From f77c5803f4d61099e572be8d877b1c4a4d6087cd Mon Sep 17 00:00:00 2001
From: yhliang <68215459+yhliang-aslp@users.noreply.github.com>
Date: 星期三, 10 五月 2023 12:02:06 +0800
Subject: [PATCH] Merge pull request #485 from alibaba-damo-academy/main

---
 egs_modelscope/punctuation/TEMPLATE/README.md |   48 +++++++++++++++++++++++-------------------------
 1 files changed, 23 insertions(+), 25 deletions(-)

diff --git a/docs/modelscope_pipeline/punc_pipeline.md b/egs_modelscope/punctuation/TEMPLATE/README.md
similarity index 61%
rename from docs/modelscope_pipeline/punc_pipeline.md
rename to egs_modelscope/punctuation/TEMPLATE/README.md
index 5618973..08814ea 100644
--- a/docs/modelscope_pipeline/punc_pipeline.md
+++ b/egs_modelscope/punctuation/TEMPLATE/README.md
@@ -1,8 +1,7 @@
 # Punctuation Restoration
-# Voice Activity Detection
 
 > **Note**: 
-> The modelscope pipeline supports all the models in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html#pretrained-models-on-modelscope) to inference and finetune. Here we take the model of the punctuation model of CT-Transformer as example to demonstrate the usage.
+> The modelscope pipeline supports all the models in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope) to inference and finetune. Here we take the model of the punctuation model of CT-Transformer as example to demonstrate the usage.
 
 ## Inference
 
@@ -12,21 +11,21 @@
 from modelscope.pipelines import pipeline
 from modelscope.utils.constant import Tasks
 
-inference_pipline = pipeline(
+inference_pipeline = pipeline(
     task=Tasks.punctuation,
     model='damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch',
     model_revision=None)
 
-rec_result = inference_pipline(text_in='example/punc_example.txt')
+rec_result = inference_pipeline(text_in='example/punc_example.txt')
 print(rec_result)
 ```
 - text浜岃繘鍒舵暟鎹紝渚嬪锛氱敤鎴风洿鎺ヤ粠鏂囦欢閲岃鍑篵ytes鏁版嵁
 ```python
-rec_result = inference_pipline(text_in='鎴戜滑閮芥槸鏈ㄥご浜轰笉浼氳璇濅笉浼氬姩')
+rec_result = inference_pipeline(text_in='鎴戜滑閮芥槸鏈ㄥご浜轰笉浼氳璇濅笉浼氬姩')
 ```
 - text鏂囦欢url锛屼緥濡傦細https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_text/punc_example.txt
 ```python
-rec_result = inference_pipline(text_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_text/punc_example.txt')
+rec_result = inference_pipeline(text_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_text/punc_example.txt')
 ```
 
 #### [CT-Transformer Realtime model](https://www.modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727/summary)
@@ -53,15 +52,15 @@
 Full code of demo, please ref to [demo](https://github.com/alibaba-damo-academy/FunASR/discussions/238)
 
 
-#### API-reference
-##### Define pipeline
+### API-reference
+#### Define pipeline
 - `task`: `Tasks.punctuation`
-- `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk
+- `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk
 - `ngpu`: `1` (Default), decoding on GPU. If ngpu=0, decoding on CPU
 - `output_dir`: `None` (Default), the output path of results if set
 - `model_revision`: `None` (Default), setting the model version
 
-##### Infer pipeline
+#### Infer pipeline
 - `text_in`: the input to decode, which could be:
   - text bytes, `e.g.`: "鎴戜滑閮芥槸鏈ㄥご浜轰笉浼氳璇濅笉浼氬姩"
   - text file, `e.g.`: example/punc_example.txt
@@ -69,38 +68,37 @@
 - `param_dict`: reserving the cache which is necessary in realtime mode. 
 
 ### Inference with multi-thread CPUs or multi GPUs
-FunASR also offer recipes [egs_modelscope/punc/TEMPLATE/infer.sh](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/punc/TEMPLATE/infer.sh) to decode with multi-thread CPUs, or multi GPUs. It is an offline recipe and only support offline model.
+FunASR also offer recipes [egs_modelscope/punctuation/TEMPLATE/infer.sh](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/punctuation/TEMPLATE/infer.sh) to decode with multi-thread CPUs, or multi GPUs. It is an offline recipe and only support offline model.
 
-- Setting parameters in `infer.sh`
-    - `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk
-    - `data_dir`: the dataset dir needs to include `punc.txt`
-    - `output_dir`: output dir of the recognition results
-    - `gpu_inference`: `true` (Default), whether to perform gpu decoding, set false for CPU inference
-    - `gpuid_list`: `0,1` (Default), which gpu_ids are used to infer
-    - `njob`: only used for CPU inference (`gpu_inference`=`false`), `64` (Default), the number of jobs for CPU decoding
-    - `checkpoint_dir`: only used for infer finetuned models, the path dir of finetuned models
-    - `checkpoint_name`: only used for infer finetuned models, `punc.pb` (Default), which checkpoint is used to infer
+#### Settings of `infer.sh`
+- `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk
+- `data_dir`: the dataset dir needs to include `punc.txt`
+- `output_dir`: output dir of the recognition results
+- `gpu_inference`: `true` (Default), whether to perform gpu decoding, set false for CPU inference
+- `gpuid_list`: `0,1` (Default), which gpu_ids are used to infer
+- `njob`: only used for CPU inference (`gpu_inference`=`false`), `64` (Default), the number of jobs for CPU decoding
+- `checkpoint_dir`: only used for infer finetuned models, the path dir of finetuned models
+- `checkpoint_name`: only used for infer finetuned models, `punc.pb` (Default), which checkpoint is used to infer
 
-- Decode with multi GPUs:
+#### Decode with multi GPUs:
 ```shell
     bash infer.sh \
     --model "damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch" \
     --data_dir "./data/test" \
     --output_dir "./results" \
-    --batch_size 64 \
+    --batch_size 1 \
     --gpu_inference true \
     --gpuid_list "0,1"
 ```
-- Decode with multi-thread CPUs:
+#### Decode with multi-thread CPUs:
 ```shell
     bash infer.sh \
     --model "damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch" \
     --data_dir "./data/test" \
     --output_dir "./results" \
     --gpu_inference false \
-    --njob 64
+    --njob 1
 ```
-
 
 ## Finetune with pipeline
 

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