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docs/modelscope_pipeline/itn_pipeline.md

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+# Inverse Text Normalization (ITN)
+
+> **Note**: 
+> The modelscope pipeline supports all the models in [model zoo](https://modelscope.cn/models?page=1&tasks=inverse-text-processing&type=audio) to inference. Here we take the model of the Japanese ITN model as example to demonstrate the usage.
+
+## Inference
+
+### Quick start
+#### [Japanese ITN model](https://modelscope.cn/models/damo/speech_inverse_text_processing_fun-text-processing-itn-ja/summary)
+```python
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+itn_inference_pipline = pipeline(
+    task=Tasks.inverse_text_processing,
+    model='damo/speech_inverse_text_processing_fun-text-processing-itn-ja',
+    model_revision=None)
+
+itn_result = itn_inference_pipline(text_in='百二十三')
+print(itn_result)
+```
+- 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。')
+```
+- text stored via url，example：https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_text/ja_itn_example.txt
+```python
+rec_result = inference_pipeline(text_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_text/ja_itn_example.txt')
+```
+
+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`
+- `model`: model name in [model zoo](https://modelscope.cn/models?page=1&tasks=inverse-text-processing&type=audio), or model path in local disk
+- `output_dir`: `None` (Default), the output path of results if set
+- `model_revision`: `None` (Default), setting the model version
+
+#### Infer pipeline
+- `text_in`: the input to decode, which could be:
+  - 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
+
+
+## Finetune with pipeline
+
+### Quick start
+
+### Finetune with your data
+
+## Inference with your finetuned model
+