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bccd20d978
* Add byte level bpe tal_csasr recipe * Minor fixes to decoding and exporting * Fix prepare.sh * Update results
134 lines
3.9 KiB
Markdown
134 lines
3.9 KiB
Markdown
## Results
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#### Pruned transducer stateless 7 (zipformer)
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See <https://github.com/k2-fsa/icefall/pull/1033>
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[./pruned_transducer_stateless7_bbpe](./pruned_transducer_stateless7_bbpe)
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**Note**: The modeling units are byte level BPEs
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The best results I have gotten are:
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Vocab size | greedy (dev & test) | modified beam search (dev & test) | |
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-- | -- | -- | --
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500 | 6.88 & 6.98 | 6.87 & 6.94 | --epoch 35 --avg 26
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The training command:
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```
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export CUDA_VISIBLE_DEVICES="0,1,2,3"
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./pruned_transducer_stateless7_bbpe/train.py \
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--world-size 4 \
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--start-epoch 1 \
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--num-epochs 35 \
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--use-fp16 1 \
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--max-duration 800 \
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--bbpe-model data/lang_bbpe_500/bbpe.model \
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--exp-dir pruned_transducer_stateless7_bbpe/exp \
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--master-port 12535
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```
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The decoding command:
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```
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./pruned_transducer_stateless7_bbpe/decode.py \
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--epoch 35 \
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--avg 26 \
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--exp-dir ./pruned_transducer_stateless7_bbpe/exp \
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--max-sym-per-frame 1 \
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--bpe-model data/lang_bbpe_500/bbpe.model \
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--max-duration 2000 \
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--decoding-method greedy_search # modified_beam_search
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```
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The pretrained model is available at: https://huggingface.co/pkufool/icefall_asr_tal_csasr_pruned_transducer_stateless7_bbpe
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### TAL_CSASR Mix Chars and BPEs training results (Pruned Transducer Stateless5)
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#### 2022-06-22
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Using the codes from this PR https://github.com/k2-fsa/icefall/pull/428.
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The WERs are
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|decoding-method | epoch(iter) | avg | dev | test |
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|--|--|--|--|--|
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|greedy_search | 30 | 24 | 7.49 | 7.58|
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|modified_beam_search | 30 | 24 | 7.33 | 7.38|
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|fast_beam_search | 30 | 24 | 7.32 | 7.42|
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|greedy_search(use-averaged-model=True) | 30 | 24 | 7.30 | 7.39|
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|modified_beam_search(use-averaged-model=True) | 30 | 24 | 7.15 | 7.22|
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|fast_beam_search(use-averaged-model=True) | 30 | 24 | 7.18 | 7.27|
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|greedy_search | 348000 | 30 | 7.46 | 7.54|
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|modified_beam_search | 348000 | 30 | 7.24 | 7.36|
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|fast_beam_search | 348000 | 30 | 7.25 | 7.39 |
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The results (CER(%) and WER(%)) for Chinese CER and English WER respectivly (zh: Chinese, en: English):
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|decoding-method | epoch(iter) | avg | dev | dev_zh | dev_en | test | test_zh | test_en |
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|--|--|--|--|--|--|--|--|--|
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|greedy_search(use-averaged-model=True) | 30 | 24 | 7.30 | 6.48 | 19.19 |7.39| 6.66 | 19.13|
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|modified_beam_search(use-averaged-model=True) | 30 | 24 | 7.15 | 6.35 | 18.95 | 7.22| 6.50 | 18.70 |
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|fast_beam_search(use-averaged-model=True) | 30 | 24 | 7.18 | 6.39| 18.90 | 7.27| 6.55 | 18.77|
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The training command for reproducing is given below:
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```
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export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5"
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./pruned_transducer_stateless5/train.py \
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--world-size 6 \
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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 90
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```
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The tensorboard training log can be found at
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https://tensorboard.dev/experiment/KaACzXOVR0OM6cy0qbN5hw/#scalars
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The decoding command is:
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```
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epoch=30
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avg=24
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use_average_model=True
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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 $use_average_model
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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 $use_average_model
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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 $use_average_model
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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_tal-csasr_pruned_transducer_stateless5>
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