Note: This recipe is trained with the codes from this PR https://github.com/k2-fsa/icefall/pull/399
# Pre-trained Transducer-Stateless5 models for the AISHELL4 dataset with icefall.
The model was trained on the far data of [AISHELL4](https://www.openslr.org/111) 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/aishell4/ASR
bash ./prepare.sh
```
* Training
```
export CUDA_VISIBLE_DEVICES="0,1,2,3"
./pruned_transducer_stateless5/train.py \
                  --world-size 4 \
                  --num-epochs 30 \
                  --start-epoch 1 \
                  --exp-dir pruned_transducer_stateless5/exp \
                  --lang-dir data/lang_char \
                  --max-duration 220
```
## Evaluation results
The decoding results (CER%) on AISHELL4(test) are listed below:

When use-averaged-model=False, the CERs are

|                                    |    test    | comment                                  |
|------------------------------------|------------|------------------------------------------|
|          greedy search             | 30.05      | --epoch 30, --avg 25, --max-duration 800 |
| modified beam search (beam size 4) | 29.16      | --epoch 30, --avg 25, --max-duration 800 |
| fast beam search (set as default)  | 29.20      | --epoch 30, --avg 25, --max-duration 1500|

When use-averaged-model=True, the CERs are

|                                    |    test    | comment                                                              |
|------------------------------------|------------|----------------------------------------------------------------------|
|          greedy search             | 29.89      | --iter 36000, --avg 8, --max-duration 800  --use-averaged-model=True |
| modified beam search (beam size 4) | 28.91      | --iter 36000, --avg 8, --max-duration 800  --use-averaged-model=True |
| fast beam search (set as default)  | 29.08      | --iter 36000, --avg 8, --max-duration 1500 --use-averaged-model=True |


{}
tensorboard region:us
TAGS
#tensorboard #region-us 

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


Pre-trained Transducer-Stateless5 models for the AISHELL4 dataset with icefall.
===============================================================================


The model was trained on the far data of AISHELL4 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 (CER%) on AISHELL4(test) are listed below:


When use-averaged-model=False, the CERs are


test: greedy search, comment: 30.05
test: modified beam search (beam size 4), comment: 29.16
test: fast beam search (set as default), comment: 29.20


When use-averaged-model=True, the CERs are


test: greedy search, comment: 29.89
test: modified beam search (beam size 4), comment: 28.91
test: fast beam search (set as default), comment: 29.08



TAGS
#tensorboard #region-us 

passage: TAGS
#tensorboard #region-us 
