From 30aa982bf29ceefaf52c0013c12c19adc57dea0e Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 27 四月 2023 21:11:04 +0800
Subject: [PATCH] docs
---
egs_modelscope/punctuation/TEMPLATE/README.md | 21 ++++++++++-----------
1 files changed, 10 insertions(+), 11 deletions(-)
diff --git a/egs_modelscope/punctuation/TEMPLATE/README.md b/egs_modelscope/punctuation/TEMPLATE/README.md
index 5618973..3eaf68a 100644
--- a/egs_modelscope/punctuation/TEMPLATE/README.md
+++ b/egs_modelscope/punctuation/TEMPLATE/README.md
@@ -1,5 +1,4 @@
# 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.
@@ -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
- `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
@@ -69,7 +68,7 @@
- `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
@@ -87,7 +86,7 @@
--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"
```
@@ -98,7 +97,7 @@
--data_dir "./data/test" \
--output_dir "./results" \
--gpu_inference false \
- --njob 64
+ --njob 1
```
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