mirror of
https://github.com/k2-fsa/icefall.git
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342 lines
10 KiB
Markdown
342 lines
10 KiB
Markdown
## Results
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### TedLium3 BPE training results (Zipformer)
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#### 2023-06-15 (Regular transducer)
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Using the codes from this PR https://github.com/k2-fsa/icefall/pull/1125.
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Number of model parameters: 65549011, i.e., 65.5 M
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The WERs are
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| | dev | test | comment |
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|------------------------------------|------------|------------|------------------------------------------|
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| greedy search | 6.74 | 6.16 | --epoch 50, --avg 22, --max-duration 500 |
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| beam search (beam size 4) | 6.56 | 5.95 | --epoch 50, --avg 22, --max-duration 500 |
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| modified beam search (beam size 4) | 6.54 | 6.00 | --epoch 50, --avg 22, --max-duration 500 |
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| fast beam search (set as default) | 6.91 | 6.28 | --epoch 50, --avg 22, --max-duration 500 |
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The training command for reproducing is given below:
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```
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export CUDA_VISIBLE_DEVICES="0,1,2,3"
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./zipformer/train.py \
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--use-fp16 true \
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--world-size 4 \
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--num-epochs 50 \
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--start-epoch 0 \
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--exp-dir zipformer/exp \
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--max-duration 1000
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```
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The tensorboard training log can be found at
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https://tensorboard.dev/experiment/AKXbJha0S9aXyfmuvG4h5A/#scalars
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The decoding command is:
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```
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epoch=50
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avg=22
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## greedy search
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./zipformer/decode.py \
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--epoch $epoch \
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--avg $avg \
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--exp-dir zipformer/exp \
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--bpe-model ./data/lang_bpe_500/bpe.model \
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--max-duration 500
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## beam search
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./zipformer/decode.py \
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--epoch $epoch \
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--avg $avg \
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--exp-dir zipformer/exp \
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--bpe-model ./data/lang_bpe_500/bpe.model \
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--max-duration 500 \
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--decoding-method beam_search \
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--beam-size 4
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## modified beam search
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./zipformer/decode.py \
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--epoch $epoch \
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--avg $avg \
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--exp-dir zipformer/exp \
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--bpe-model ./data/lang_bpe_500/bpe.model \
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--max-duration 500 \
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--decoding-method modified_beam_search \
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--beam-size 4
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## fast beam search
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./zipformer/decode.py \
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--epoch $epoch \
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--avg $avg \
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--exp-dir ./zipformer/exp \
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--bpe-model ./data/lang_bpe_500/bpe.model \
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--max-duration 1500 \
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--decoding-method fast_beam_search \
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--beam 4 \
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--max-contexts 4 \
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--max-states 8
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```
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A pre-trained model and decoding logs can be found at <https://huggingface.co/desh2608/icefall-asr-tedlium3-zipformer>
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#### 2023-06-26 (Modified transducer)
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```
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./zipformer/train.py \
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--use-fp16 true \
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--world-size 4 \
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--num-epochs 50 \
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--start-epoch 0 \
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--exp-dir zipformer/exp \
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--max-duration 1000 \
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--rnnt-type modified
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```
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The tensorboard training log can be found at
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https://tensorboard.dev/experiment/3d4bYmbJTGiWQQaW88CVEQ/#scalars
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| | dev | test | comment |
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|------------------------------------|------------|------------|------------------------------------------|
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| greedy search | 6.32 | 5.83 | --epoch 50, --avg 22, --max-duration 500 |
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| modified beam search (beam size 4) | 6.16 | 5.79 | --epoch 50, --avg 22, --max-duration 500 |
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| fast beam search (set as default) | 6.30 | 5.89 | --epoch 50, --avg 22, --max-duration 500 |
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A pre-trained model and decoding logs can be found at <https://huggingface.co/desh2608/icefall-asr-tedlium3-zipformer>.
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### TedLium3 BPE training results (Conformer-CTC 2)
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#### [conformer_ctc2](./conformer_ctc2)
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See <https://github.com/k2-fsa/icefall/pull/696> for more details.
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The tensorboard log can be found at
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<https://tensorboard.dev/experiment/5NQQiqOqSqazfn4w2yeWEQ/>
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You can find a pretrained model and decoding results at:
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<https://huggingface.co/videodanchik/icefall-asr-tedlium3-conformer-ctc2>
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Number of model parameters: 101141699, i.e., 101.14 M
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The WERs are
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| | dev | test | comment |
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|--------------------------|------------|-------------|---------------------|
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| ctc decoding | 6.45 | 5.96 | --epoch 38 --avg 26 |
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| 1best | 5.92 | 5.51 | --epoch 38 --avg 26 |
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| whole lattice rescoring | 5.96 | 5.47 | --epoch 38 --avg 26 |
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| attention decoder | 5.60 | 5.33 | --epoch 38 --avg 26 |
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The training command for reproducing is given below:
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```
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export CUDA_VISIBLE_DEVICES="0,1,2,3"
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./conformer_ctc2/train.py \
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--world-size 4 \
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--num-epochs 40 \
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--exp-dir conformer_ctc2/exp \
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--max-duration 350 \
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--use-fp16 true
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```
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The decoding command is:
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```
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epoch=38
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avg=26
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## ctc decoding
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./conformer_ctc2/decode.py \
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--method ctc-decoding \
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--exp-dir conformer_ctc2/exp \
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--lang-dir data/lang_bpe_500 \
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--result-dir conformer_ctc2/exp \
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--max-duration 500 \
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--epoch $epoch \
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--avg $avg
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## 1best
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./conformer_ctc2/decode.py \
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--method 1best \
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--exp-dir conformer_ctc2/exp \
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--lang-dir data/lang_bpe_500 \
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--result-dir conformer_ctc2/exp \
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--max-duration 500 \
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--epoch $epoch \
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--avg $avg
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## whole lattice rescoring
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./conformer_ctc2/decode.py \
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--method whole-lattice-rescoring \
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--exp-dir conformer_ctc2/exp \
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--lm-path data/lm/G_4_gram_big.pt \
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--lang-dir data/lang_bpe_500 \
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--result-dir conformer_ctc2/exp \
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--max-duration 500 \
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--epoch $epoch \
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--avg $avg
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## attention decoder
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./conformer_ctc2/decode.py \
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--method attention-decoder \
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--exp-dir conformer_ctc2/exp \
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--lang-dir data/lang_bpe_500 \
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--result-dir conformer_ctc2/exp \
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--max-duration 500 \
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--epoch $epoch \
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--avg $avg
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```
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### TedLium3 BPE training results (Pruned Transducer)
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#### 2022-03-21
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Using the codes from this PR https://github.com/k2-fsa/icefall/pull/261.
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The WERs are
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| | dev | test | comment |
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|------------------------------------|------------|------------|------------------------------------------|
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| greedy search | 7.27 | 6.69 | --epoch 29, --avg 13, --max-duration 100 |
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| beam search (beam size 4) | 6.70 | 6.04 | --epoch 29, --avg 13, --max-duration 100 |
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| modified beam search (beam size 4) | 6.77 | 6.14 | --epoch 29, --avg 13, --max-duration 100 |
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| fast beam search (set as default) | 7.14 | 6.50 | --epoch 29, --avg 13, --max-duration 1500|
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The training command for reproducing is given below:
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```
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export CUDA_VISIBLE_DEVICES="0,1,2,3"
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./pruned_transducer_stateless/train.py \
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--world-size 4 \
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--num-epochs 30 \
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--start-epoch 0 \
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--exp-dir pruned_transducer_stateless/exp \
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--max-duration 300
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```
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The tensorboard training log can be found at
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https://tensorboard.dev/experiment/VpA8b7SZQ7CEjZs9WZ5HNA/#scalars
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The decoding command is:
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```
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epoch=29
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avg=13
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## greedy search
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./pruned_transducer_stateless/decode.py \
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--epoch $epoch \
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--avg $avg \
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--exp-dir pruned_transducer_stateless/exp \
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--bpe-model ./data/lang_bpe_500/bpe.model \
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--max-duration 100
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## beam search
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./pruned_transducer_stateless/decode.py \
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--epoch $epoch \
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--avg $avg \
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--exp-dir pruned_transducer_stateless/exp \
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--bpe-model ./data/lang_bpe_500/bpe.model \
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--max-duration 100 \
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--decoding-method beam_search \
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--beam-size 4
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## modified beam search
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./pruned_transducer_stateless/decode.py \
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--epoch $epoch \
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--avg $avg \
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--exp-dir pruned_transducer_stateless/exp \
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--bpe-model ./data/lang_bpe_500/bpe.model \
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--max-duration 100 \
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--decoding-method modified_beam_search \
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--beam-size 4
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## fast beam search
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./pruned_transducer_stateless/decode.py \
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--epoch $epoch \
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--avg $avg \
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--exp-dir ./pruned_transducer_stateless/exp \
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--bpe-model ./data/lang_bpe_500/bpe.model \
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--max-duration 1500 \
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--decoding-method fast_beam_search \
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--beam 4 \
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--max-contexts 4 \
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--max-states 8
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```
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A pre-trained model and decoding logs can be found at <https://huggingface.co/luomingshuang/icefall_asr_tedlium3_pruned_transducer_stateless>
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### TedLium3 BPE training results (Transducer)
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#### Conformer encoder + embedding decoder
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##### 2022-03-21
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Using the codes from this PR https://github.com/k2-fsa/icefall/pull/233
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And the SpecAugment codes from this PR https://github.com/lhotse-speech/lhotse/pull/604
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Conformer encoder + non-current decoder. The decoder
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contains only an embedding layer and a Conv1d (with kernel size 2).
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The WERs are
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| | dev | test | comment |
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|------------------------------------|------------|------------|------------------------------------------|
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| greedy search | 7.19 | 6.70 | --epoch 29, --avg 11, --max-duration 100 |
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| beam search (beam size 4) | 7.02 | 6.36 | --epoch 29, --avg 11, --max-duration 100 |
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| modified beam search (beam size 4) | 6.91 | 6.33 | --epoch 29, --avg 11, --max-duration 100 |
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The training command for reproducing is given below:
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```
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export CUDA_VISIBLE_DEVICES="0,1,2,3"
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./transducer_stateless/train.py \
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--world-size 4 \
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--num-epochs 30 \
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--start-epoch 0 \
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--exp-dir transducer_stateless/exp \
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--max-duration 300
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```
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The tensorboard training log can be found at
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https://tensorboard.dev/experiment/4ks15jYHR4uMyvpW7Nz76Q/#scalars
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The decoding command is:
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```
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epoch=29
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avg=11
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## greedy search
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./transducer_stateless/decode.py \
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--epoch $epoch \
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--avg $avg \
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--exp-dir transducer_stateless/exp \
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--bpe-model ./data/lang_bpe_500/bpe.model \
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--max-duration 100
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## beam search
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./transducer_stateless/decode.py \
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--epoch $epoch \
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--avg $avg \
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--exp-dir transducer_stateless/exp \
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--bpe-model ./data/lang_bpe_500/bpe.model \
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--max-duration 100 \
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--decoding-method beam_search \
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--beam-size 4
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## modified beam search
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./transducer_stateless/decode.py \
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--epoch $epoch \
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--avg $avg \
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--exp-dir transducer_stateless/exp \
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--bpe-model ./data/lang_bpe_500/bpe.model \
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--max-duration 100 \
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--decoding-method modified_beam_search \
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--beam-size 4
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```
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A pre-trained model and decoding logs can be found at <https://huggingface.co/luomingshuang/icefall_asr_tedlium3_transducer_stateless>
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