Note: This recipe is trained with the codes from this PR https://github.com/k2-fsa/icefall/pull/874.
### Pre-trained conv_emformer_transducer_stateless2 models for the TAL_CSASR dataset with icefall.

The model was trained on the far data of TAL_CSASR with the scripts in icefall based on the latest version k2.
You can use the trained model to export it to ncnn and run it with sherpa-ncnn.

### Training procedure

- Install k2 : https://k2.readthedocs.io/en/latest/installation/index.html
- Install lhotse : https://lhotse.readthedocs.io/en/latest/getting-started.html#installation
- Clone icefall : https://github.com/k2-fsa/icefall

```
git clone https://github.com/k2-fsa/icefall
cd icefall

```
- Preparing data.
```
cd egs/tal_csasr_conv_emformer/ASR
bash ./prepare.sh
```
- Training
```
bash run.sh

```
- Evaluation results
The decoding results (CER%) on TAL_CSASR(dev and test) are listed below:

|decoding-method|epoch(iter) |avg| dev|test|
|----|---|---|---|---|
|fast_beam_search | 6 | 3 | 11.36 | 11.37|

- Export model to ncnn
reference : https://k2-fsa.github.io/icefall/model-export/export-ncnn.html
```
./conv_emformer_transducer_stateless2/export-for-ncnn.py \
--exp-dir exp_conv_emformer \
--lang_dir data/lang_char \
--epoch 5 \
--iter 8000 \
--avg 3 \
--use-averaged-model 1 \
--num-encoder-layers 12 \
--chunk-length 32 \
--cnn-module-kernel 31 \
--left-context-length 32 \
--right-context-length 8 \
--memory-size 32

```
- Export torchscript model via pnnx
```
pnnx ./encoder_jit_trace-pnnx.pt
pnnx ./decoder_jit_trace-pnnx.pt
pnnx ./joiner_jit_trace-pnnx.pt
```
- Modify the following two lines in your encoder_jit_trace-pnnx.ncnn.param file.

![image.png](https://s3.amazonaws.com/moonup/production/uploads/1675589236011-62ea602aa32d5132d70ca52c.png)

- Then you can use the following code to test the converted models.
```
model/tokens.txt \
model/encoder_jit_trace-pnnx.ncnn.param \
model/encoder_jit_trace-pnnx.ncnn.bin \
model/decoder_jit_trace-pnnx.ncnn.param \
model/decoder_jit_trace-pnnx.ncnn.bin \
model/joiner_jit_trace-pnnx.ncnn.param \
model/joiner_jit_trace-pnnx.ncnn.bin \
test_wavs/0.wav

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

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


### Pre-trained conv\_emformer\_transducer\_stateless2 models for the TAL\_CSASR dataset with icefall.


The model was trained on the far data of TAL\_CSASR with the scripts in icefall based on the latest version k2.
You can use the trained model to export it to ncnn and run it with sherpa-ncnn.


### Training procedure


* Install k2 : URL
* Install lhotse : URL
* Clone icefall : URL
* Preparing data.
* Training
* Evaluation results
The decoding results (CER%) on TAL\_CSASR(dev and test) are listed below:



* Export model to ncnn
reference : URL
* Export torchscript model via pnnx
* Modify the following two lines in your encoder\_jit\_trace-URL file.


!URL


* Then you can use the following code to test the converted models.



### Pre-trained conv\_emformer\_transducer\_stateless2 models for the TAL\_CSASR dataset with icefall.


The model was trained on the far data of TAL\_CSASR with the scripts in icefall based on the latest version k2.
You can use the trained model to export it to ncnn and run it with sherpa-ncnn. ### Training procedure


* Install k2 : URL
* Install lhotse : URL
* Clone icefall : URL
* Preparing data.
* Training
* Evaluation results
The decoding results (CER%) on TAL\_CSASR(dev and test) are listed below:



* Export model to ncnn
reference : URL
* Export torchscript model via pnnx
* Modify the following two lines in your encoder\_jit\_trace-URL file.


!URL


* Then you can use the following code to test the converted models.
TAGS
#region-us 
 ### Pre-trained conv\_emformer\_transducer\_stateless2 models for the TAL\_CSASR dataset with icefall.


The model was trained on the far data of TAL\_CSASR with the scripts in icefall based on the latest version k2.
You can use the trained model to export it to ncnn and run it with sherpa-ncnn. ### Training procedure


* Install k2 : URL
* Install lhotse : URL
* Clone icefall : URL
* Preparing data.
* Training
* Evaluation results
The decoding results (CER%) on TAL\_CSASR(dev and test) are listed below:



* Export model to ncnn
reference : URL
* Export torchscript model via pnnx
* Modify the following two lines in your encoder\_jit\_trace-URL file.


!URL


* Then you can use the following code to test the converted models.
TAGS
#region-us 
### Pre-trained conv\_emformer\_transducer\_stateless2 models for the TAL\_CSASR dataset with icefall.


The model was trained on the far data of TAL\_CSASR with the scripts in icefall based on the latest version k2.
You can use the trained model to export it to ncnn and run it with sherpa-ncnn.### Training procedure


* Install k2 : URL
* Install lhotse : URL
* Clone icefall : URL
* Preparing data.
* Training
* Evaluation results
The decoding results (CER%) on TAL\_CSASR(dev and test) are listed below:



* Export model to ncnn
reference : URL
* Export torchscript model via pnnx
* Modify the following two lines in your encoder\_jit\_trace-URL file.


!URL


* Then you can use the following code to test the converted models.