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* pruned-transducer-stateless5 recipe for aishell4 * pruned-transducer-stateless5 recipe for aishell4 * do some changes and text normalize * do some changes * add text normalize * combine the training data and decode without webdataset * update codes for merging * Do a change for READMD.md
118 lines
3.7 KiB
Markdown
118 lines
3.7 KiB
Markdown
## Results
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### Aishell4 Char training results (Pruned Transducer Stateless5)
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#### 2022-06-13
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Using the codes from this PR https://github.com/k2-fsa/icefall/pull/399.
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When use-averaged-model=False, the CERs are
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| | test | comment |
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|------------------------------------|------------|------------------------------------------|
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| greedy search | 30.05 | --epoch 30, --avg 25, --max-duration 800 |
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| modified beam search (beam size 4) | 29.16 | --epoch 30, --avg 25, --max-duration 800 |
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| fast beam search (set as default) | 29.20 | --epoch 30, --avg 25, --max-duration 1500|
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When use-averaged-model=True, the CERs are
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| | test | comment |
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|------------------------------------|------------|----------------------------------------------------------------------|
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| greedy search | 29.89 | --iter 36000, --avg 8, --max-duration 800 --use-averaged-model=True |
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| modified beam search (beam size 4) | 28.91 | --iter 36000, --avg 8, --max-duration 800 --use-averaged-model=True |
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| fast beam search (set as default) | 29.08 | --iter 36000, --avg 8, --max-duration 1500 --use-averaged-model=True |
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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_stateless5/train.py \
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--world-size 4 \
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--num-epochs 30 \
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--start-epoch 1 \
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--exp-dir pruned_transducer_stateless5/exp \
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--lang-dir data/lang_char \
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--max-duration 220 \
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--save-every-n 4000
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```
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The tensorboard training log can be found at
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https://tensorboard.dev/experiment/tjaVRKERS8C10SzhpBcxSQ/#scalars
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When use-averaged-model=False, the decoding command is:
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```
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epoch=30
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avg=25
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## greedy search
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./pruned_transducer_stateless5/decode.py \
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--epoch $epoch \
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--avg $avg \
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--exp-dir pruned_transducer_stateless5/exp \
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--lang-dir ./data/lang_char \
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--max-duration 800
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## modified beam search
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./pruned_transducer_stateless5/decode.py \
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--epoch $epoch \
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--avg $avg \
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--exp-dir pruned_transducer_stateless5/exp \
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--lang-dir ./data/lang_char \
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--max-duration 800 \
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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_stateless5/decode.py \
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--epoch $epoch \
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--avg $avg \
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--exp-dir ./pruned_transducer_stateless5/exp \
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--lang-dir ./data/lang_char \
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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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When use-averaged-model=True, the decoding command is:
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```
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iter=36000
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avg=8
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## greedy search
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./pruned_transducer_stateless5/decode.py \
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--epoch $epoch \
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--avg $avg \
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--exp-dir pruned_transducer_stateless5/exp \
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--lang-dir ./data/lang_char \
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--max-duration 800 \
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--use-averaged-model True
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## modified beam search
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./pruned_transducer_stateless5/decode.py \
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--epoch $epoch \
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--avg $avg \
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--exp-dir pruned_transducer_stateless5/exp \
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--lang-dir ./data/lang_char \
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--max-duration 800 \
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--decoding-method modified_beam_search \
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--beam-size 4 \
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--use-averaged-model True
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## fast beam search
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./pruned_transducer_stateless5/decode.py \
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--epoch $epoch \
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--avg $avg \
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--exp-dir ./pruned_transducer_stateless5/exp \
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--lang-dir ./data/lang_char \
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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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--use-averaged-model True
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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_aishell4_pruned_transducer_stateless5>
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