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## Results
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## Results
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### SPGISpeech BPE training results (Zipformer Transducer)
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#### 2024-01-05
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#### Zipformer encoder + embedding decoder
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Transducer: Zipformer encoder + stateless decoder.
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The WERs are:
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| | dev | val | comment |
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|---------------------------|------------|------------|------------------------------------------|
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| greedy search | 2.08 | 2.14 | --epoch 30 --avg 10 |
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| modified beam search | 2.05 | 2.09 | --epoch 30 --avg 10 --beam-size 4 |
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n| fast beam search | 2.07 | 2.17 | --epoch 30 --avg 10 --beam 20 --max-contexts 8 --max-states 64 |
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**NOTE:** SPGISpeech transcripts can be prepared in `ortho` or `norm` ways, which refer to whether the
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transcripts are orthographic or normalized. These WERs correspond to the normalized transcription
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scenario.
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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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python zipformer/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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--use-fp16 1 \
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--exp-dir zipformer/exp \
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--num-workers 2 \
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--max-duration 1000
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```
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The decoding command is:
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```
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# greedy search
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python ./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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--max-duration 1000 \
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--decoding-method modified_beam_search
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--decoding-method greedy_search
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# modified beam search
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python ./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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--max-duration 1000 \
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--decoding-method modified_beam_search
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# fast beam search
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python ./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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--max-duration 1000 \
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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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### SPGISpeech BPE training results (Pruned Transducer)
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### SPGISpeech BPE training results (Pruned Transducer)
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#### 2022-05-11
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#### 2022-05-11
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