/exp \
  --bpe-model ./data/lang_bpe_500/bpe.model \
  --max-duration 1500 \
  --decoding-method fast_beam_search \
  --beam 4 \
  --max-contexts 4 \
  --max-states 8

A pre-trained model and decoding logs can be found at https://huggingface.co/desh2608/icefall-asr-tedlium3-zipformer

2023-06-26 (Modified transducer)

./zipformer/train.py \
  --use-fp16 true \
  --world-size 4 \
  --num-epochs 50 \
  --start-epoch 0 \
  --exp-dir zipformer/exp \
  --max-duration 1000 \
  --rnnt-type modified

The tensorboard training log can be found at https://tensorboard.dev/experiment/3d4bYmbJTGiWQQaW88CVEQ/#scalars

dev test comment
greedy search 6.32 5.83 --epoch 50, --avg 22, --max-duration 500
modified beam search (beam size 4) 6.16 5.79 --epoch 50, --avg 22, --max-duration 500
fast beam search (set as default) 6.30 5.89 --epoch 50, --avg 22, --max-duration 500

A pre-trained model and decoding logs can be found at https://huggingface.co/desh2608/icefall-asr-tedlium3-zipformer.

TedLium3 BPE training results (Conformer-CTC 2)

conformer_ctc2

See k2-fsa#696 for more details.

The tensorboard log can be found at https://tensorboard.dev/experiment/5NQQiqOqSqazfn4w2yeWEQ/

You can find a pretrained model and decoding results at: https://huggingface.co/videodanchik/icefall-asr-tedlium3-conformer-ctc2

Number of model parameters: 101141699, i.e., 101.14 M

The WERs are

dev test comment
ctc decoding 6.45 5.96 --epoch 38 --avg 26
1best 5.92 5.51 --epoch 38 --avg 26
whole lattice rescoring 5.96 5.47 --epoch 38 --avg 26
attention decoder 5.60 5.33 --epoch 38 --avg 26

The training command for reproducing is given below:

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

./conformer_ctc2/train.py \
    --world-size 4 \
    --num-epochs 40 \
    --exp-dir conformer_ctc2/exp \
    --max-duration 350 \
    --use-fp16 true

The decoding command is:

epoch=38
avg=26

## ctc decoding
./conformer_ctc2/decode.py \
  --method ctc-decoding \
  --exp-dir conformer_ctc2/exp \
  --lang-dir data/lang_bpe_500 \
  --result-dir conformer_ctc2/exp \
  --max-duration 500 \
  --epoch $epoch \
  --avg $avg

## 1best
./conformer_ctc2/decode.py \
  --method 1best \
  --exp-dir conformer_ctc2/exp \
  --lang-dir data/lang_bpe_500 \
  --result-dir conformer_ctc2/exp \
  --max-duration 500 \
  --epoch $epoch \
  --avg $avg

## whole lattice rescoring
./conformer_ctc2/decode.py \
  --method whole-lattice-rescoring \
  --exp-dir conformer_ctc2/exp \
  --lm-path data/lm/G_4_gram_big.pt \
  --lang-dir data/lang_bpe_500 \
  --result-dir conformer_ctc2/exp \
  --max-duration 500 \
  --epoch $epoch \
  --avg $avg

## attention decoder
./conformer_ctc2/decode.py \
  --method attention-decoder \
  --exp-dir conformer_ctc2/exp \
  --lang-dir data/lang_bpe_500 \
  --result-dir conformer_ctc2/exp \
  --max-duration 500 \
  --epoch $epoch \
  --avg $avg

TedLium3 BPE training results (Pruned Transducer)

2022-03-21

Using the codes from this PR k2-fsa#261.

The WERs are

dev test comment
greedy search 7.27 6.69 --epoch 29, --avg 13, --max-duration 100
beam search (beam size 4) 6.70 6.04 --epoch 29, --avg 13, --max-duration 100
modified beam search (beam size 4) 6.77 6.14 --epoch 29, --avg 13, --max-duration 100
fast beam search (set as default) 7.14 6.50 --epoch 29, --avg 13, --max-duration 1500

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 \
  --max-duration 300

The tensorboard training log can be found at https://tensorboard.dev/experiment/VpA8b7SZQ7CEjZs9WZ5HNA/#scalars

The decoding command is:

epoch=29
avg=13

## greedy search
./pruned_transducer_stateless/decode.py \
  --epoch $epoch \
  --avg $avg \
  --exp-dir pruned_transducer_stateless/exp \
  --bpe-model ./data/lang_bpe_500/bpe.model \
  --max-duration 100

## beam search
./pruned_transducer_stateless/decode.py \
  --epoch $epoch \
  --avg $avg \
  --exp-dir pruned_transducer_stateless/exp \
  --bpe-model ./data/lang_bpe_500/bpe.model \
  --max-duration 100 \
  --decoding-method beam_search \
  --beam-size 4

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

## fast beam search
./pruned_transducer_stateless/decode.py \
        --epoch $epoch \
        --avg $avg \
        --exp-dir ./pruned_transducer_stateless/exp \
        --bpe-model ./data/lang_bpe_500/bpe.model \
        --max-duration 1500 \
        --decoding-method fast_beam_search \
        --beam 4 \
        --max-contexts 4 \
        --max-states 8

A pre-trained model and decoding logs can be found at https://huggingface.co/luomingshuang/icefall_asr_tedlium3_pruned_transducer_stateless

TedLium3 BPE training results (Transducer)

Conformer encoder + embedding decoder

2022-03-21

Using the codes from this PR k2-fsa#233 And the SpecAugment codes from this PR lhotse-speech/lhotse#604

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

The WERs are

dev test comment
greedy search 7.19 6.70 --epoch 29, --avg 11, --max-duration 100
beam search (beam size 4) 7.02 6.36 --epoch 29, --avg 11, --max-duration 100
modified beam search (beam size 4) 6.91 6.33 --epoch 29, --avg 11, --max-duration 100

The training command for reproducing is given below:

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

./transducer_stateless/train.py \
  --world-size 4 \
  --num-epochs 30 \
  --start-epoch 0 \
  --exp-dir transducer_stateless/exp \
  --max-duration 300

The tensorboard training log can be found at https://tensorboard.dev/experiment/4ks15jYHR4uMyvpW7Nz76Q/#scalars

The decoding command is:

epoch=29
avg=11

## greedy search
./transducer_stateless/decode.py \
  --epoch $epoch \
  --avg $avg \
  --exp-dir transducer_stateless/exp \
  --bpe-model ./data/lang_bpe_500/bpe.model \
  --max-duration 100

## beam search
./transducer_stateless/decode.py \
  --epoch $epoch \
  --avg $avg \
  --exp-dir transducer_stateless/exp \
  --bpe-model ./data/lang_bpe_500/bpe.model \
  --max-duration 100 \
  --decoding-method beam_search \
  --beam-size 4

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

A pre-trained model and decoding logs can be found at https://huggingface.co/luomingshuang/icefall_asr_tedlium3_transducer_stateless