<<<<<<< HEAD
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) | 6.77       | 6.14       | --epoch 29, --avg 13, --max-duration 100 |

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<<<<<<< HEAD
Note: This recipe is trained with the codes from this PR URL
And the SpecAugment codes from this PR URL


Pre-trained Transducer-Stateless models for the TEDLium3 dataset with icefall.
==============================================================================


The model was trained on full TEDLium3 with the scripts in icefall.


Training procedure
------------------


The main repositories are list below, we will update the training and decoding scripts with the update of version.  

k2: URL
icefall: URL
lhotse: URL


* Install k2 and lhotse, k2 installation guide refers to URL lhotse refers to URL I think the latest version would be ok. And please also install the requirements listed in icefall.
* Clone icefall(URL and check to the commit showed above.
* Preparing data.
* Training


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




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