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126 lines
3.3 KiB
Markdown
126 lines
3.3 KiB
Markdown
# Results
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### IWSLT Tunisian training results (Stateless Pruned Transducer)
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#### 2023-06-01
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| Decoding method | dev Bleu | test Bleu | comment |
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|------------------------------------|------------|------------|------------------------------------------|
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| modified beam search | 11.1 | 9.2 | --epoch 20, --avg 10, beam(10), pruned range 5 |
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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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./pruned_transducer_stateless5/train.py \
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--world-size 4 \
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--num-epochs 20 \
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--start-epoch 1 \
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--exp-dir pruned_transducer_stateless5/exp \
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--max-duration 300 \
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--bucketing-sampler 1\
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--num-buckets 50
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```
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The tensorboard training log can be found at
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https://tensorboard.dev/experiment/YnzQNCVDSxCvP1onrCzg9A/
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The decoding command is:
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```
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for method in modified_beam_search; do
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for epoch in 15 20; do
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./pruned_transducer_stateless5/decode.py \
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--epoch $epoch \
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--beam-size 20 \
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--avg 10 \
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--exp-dir ./pruned_transducer_stateless5/exp_st \
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--max-duration 300 \
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--decoding-method $method \
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--max-sym-per-frame 1 \
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--num-encoder-layers 12 \
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--dim-feedforward 1024 \
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--nhead 8 \
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--encoder-dim 256 \
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--decoder-dim 256 \
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--joiner-dim 256 \
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--use-averaged-model true
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done
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done
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```
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### IWSLT Tunisian training results (Zipformer)
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#### 2023-06-01
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You can find a pretrained model, training logs, decoding logs, and decoding results at:
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<https://huggingface.co/AmirHussein/zipformer-iwslt22-Ta>
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| Decoding method | dev Bleu | test Bleu | comment |
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|------------------------------------|------------|------------|------------------------------------------|
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| modified beam search | 14.7 | 12.4 | --epoch 20, --avg 10, beam(10),pruned range 5 |
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| modified beam search | 15.5 | 13 | --epoch 20, --avg 10, beam(20),pruned range 5 |
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| modified beam search | 18.2 | 14.8 | --epoch 20, --avg 10, beam(20), pruned range 10 |
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To reproduce the above result, use the following commands for training:
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# Note: the model was trained on V-100 32GB GPU
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# ST medium model 42.5M prune-range 10
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```
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./zipformer/train.py \
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--world-size 4 \
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--num-epochs 25 \
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--start-epoch 1 \
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--use-fp16 1 \
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--exp-dir zipformer/exp-st-medium \
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--causal 0 \
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--num-encoder-layers 2,2,2,2,2,2 \
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--feedforward-dim 512,768,1024,1536,1024,768 \
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--encoder-dim 192,256,384,512,384,256 \
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--encoder-unmasked-dim 192,192,256,256,256,192 \
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--max-duration 800 \
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--prune-range 10 \
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--warm-step 5000 \
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--lr-epochs 8 \
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--base-lr 0.055 \
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--use-hat False
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```
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The decoding command is:
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```
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for method in modified_beam_search; do
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for epoch in 15 20; do
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./zipformer/decode.py \
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--epoch $epoch \
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--beam-size 20 \
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--avg 10 \
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--exp-dir ./zipformer/exp-st-medium-prun10 \
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--max-duration 800 \
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--decoding-method $method \
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--num-encoder-layers 2,2,2,2,2,2 \
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--feedforward-dim 512,768,1024,1536,1024,768 \
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--encoder-dim 192,256,384,512,384,256 \
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--encoder-unmasked-dim 192,192,256,256,256,192 \
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--context-size 2 \
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--use-averaged-model true \
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--use-hat False
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done
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done
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```
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