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AmirHussein96 2025-09-15 23:36:29 -04:00
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15
egs/iwslt22_ta/ASR/RESULTS.md
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@ -1,6 +1,7 @@
# Results # Results
### IWSLT Tunisian training results (Stateless Pruned Transducer) ### IWSLT Tunisian training results (Stateless Pruned Transducer)
#### 2023-06-01 #### 2023-06-01
@ -8,7 +9,7 @@
| Decoding method | dev WER | test WER | comment | | 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: The training command for reproducing is given below:
@ -54,14 +55,14 @@ done
#### 2023-06-01 #### 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://tensorboard.dev/experiment/yLE399ZPTzePG8B39jRyOw/ <https://huggingface.co/AmirHussein/zipformer-iwslt22-Ta>
| Decoding method | dev WER | test WER | comment | | 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: 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" export CUDA_VISIBLE_DEVICES="0,1"
./zipformer/train.py \ ./zipformer/train.py \
--world-size 2 \ --world-size 4 \
--num-epochs 20 \ --num-epochs 20 \
--start-epoch 1 \ --start-epoch 1 \
--use-fp16 1 \ --use-fp16 1 \
@ -82,7 +83,7 @@ export CUDA_VISIBLE_DEVICES="0,1"
--encoder-unmasked-dim 192,192,256,256,256,192 \ --encoder-unmasked-dim 192,192,256,256,256,192 \
--max-duration 800 \ --max-duration 800 \
--prune-range 10 --prune-range 10
``` ```
The decoding command is: The decoding command is:
@ -99,7 +100,7 @@ for method in modified_beam_search; do
--num-encoder-layers 2,2,2,2,2,2 \ --num-encoder-layers 2,2,2,2,2,2 \
--feedforward-dim 512,768,1024,1536,1024,768 \ --feedforward-dim 512,768,1024,1536,1024,768 \
--encoder-dim 192,256,384,512,384,256 \ --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 --use-averaged-model true
done done
``` ```

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egs/iwslt22_ta/ST/RESULTS.md
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@ -57,7 +57,8 @@ done
#### 2023-06-01 #### 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: The decoding command is: