Note: This recipe is trained with the codes from this PR https://github.com/k2-fsa/icefall/pull/378
And the SpecAugment codes from this PR https://github.com/lhotse-speech/lhotse/pull/604.
# Pre-trained Transducer-Stateless2 models for the Alimeeting dataset with icefall.
The model was trained on the far data of [Alimeeting](https://www.openslr.org/119) 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/alimeeting/ASR
bash ./prepare.sh
```
* Training
```
export CUDA_VISIBLE_DEVICES="0,1,2,3"
./pruned_transducer_stateless2/train.py \
                  --world-size 4 \
                  --num-epochs 30 \
                  --start-epoch 0 \
                  --exp-dir pruned_transducer_stateless2/exp \
                  --lang-dir data/lang_char \
                  --max-duration 220
```
## Evaluation results
The decoding results (WER%) on Alimeeting(eval and test) are listed below, we got this result by averaging models from epoch 12 to 29.
The WERs are
|                                    |     eval   |    test    | comment                                  |
|------------------------------------|------------|------------|------------------------------------------|
|          greedy search             | 31.77      | 34.66      | --epoch 29, --avg 18, --max-duration 100 |
| modified beam search (beam size 4) | 30.38      | 33.02      | --epoch 29, --avg 18, --max-duration 100 |
| fast beam search (set as default)  | 31.39      | 34.25      | --epoch 29, --avg 18, --max-duration 1500|


{}
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#has_space #region-us 

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


Pre-trained Transducer-Stateless2 models for the Alimeeting dataset with icefall.
=================================================================================


The model was trained on the far data of Alimeeting with the scripts in 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: 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 Alimeeting(eval and test) are listed below, we got this result by averaging models from epoch 12 to 29.
The WERs are




TAGS
#has_space #region-us 

passage: TAGS
#has_space #region-us 
