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153 lines
4.6 KiB
Markdown
153 lines
4.6 KiB
Markdown
## Results
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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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