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https://github.com/geekwenjie/SmartJavaAI.git
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1、人脸模块：新增小视科技（MiniVision）活体检测模型
2、人脸模块：新增阿里通义工作室活体检测模型 3、人脸模块：新增2个表情识别模型 4、人脸模块：新增InsightFace、ElasticFace人脸识别模型 5、人脸模块：新增Seetaface6质量评估模型 6、目标检测模块：开放更多自定义模型参数 7、人脸模块：支持base64图片 8、实现接口 AutoCloseable，支持资源的自动释放 9、OCR模块：解决加方向矫正后无法连续识别bug 10、人脸模块：解决人脸更新后缓存问题 11、优化部分功能
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dengwenjie
7
examples/translation-example/.gitignore
vendored
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examples/translation-example/.gitignore
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.idea
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.idea/
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target
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log
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*.iml
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/.settings/
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/logging.file_IS_UNDEFINED/
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examples/translation-example/README.md
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examples/translation-example/README.md
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# 机器翻译示例
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## 📁 项目结构
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```
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└── main
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├── java
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│ └── smartai
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│ └── examples
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│ └── nlp
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│ └── translation 机器翻译
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│ └── TranslationDemo.java
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└── resources
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└── logback.xml
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```
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---
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## 🚀 快速开始
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1. 克隆项目到本地：
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2. 导入项目至 IntelliJ IDEA。
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3. 根据需要修改模型路径（见各 demo 中注释）。
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4. 运行对应的 JUnit 测试类方法即可体验各项功能。
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---
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## 📄 文档
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有关完整使用说明，请查阅 SmartJavaAI 官方文档：
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[http://doc.smartjavaai.cn](http://doc.smartjavaai.cn)
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---
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201
examples/translation-example/pom.xml
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examples/translation-example/pom.xml
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<?xml version="1.0" encoding="UTF-8"?>
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<project xmlns="http://maven.apache.org/POM/4.0.0"
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xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
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xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
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<modelVersion>4.0.0</modelVersion>
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<groupId>cn.smartjavaai</groupId>
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<artifactId>examples</artifactId>
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<version>1.0.0-SNAPSHOT</version>
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<properties>
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<maven.compiler.source>11</maven.compiler.source>
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<maven.compiler.target>11</maven.compiler.target>
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<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
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<smartjavaai.version>1.0.19</smartjavaai.version>
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<!--如果打包运行，需要替换成你的main-->
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<exec.mainClass>smartai.examples.nlp.translation.TranslationDemo</exec.mainClass>
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<javacv.version>1.5.10</javacv.version>
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<javacv.platform.macosx-arm64>macosx-arm64</javacv.platform.macosx-arm64>
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<javacv.platform.linux-x86_64>linux-x86_64</javacv.platform.linux-x86_64>
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<javacv.platform.linux-arm64>linux-arm64</javacv.platform.linux-arm64>
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<javacv.platform.windows-x86_64>windows-x86_64</javacv.platform.windows-x86_64>
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<djl.platform.windows-x86_64>win-x86_64</djl.platform.windows-x86_64>
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<djl.platform.linux-x86_64>linux-x86_64</djl.platform.linux-x86_64>
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<djl.platform.linux-aarch64>linux-aarch64</djl.platform.linux-aarch64>
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<djl.platform.osx-aarch64>osx-aarch64</djl.platform.osx-aarch64>
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</properties>
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<dependencyManagement>
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<dependencies>
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<dependency>
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<groupId>cn.smartjavaai</groupId>
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<artifactId>smartjavaai-bom</artifactId>
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<version>${smartjavaai.version}</version>
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<type>pom</type>
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<!-- 注意这里是import -->
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<scope>import</scope>
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</dependency>
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</dependencies>
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</dependencyManagement>
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<dependencies>
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<dependency>
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<groupId>commons-cli</groupId>
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<artifactId>commons-cli</artifactId>
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<version>1.9.0</version>
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</dependency>
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<dependency>
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<groupId>commons-io</groupId>
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<artifactId>commons-io</artifactId>
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<version>2.17.0</version>
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</dependency>
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<dependency>
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<groupId>org.apache.logging.log4j</groupId>
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<artifactId>log4j-slf4j2-impl</artifactId>
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<version>2.24.1</version>
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</dependency>
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<dependency>
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<groupId>org.testng</groupId>
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<artifactId>testng</artifactId>
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<version>7.10.2</version>
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<scope>test</scope>
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</dependency>
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<dependency>
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<groupId>ch.qos.logback</groupId>
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<artifactId>logback-classic</artifactId>
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<version>1.2.3</version>
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</dependency>
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<dependency>
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<groupId>org.slf4j</groupId>
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<artifactId>slf4j-api</artifactId>
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<version>1.7.30</version>
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</dependency>
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<dependency>
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<groupId>com.alibaba</groupId>
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<artifactId>fastjson</artifactId>
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<version>1.2.83</version>
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</dependency>
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<dependency>
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<groupId>junit</groupId>
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<artifactId>junit</artifactId>
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<version>4.13.2</version>
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</dependency>
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<!--翻译模块-->
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<dependency>
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<groupId>cn.smartjavaai</groupId>
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<artifactId>smartjavaai-translate</artifactId>
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</dependency>
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<dependency>
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<groupId>ai.djl.pytorch</groupId>
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<artifactId>pytorch-jni</artifactId>
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<version>2.5.1-0.32.0</version>
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<scope>runtime</scope>
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</dependency>
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<!-- windows平台 (保留对应平台的配置，可以减小包大小)-->
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<dependency>
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<groupId>ai.djl.pytorch</groupId>
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<artifactId>pytorch-native-cpu</artifactId>
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<classifier>${djl.platform.windows-x86_64}</classifier>
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<version>2.5.1</version>
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<scope>runtime</scope>
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</dependency>
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<!-- linux x86 平台 (保留对应平台的配置，可以减小包大小)-->
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<dependency>
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<groupId>ai.djl.pytorch</groupId>
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<artifactId>pytorch-native-cpu</artifactId>
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<classifier>${djl.platform.linux-x86_64}</classifier>
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<version>2.5.1</version>
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<scope>runtime</scope>
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</dependency>
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<!-- macOS M系列 平台 (保留对应平台的配置，可以减小包大小)-->
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<dependency>
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<groupId>ai.djl.pytorch</groupId>
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<artifactId>pytorch-native-cpu</artifactId>
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<classifier>${djl.platform.osx-aarch64}</classifier>
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<version>2.5.1</version>
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<scope>runtime</scope>
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</dependency>
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<!-- linux aarch64 平台 (保留对应平台的配置，可以减小包大小)-->
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<dependency>
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<groupId>ai.djl.pytorch</groupId>
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<artifactId>pytorch-native-cpu-precxx11</artifactId>
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<classifier>${djl.platform.linux-aarch64}</classifier>
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<version>2.5.1</version>
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<scope>runtime</scope>
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</dependency>
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</dependencies>
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<build>
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<plugins>
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<plugin>
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<groupId>org.apache.maven.plugins</groupId>
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<artifactId>maven-shade-plugin</artifactId>
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<version>3.5.0</version>
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<executions>
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<execution>
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<phase>package</phase>
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<goals><goal>shade</goal></goals>
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<configuration>
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<createDependencyReducedPom>false</createDependencyReducedPom>
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<transformers>
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<transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer"/>
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<transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
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<mainClass>${exec.mainClass}</mainClass>
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</transformer>
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</transformers>
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</configuration>
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</execution>
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</executions>
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</plugin>
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</plugins>
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</build>
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<repositories>
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<repository>
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<id>aliyunmaven</id>
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<name>阿里云公共仓库</name>
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<url>https://maven.aliyun.com/repository/public</url>
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<releases>
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<enabled>true</enabled>
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</releases>
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<snapshots>
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<enabled>false</enabled>
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</snapshots>
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</repository>
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</repositories>
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</project>
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@@ -0,0 +1,100 @@
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package smartai.examples.nlp.translation;
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import ai.djl.util.JsonUtils;
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import cn.smartjavaai.common.entity.R;
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import cn.smartjavaai.common.enums.DeviceEnum;
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import cn.smartjavaai.translation.config.TranslationModelConfig;
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import cn.smartjavaai.translation.entity.TranslateParam;
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import cn.smartjavaai.translation.enums.LanguageCode;
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import cn.smartjavaai.translation.enums.TranslationModeEnum;
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import cn.smartjavaai.translation.factory.TranslationModelFactory;
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import cn.smartjavaai.translation.model.TranslationModel;
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import lombok.Data;
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import lombok.extern.slf4j.Slf4j;
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import org.junit.Test;
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/**
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* 翻译Demo
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* 支持 Meta AI 开源的 NLLB-200 模型，实现 200 多种语言之间的高质量互译。
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* NLLB-200官网地址：https://github.com/facebookresearch/fairseq/tree/nllb
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* 模型下载地址：https://pan.baidu.com/s/1wf7btnb4cyBFv7DB7baHnw?pwd=1234 提取码: 1234
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* @author dwj
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*/
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@Slf4j
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public class TranslationDemo {
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/**
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* 翻译
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*/
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@Test
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public void translate() {
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try {
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TranslationModelConfig config = new TranslationModelConfig();
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//指定翻译模型：NLLB
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config.setModelEnum(TranslationModeEnum.NLLB_MODEL);
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//指定模型路径，需将模型路径修改为本地的模型路径
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config.setModelPath("/Users/xxx/Documents/develop/model/trans/traced_translation_cpu.pt");
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TranslationModel translationModel = TranslationModelFactory.getInstance().getModel(config);
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//翻译参数
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TranslateParam translateParam = new TranslateParam();
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//输入文字
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translateParam.setInput("你好，欢迎使用SmartJavaAI！");
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//源语言：中文
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translateParam.setSourceLanguage(LanguageCode.ZHO_HANS);
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//目标语言：英文
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translateParam.setTargetLanguage(LanguageCode.ENG_LATN);
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R<String> result = translationModel.translate(translateParam);
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if(result.isSuccess()){
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log.info("翻译结果：{}", result.getData());
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}else{
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log.error("翻译失败：{}", result.getMessage());
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}
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//目标语言：韩语
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translateParam.setTargetLanguage(LanguageCode.KOR_HANG);
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R<String> result2 = translationModel.translate(translateParam);
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if(result2.isSuccess()){
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log.info("翻译结果：{}", result2.getData());
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}else{
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log.error("翻译失败：{}", result2.getMessage());
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}
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} catch (Exception e) {
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e.printStackTrace();
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}
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}
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/**
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* GPU 翻译
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*/
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@Test
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public void translateGpu() {
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try {
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TranslationModelConfig config = new TranslationModelConfig();
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//指定翻译模型：NLLB
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config.setModelEnum(TranslationModeEnum.NLLB_MODEL);
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//指定设备：GPU
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config.setDevice(DeviceEnum.GPU);
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//指定模型路径，需将模型路径修改为本地的 GPU 模型路径
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config.setModelPath("/Users/xxx/Documents/develop/model/trans/traced_translation_gpu.pt");
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//获取翻译模型
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TranslationModel translationModel = TranslationModelFactory.getInstance().getModel(config);
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//翻译参数
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TranslateParam translateParam = new TranslateParam();
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//输入文字
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translateParam.setInput("你好，欢迎使用SmartJavaAI！");
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//源语言：中文
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translateParam.setSourceLanguage(LanguageCode.ZHO_HANS);
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//目标语言：韩语
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translateParam.setTargetLanguage(LanguageCode.ENG_LATN);
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R<String> result = translationModel.translate(translateParam);
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if(result.isSuccess()){
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log.info("翻译结果：{}", result.getData());
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}else{
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log.error("翻译失败：{}", result.getMessage());
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}
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} catch (Exception e) {
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e.printStackTrace();
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}
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}
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}
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@@ -0,0 +1,3 @@
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Manifest-Version: 1.0
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Main-Class: smartai.examples.face.SeetaFace6LinuxDemo
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14
examples/translation-example/src/main/resources/logback.xml
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14
examples/translation-example/src/main/resources/logback.xml
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<?xml version="1.0" encoding="UTF-8"?>
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<!-- 步骤2: 配置文件 (src/main/resources/logback.xml) -->
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<configuration scan="true" scanPeriod="30 seconds">
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<!-- 控制台日志输出 -->
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<appender name="CONSOLE" class="ch.qos.logback.core.ConsoleAppender">
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<encoder>
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<pattern>%d{yyyy-MM-dd HH:mm:ss.SSS} [%thread] %highlight(%-5level) %cyan(%logger{36}) - %msg%n</pattern>
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</encoder>
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</appender>
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<root level="DEBUG">
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<appender-ref ref="CONSOLE" />
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</root>
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</configuration>
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Reference in New Issue
Block a user