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## Results
### SPGISpeech BPE training results (Zipformer Transducer)
#### 2024-01-05
#### Zipformer encoder + embedding decoder
Transducer: Zipformer encoder + stateless decoder.
The WERs are:
| | dev | val | comment |
|---------------------------|------------|------------|------------------------------------------|
| greedy search | 2.08 | 2.14 | --epoch 30 --avg 10 |
| modified beam search | 2.05 | 2.09 | --epoch 30 --avg 10 --beam-size 4 |
n| fast beam search | 2.07 | 2.17 | --epoch 30 --avg 10 --beam 20 --max-contexts 8 --max-states 64 |
**NOTE:** SPGISpeech transcripts can be prepared in `ortho` or `norm` ways, which refer to whether the
transcripts are orthographic or normalized. These WERs correspond to the normalized transcription
scenario.
The training command for reproducing is given below:
```
export CUDA_VISIBLE_DEVICES="0,1,2,3"
python zipformer/train.py \
--world-size 4 \
--num-epochs 30 \
--start-epoch 1 \
--use-fp16 1 \
--exp-dir zipformer/exp \
--num-workers 2 \
--max-duration 1000
```
The decoding command is:
```
# greedy search
python ./zipformer/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir ./zipformer/exp \
--max-duration 1000 \
--decoding-method modified_beam_search
--decoding-method greedy_search
# modified beam search
python ./zipformer/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir ./zipformer/exp \
--max-duration 1000 \
--decoding-method modified_beam_search
# fast beam search
python ./zipformer/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir ./zipformer/exp \
--max-duration 1000 \
--decoding-method fast_beam_search
--beam 4 \
--max-contexts 4 \
--max-states 8
```
### SPGISpeech BPE training results (Pruned Transducer)
#### 2022-05-11