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AmirHussein96 2025-09-15 23:36:29 -04:00
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# Results
### IWSLT Tunisian training results (Stateless Pruned Transducer)
#### 2023-06-01
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| Decoding method | dev WER | test WER | comment |
|------------------------------------|------------|------------|------------------------------------------|
| modified beam search | 47.6 | 51.2 | --epoch 20, --avg 10 |
| modified beam search | 47.6 | 51.2 | --epoch 20, --avg 13 |
The training command for reproducing is given below:
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#### 2023-06-01
You can find a pretrained model, training logs, decoding logs, and decoding results at:
https://tensorboard.dev/experiment/yLE399ZPTzePG8B39jRyOw/
You can find a pretrained model, training logs, decoding logs, and decoding results at:
<https://huggingface.co/AmirHussein/zipformer-iwslt22-Ta>
| Decoding method | dev WER | test WER | comment |
|------------------------------------|------------|------------|------------------------------------------|
| modified beam search | 40.8 | 44.4 | --epoch 20, --avg 10 |
| modified beam search | 40.8 | 44.1 | --epoch 20, --avg 13 |
To reproduce the above result, use the following commands for training:
@ -70,7 +71,7 @@ To reproduce the above result, use the following commands for training:
```
export CUDA_VISIBLE_DEVICES="0,1"
./zipformer/train.py \
--world-size 2 \
--world-size 4 \
--num-epochs 20 \
--start-epoch 1 \
--use-fp16 1 \
@ -82,7 +83,7 @@ export CUDA_VISIBLE_DEVICES="0,1"
--encoder-unmasked-dim 192,192,256,256,256,192 \
--max-duration 800 \
--prune-range 10
```
The decoding command is:
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--num-encoder-layers 2,2,2,2,2,2 \
--feedforward-dim 512,768,1024,1536,1024,768 \
--encoder-dim 192,256,384,512,384,256 \
--encoder-unmasked-dim 192,192,256,256,256,192
--encoder-unmasked-dim 192,192,256,256,256,192 \
--use-averaged-model true
done
```

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#### 2023-06-01
You can find a pretrained model, training logs, decoding logs, and decoding results at:
You can find a pretrained model, training logs, decoding logs, and decoding results at:
<https://huggingface.co/AmirHussein/zipformer-iwslt22-Ta>
@ -95,8 +96,6 @@ To reproduce the above result, use the following commands for training:
```
The tensorboard training log can be found at
https://tensorboard.dev/experiment/4sa4M1mRQyKjOE4o95mWUw/
The decoding command is: