Note: This recipe is trained with the codes from this PR https://github.com/k2-fsa/icefall/pull/349
# Pre-trained Transducer-Stateless2 models for the WenetSpeech dataset with icefall.
The model was trained on the L subset of WenetSpeech with the scripts in [icefall](https://github.com/k2-fsa/icefall) based on the latest version k2.  
## 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/wenetspeech/ASR
bash ./prepare.sh
```
* Training
```
export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
./pruned_transducer_stateless2/train.py \
                  --world-size 8 \
                  --num-epochs 15 \
                  --start-epoch 0 \
                  --exp-dir pruned_transducer_stateless2/exp \
                  --lang-dir data/lang_char \
                  --max-duration 180 \
                  --valid-interval 3000 \
                  --model-warm-step 3000 \
                  --save-every-n 8000 \
                  --training-subset L
```
## Evaluation results
The decoding results (WER%) on WenetSpeech(dev, test-net and test-meeting) are listed below, we got this result by averaging models from epoch 9 to 10.
The WERs are
|                                    |  dev  | test-net | test-meeting | comment                                  |
|------------------------------------|-------|----------|--------------|------------------------------------------|
|          greedy search             | 7.80  | 8.75     | 13.49        | --epoch 10, --avg 2, --max-duration 100  |
| modified beam search (beam size 4) | 7.76  | 8.71     | 13.41        | --epoch 10, --avg 2, --max-duration 100  |
| fast beam search (1best)           | 7.94  | 8.74     | 13.80        | --epoch 10, --avg 2, --max-duration 1500 |
| fast beam search (nbest)           | 9.82  | 10.98    | 16.37        | --epoch 10, --avg 2, --max-duration 600  |
| fast beam search (nbest oracle)    | 6.88  | 7.18     | 11.77        | --epoch 10, --avg 2, --max-duration 600  |
| fast beam search (nbest LG)        | 14.94 | 16.14    | 22.93        | --epoch 10, --avg 2, --max-duration 600  |