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[to #43115513] update quick_start doc and add pai-easynlp back
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/9655518 * update quick_start doc and add pai-easynlp back * update doc with sndfile
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wenmeng.zwm
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# 快速开始
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ModelScope Library目前支持tensorflow，pytorch深度学习框架进行模型训练、推理， 在Python 3.7+, Pytorch 1.8+, Tensorflow1.15，Tensorflow 2.x上测试可运行。
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注： `语音相关`的功能仅支持 python3.7,tensorflow1.15的`linux`环境使用。 其他功能可以在windows、mac上安装使用。
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**注： **`**语音相关**`**的功能仅支持 python3.7,tensorflow1.15的**`**linux**`**环境使用。 其他功能可以在windows、mac上安装使用。**
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## python环境配置
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首先，参考[文档](https://docs.anaconda.com/anaconda/install/) 安装配置Anaconda环境
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首先，参考[文档](https://docs.anaconda.com/anaconda/install/) 安装配置Anaconda环境。
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安装完成后，执行如下命令为modelscope library创建对应的python环境。
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```shell
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conda create -n modelscope python=3.7
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conda activate modelscope
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```
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## 安装深度学习框架
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* 安装pytorch[参考链接](https://pytorch.org/get-started/locally/)
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- 安装pytorch[参考链接](https://pytorch.org/get-started/locally/)。
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```shell
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pip install torch torchvision
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pip3 install torch torchvision torchaudio
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```
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* 安装Tensorflow[参考链接](https://www.tensorflow.org/install/pip)
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- 安装Tensorflow[参考链接](https://www.tensorflow.org/install/pip)。
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```shell
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pip install --upgrade tensorflow
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```
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## ModelScope library 安装
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注： 如果在安装过程中遇到错误，请前往[常见问题](faq.md)查找解决方案。
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### pip安装
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执行如下命令：
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执行如下命令可以安装所有领域依赖：
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```shell
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pip install "modelscope[all]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
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pip install "modelscope[cv,nlp,audio,multi-modal]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
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```
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如需体验`语音功能`，请`额外`执行如下命令：
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如仅需体验`语音功能`，请执行如下命令：
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```shell
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pip install "modelscope[audio]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
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```
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如仅需体验CV功能，可执行如下命令安装依赖：
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```shell
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pip install "modelscope[cv]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
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```
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如仅需体验NLP功能，可执行如下命令安装依赖：
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```shell
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pip install "modelscope[nlp]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
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```
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如仅需体验多模态功能，可执行如下命令安装依赖：
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```shell
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pip install "modelscope[multi-modal]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
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```
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**注**：
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1. `**语音相关**`**的功能仅支持 python3.7,tensorflow1.15的**`**linux**`**环境使用。 其他功能可以在windows、mac上安装使用。**
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2. 语音领域中一部分模型使用了三方库SoundFile进行wav文件处理，**在Linux系统上用户需要手动安装SoundFile的底层依赖库libsndfile**，在Windows和MacOS上会自动安装不需要用户操作。详细信息可参考[SoundFile官网](https://github.com/bastibe/python-soundfile#installation)。以Ubuntu系统为>例，用户需要执行如下命令:
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```shell
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sudo apt-get update
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sudo apt-get install libsndfile1
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```
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3. **CV功能使用需要安装mmcv-full， 请参考mmcv**[**安装手册**](https://github.com/open-mmlab/mmcv#installation)**进行安装**
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### 使用源码安装
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适合本地开发调试使用，修改源码后可以直接执行
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下载源码可以直接clone代码到本地
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适合本地开发调试使用，修改源码后可以直接执行。
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ModelScope的源码可以直接clone到本地：
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```shell
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git clone git@gitlab.alibaba-inc.com:Ali-MaaS/MaaS-lib.git modelscope
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cd modelscope
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git fetch origin master
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git checkout master
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cd modelscope
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```
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安装依赖并设置PYTHONPATH
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```shell
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pip install -e ".[all]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
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export PYTHONPATH=`pwd`
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```
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注： 6.30版本需要把cv、nlp、multi-modal领域依赖都装上，7.30号各个领域依赖会作为选装，用户需要使用哪个领域安装对应领域依赖即可。
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如需使用语音功能，请执行如下命令安装语音功能所需依赖
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```shell
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pip install -e ".[audio]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo
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```
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安装依赖
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如需安装所有依赖，请执行如下命令
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```shell
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pip install -e ".[cv,nlp,audio,multi-modal]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
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```
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如需体验`语音功能`，请单独执行如下命令：
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```shell
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pip install -e ".[audio]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
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```
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如仅需体验CV功能，可执行如下命令安装依赖：
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```shell
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pip install -e ".[cv]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
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```
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如仅需体验NLP功能，可执行如下命令安装依赖：
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```shell
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pip install -e ".[nlp]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
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```
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如仅需体验多模态功能，可执行如下命令安装依赖：
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```shell
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pip install -e ".[multi-modal]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
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```
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###
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### 安装验证
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安装成功后，可以执行如下命令进行验证安装是否正确
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安装成功后，可以执行如下命令进行验证安装是否正确：
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```shell
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python -c "from modelscope.pipelines import pipeline;print(pipeline('word-segmentation')('今天天气不错，适合 出去游玩'))"
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{'output': '今天 天气 不错 ， 适合 出去 游玩'}
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```
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## 推理
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pipeline函数提供了简洁的推理接口，相关介绍和示例请参考[pipeline使用教程](tutorials/pipeline.md)
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## 训练 & 评估
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Trainer类提供了简洁的Finetuning和评估接口，相关介绍和示例请参考[Trainer使用教程](tutorials/trainer.md)
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@@ -1,8 +1,6 @@
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en_core_web_sm>=2.3.5
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fairseq>=0.10.2
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# temporarily remove pai-easynl due to its hard dependency scipy==1.5.4
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# will be added back
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# pai-easynlp
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pai-easynlp
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# rough-score was just recently updated from 0.0.4 to 0.0.7
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# which introduced compatability issues that are being investigated
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rouge_score<=0.0.4
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