add itn_pipeline.md

docs/modelscope_pipeline/itn_pipeline.md

@@ -18,10 +18,12 @@ itn_inference_pipline = pipeline(
 
 itn_result = itn_inference_pipline(text_in='百二十三')
 print(itn_result)
+# 123
 ```
 - read text data directly.
 ```python
 rec_result = inference_pipeline(text_in='一九九九年に誕生した同商品にちなみ、約三十年前、二十四歳の頃の幸四郎の写真を公開。')
+# 1999年に誕生した同商品にちなみ、約30年前、24歳の頃の幸四郎の写真を公開。
 ```
 - text stored via url，example：https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_text/ja_itn_example.txt
 ```python
@@ -30,22 +32,6 @@ rec_result = inference_pipeline(text_in='https://isv-data.oss-cn-hangzhou.aliyun
 
 Full code of demo, please ref to [demo](https://github.com/alibaba-damo-academy/FunASR/tree/main/fun_text_processing/inverse_text_normalization)
 
-### Modify Your Own ITN Model
-The rule-based ITN code is open-sourced in [FunTextProcessing](https://github.com/alibaba-damo-academy/FunASR/tree/main/fun_text_processing), users can modify by their own grammar rules. After modify the rules, the users can export their own ITN models in local directory.
-
-### Export ITN Model
-Use the code in FunASR to export ITN model. An example to export ITN model to local folder is shown as below.
-```shell
-cd fun_text_processing/inverse_text_normalization/
-python export_models.py --language ja --export_dir ./itn_models/
-```
-
-### Evaluate ITN Model
-Users can evaluate their own ITN model in local directory. Here is an example:
-```shell
-python fun_text_processing/inverse_text_normalization/inverse_normalize.py --input_file ja_itn_example.txt --cache_dir ./itn_models/ --output_file output.txt --language=ja
-```
-
 ### API-reference
 #### Define pipeline
 - `task`: `Tasks.inverse_text_processing`
@@ -58,4 +44,19 @@ python fun_text_processing/inverse_text_normalization/inverse_normalize.py --inp
   - text bytes, `e.g.`: "一九九九年に誕生した同商品にちなみ、約三十年前、二十四歳の頃の幸四郎の写真を公開。"
   - text file, `e.g.`: https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_text/ja_itn_example.txt
   In this case of `text file` input, `output_dir` must be set to save the output results
-  
+
+## Modify Your Own ITN Model
+The rule-based ITN code is open-sourced in [FunTextProcessing](https://github.com/alibaba-damo-academy/FunASR/tree/main/fun_text_processing), users can modify by their own grammar rules for different languages. Let's take Japanese as an example, users can add their own whitelist in fun_text_processing/inverse_text_normalization/ja/data/whitelist.tsv. After modify the rules, the users can export their own ITN models in local directory.
+
+### Export ITN Model
+Use the code in FunASR to export ITN model. An example to export ITN model to local folder is shown as below.
+```shell
+cd fun_text_processing/inverse_text_normalization/
+python export_models.py --language ja --export_dir ./itn_models/
+```
+
+### Evaluate ITN Model
+Users can evaluate their own ITN model in local directory. Here is an example:
+```shell
+python fun_text_processing/inverse_text_normalization/inverse_normalize.py --input_file ja_itn_example.txt --cache_dir ./itn_models/ --output_file output.txt --language=ja
+```