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Note: This recipe is trained with the codes from this PR https://github.com/k2-fsa/icefall/pull/261
And the SpecAugment codes from this PR https://github.com/lhotse-speech/lhotse/pull/604.
# Pre-trained Transducer-Stateless models for the TEDLium3 dataset with icefall.
The model was trained on full [TEDLium3](https://www.openslr.org/51) with the scripts in [icefall](https://github.com/k2-fsa/icefall).
## Training procedure
The main repositories are list below, we will update the training and decoding scripts with the update of version.
k2: https://github.com/k2-fsa/k2
icefall: https://github.com/k2-fsa/icefall
lhotse: https://github.com/lhotse-speech/lhotse
* Install k2 and lhotse, k2 installation guide refers to https://k2.readthedocs.io/en/latest/installation/index.html, lhotse refers to https://lhotse.readthedocs.io/en/latest/getting-started.html#installation. I think the latest version would be ok. And please also install the requirements listed in icefall.
* Clone icefall(https://github.com/k2-fsa/icefall) and check to the commit showed above.
```
git clone https://github.com/k2-fsa/icefall
cd icefall
```
* Preparing data.
```
cd egs/tedlium3/ASR
bash ./prepare.sh
```
* Training
```
export CUDA_VISIBLE_DEVICES="0,1,2,3"
./pruned_transducer_stateless/train.py \
--world-size 4 \
--num-epochs 30 \
--start-epoch 0 \
--exp-dir pruned_transducer_stateless/exp \
--max-duration 300
```
## Evaluation results
The decoding results (WER%) on TEDLium3 (dev and test) are listed below, we got this result by averaging models from epoch 17 to 29.
The WERs are
| | dev | test | comment |
|------------------------------------|------------|------------|------------------------------------------|
| 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) | 6.72 | 6.12 | --epoch 29, --avg 13, --max-duration 100 |
|