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update for results

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luomingshuang 2022-04-11 19:35:39 +08:00
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README.md
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@ -176,7 +176,7 @@ The best WER using modified beam search with beam size 4 is:
| | dev | test |
|-----|-------|--------|
| WER | 6.72 | 6.12 |
| WER | 6.77 | 6.14 |
We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1je_1zGrOkGVVd4WLzgkXRHxl-I27yWtz?usp=sharing)

4
egs/tedlium3/ASR/RESULTS.md
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@ -12,7 +12,7 @@ The WERs are
|------------------------------------|------------|------------|------------------------------------------|
| greedy search | 7.27 | 6.69 | --epoch 29, --avg 13, --max-duration 100 |
| beam search (beam size 4) | 6.70 | 6.04 | --epoch 29, --avg 13, --max-duration 100 |
| modified beam search (beam size 4) | 6.77 | 6.12 | --epoch 29, --avg 13, --max-duration 100 |
| modified beam search (beam size 4) | 6.77 | 6.14 | --epoch 29, --avg 13, --max-duration 100 |
| fast beam search (set as default) | 7.14 | 6.50 | --epoch 29, --avg 13, --max-duration 1500|
The training command for reproducing is given below:
@ -37,7 +37,7 @@ epoch=29
avg=13
## greedy search
./pruned_transducer_stateless/decode.py \
./prured_transducer_stateless/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir pruned_transducer_stateless/exp \