翻译内容

使用 Cloud Translation 高级版 API

成功训练模型后,您可以使用 Cloud Translation 高级版 API translateText 方法翻译内容。Cloud Translation 高级版 API 支持使用词汇表批量翻译请求

REST 和命令行

您必须确保已为项目启用了 Cloud AutoML API。在将 AutoML 模型与 AutoML API 搭配使用时,必须确保满足此条件。请参阅此使用入门文档,以启用 API。

在使用任何请求数据之前,请先进行以下替换:

  • project-number-or-id:您的 Google Cloud 项目编号或 ID

HTTP 方法和网址:

POST https://translation.googleapis.com/v3/projects/project-number-or-id/locations/us-central1:translateText

请求 JSON 正文:

{
  "model": "projects/project-number-or-id/locations/us-central1/models/TRL1395675701985363739",
  "sourceLanguageCode": "en",
  "targetLanguageCode": "ru",
  "contents": ["Dr. Watson, please discard your trash. You've shared unsolicited email with me.
  Let's talk about spam and importance ranking in a confidential mode."]
}

如需发送请求,请选择以下方式之一:

curl

将请求正文保存在名为 request.json 的文件中,然后执行以下命令:

curl -X POST \
-H "Authorization: Bearer "$(gcloud auth application-default print-access-token) \
-H "Content-Type: application/json; charset=utf-8" \
-d @request.json \
"https://translation.googleapis.com/v3/projects/project-number-or-id/locations/us-central1:translateText "

PowerShell

将请求正文保存在名为 request.json 的文件中,然后执行以下命令:

$cred = gcloud auth application-default print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
-Method POST `
-Headers $headers `
-ContentType: "application/json; charset=utf-8" `
-InFile request.json `
-Uri "https://translation.googleapis.com/v3/projects/project-number-or-id/locations/us-central1:translateText " | Select-Object -Expand Content

您应该收到类似以下内容的 JSON 响应:

{
  "translation": {
    "translatedText": "Доктор Ватсон, пожалуйста, откажитесь от своего мусора.
    Вы поделились нежелательной электронной почтой со мной. Давайте поговорим о
    спаме и важности рейтинга в конфиденциальном режиме.",
    "model": "projects/project-number/locations/us-central1/models/TRL1395675701985363739"
  }
}

Go

import (
	"context"
	"fmt"
	"io"

	translate "cloud.google.com/go/translate/apiv3"
	translatepb "google.golang.org/genproto/googleapis/cloud/translate/v3"
)

// translateTextWithModel translates input text and returns translated text.
func translateTextWithModel(w io.Writer, projectID string, location string, sourceLang string, targetLang string, text string, modelID string) error {
	// projectID := "my-project-id"
	// location := "us-central1"
	// sourceLang := "en"
	// targetLang := "fr"
	// text := "Hello, world!"
	// modelID := "your-model-id"

	ctx := context.Background()
	client, err := translate.NewTranslationClient(ctx)
	if err != nil {
		return fmt.Errorf("NewTranslationClient: %v", err)
	}
	defer client.Close()

	req := &translatepb.TranslateTextRequest{
		Parent:             fmt.Sprintf("projects/%s/locations/%s", projectID, location),
		SourceLanguageCode: sourceLang,
		TargetLanguageCode: targetLang,
		MimeType:           "text/plain", // Mime types: "text/plain", "text/html"
		Contents:           []string{text},
		Model:              fmt.Sprintf("projects/%s/locations/%s/models/%s", projectID, location, modelID),
	}

	resp, err := client.TranslateText(ctx, req)
	if err != nil {
		return fmt.Errorf("TranslateText: %v", err)
	}

	// Display the translation for each input text provided
	for _, translation := range resp.GetTranslations() {
		fmt.Fprintf(w, "Translated text: %v\n", translation.GetTranslatedText())
	}

	return nil
}

Java

import com.google.cloud.translate.v3.LocationName;
import com.google.cloud.translate.v3.TranslateTextRequest;
import com.google.cloud.translate.v3.TranslateTextResponse;
import com.google.cloud.translate.v3.Translation;
import com.google.cloud.translate.v3.TranslationServiceClient;
import java.io.IOException;

public class TranslateTextWithModel {

  public static void translateTextWithModel() throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR-PROJECT-ID";
    // Supported Languages: https://cloud.google.com/translate/docs/languages
    String sourceLanguage = "your-source-language";
    String targetLanguage = "your-target-language";
    String text = "your-text";
    String modelId = "YOUR-MODEL-ID";
    translateTextWithModel(projectId, sourceLanguage, targetLanguage, text, modelId);
  }

  // Translating Text with Model
  public static void translateTextWithModel(
      String projectId, String sourceLanguage, String targetLanguage, String text, String modelId)
      throws IOException {

    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (TranslationServiceClient client = TranslationServiceClient.create()) {
      // Supported Locations: `global`, [glossary location], or [model location]
      // Glossaries must be hosted in `us-central1`
      // Custom Models must use the same location as your model. (us-central1)
      String location = "us-central1";
      LocationName parent = LocationName.of(projectId, location);
      String modelPath =
          String.format("projects/%s/locations/%s/models/%s", projectId, location, modelId);

      // Supported Mime Types: https://cloud.google.com/translate/docs/supported-formats
      TranslateTextRequest request =
          TranslateTextRequest.newBuilder()
              .setParent(parent.toString())
              .setMimeType("text/plain")
              .setSourceLanguageCode(sourceLanguage)
              .setTargetLanguageCode(targetLanguage)
              .addContents(text)
              .setModel(modelPath)
              .build();

      TranslateTextResponse response = client.translateText(request);

      // Display the translation for each input text provided
      for (Translation translation : response.getTranslationsList()) {
        System.out.printf("Translated text: %s\n", translation.getTranslatedText());
      }
    }
  }
}

Node.js

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const projectId = 'YOUR_PROJECT_ID';
// const location = 'us-central1';
// const modelId = 'YOUR_MODEL_ID';
// const text = 'text to translate';

// Imports the Google Cloud Translation library
const {TranslationServiceClient} = require('@google-cloud/translate');

// Instantiates a client
const translationClient = new TranslationServiceClient();
async function translateTextWithModel() {
  // Construct request
  const request = {
    parent: `projects/${projectId}/locations/${location}`,
    contents: [text],
    mimeType: 'text/plain', // mime types: text/plain, text/html
    sourceLanguageCode: 'en',
    targetLanguageCode: 'ja',
    model: `projects/${projectId}/locations/${location}/models/${modelId}`,
  };

  // Run request
  const [response] = await translationClient.translateText(request);

  for (const translation of response.translations) {
    console.log(`Translated Content: ${translation.translatedText}`);
  }
}

translateTextWithModel();

Python


from google.cloud import translate

def translate_text_with_model(
    text="YOUR_TEXT_TO_TRANSLATE",
    project_id="YOUR_PROJECT_ID",
    model_id="YOUR_MODEL_ID",
):
    """Translates a given text using Translation custom model."""

    client = translate.TranslationServiceClient()

    location = "us-central1"
    parent = f"projects/{project_id}/locations/{location}"
    model_path = f"{parent}/models/{model_id}"

    # Supported language codes: https://cloud.google.com/translate/docs/languages
    response = client.translate_text(
        request={
            "contents": [text],
            "target_language_code": "ja",
            "model": model_path,
            "source_language_code": "en",
            "parent": parent,
            "mime_type": "text/plain",  # mime types: text/plain, text/html
        }
    )
    # Display the translation for each input text provided
    for translation in response.translations:
        print("Translated text: {}".format(translation.translated_text))

其他语言

C#:请按照客户端库页面上的 C# 设置说明操作,然后访问 .NET 版 AutoML Translation 参考文档

PHP:请按照客户端库页面上的 PHP 设置说明操作,然后访问 PHP 版 AutoML Translation 参考文档

Ruby:请按照客户端库页面上的 Ruby 设置说明操作,然后访问 Ruby 版 AutoML Translation 参考文档

使用 AutoML Translation

您还可以使用 AutoML Translation 通过自定义模型来翻译内容。

网页界面

  1. 转到 Google Cloud Console 中的 AutoML Translation 模型页面。

  2. 如果您要使用的模型位于另一个项目中,请从标题栏中的项目选择器中选择该项目。

  3. 从模型列表中,选择用于翻译文本的模型。

  4. 点击模型的预测标签页。

  5. 在文本框中输入要翻译的内容,然后点击翻译

    您可以将自定义模型与基本模型(Google NMT 模型)的结果进行比较,默认情况下,Cloud Translation 高级版会使用基本模型。

REST 和命令行

在使用任何请求数据之前,请先进行以下替换:

  • model-name:模型的完整名称。模型的完整名称包括您的项目名称和位置。模型名称类似于以下示例:projects/project-id/locations/us-central1/models/model-id
  • source-language-text:要从源语言翻译为目标语言的文本

HTTP 方法和网址:

POST https://automl.googleapis.com/v1/model-name:predict

请求 JSON 正文:

{
  "payload" : {
     "textSnippet": {
        "content": "source-language-text"
      }
  }
}

如需发送请求,请选择以下方式之一:

curl

将请求正文保存在名为 request.json 的文件中,然后执行以下命令:

curl -X POST \
-H "Authorization: Bearer "$(gcloud auth application-default print-access-token) \
-H "Content-Type: application/json; charset=utf-8" \
-d @request.json \
"https://automl.googleapis.com/v1/model-name:predict"

PowerShell

将请求正文保存在名为 request.json 的文件中,然后执行以下命令:

$cred = gcloud auth application-default print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
-Method POST `
-Headers $headers `
-ContentType: "application/json; charset=utf-8" `
-InFile request.json `
-Uri "https://automl.googleapis.com/v1/model-name:predict" | Select-Object -Expand Content

您应该收到类似以下内容的 JSON 响应:

{
  "payload": [
    {
      "translation": {
        "translatedContent": {
          "content": "target-language-text"
        }
      }
    }
  ]
}

Go

import (
	"context"
	"fmt"
	"io"

	automl "cloud.google.com/go/automl/apiv1"
	automlpb "google.golang.org/genproto/googleapis/cloud/automl/v1"
)

// translatePredict does a prediction for translate.
func translatePredict(w io.Writer, projectID string, location string, modelID string, content string) error {
	// projectID := "my-project-id"
	// location := "us-central1"
	// modelID := "TRL123456789..."
	// content := "text to translate"

	ctx := context.Background()
	client, err := automl.NewPredictionClient(ctx)
	if err != nil {
		return fmt.Errorf("NewPredictionClient: %v", err)
	}
	defer client.Close()

	req := &automlpb.PredictRequest{
		Name: fmt.Sprintf("projects/%s/locations/%s/models/%s", projectID, location, modelID),
		Payload: &automlpb.ExamplePayload{
			Payload: &automlpb.ExamplePayload_TextSnippet{
				TextSnippet: &automlpb.TextSnippet{
					Content:  content,
					MimeType: "text/plain", // Types: "text/plain", "text/html"
				},
			},
		},
	}

	resp, err := client.Predict(ctx, req)
	if err != nil {
		return fmt.Errorf("Predict: %v", err)
	}

	for _, payload := range resp.GetPayload() {
		fmt.Fprintf(w, "Translated content: %v\n", payload.GetTranslation().GetTranslatedContent().GetContent())
	}

	return nil
}

Java

import com.google.cloud.automl.v1.ExamplePayload;
import com.google.cloud.automl.v1.ModelName;
import com.google.cloud.automl.v1.PredictRequest;
import com.google.cloud.automl.v1.PredictResponse;
import com.google.cloud.automl.v1.PredictionServiceClient;
import com.google.cloud.automl.v1.TextSnippet;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Paths;

class TranslatePredict {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR_PROJECT_ID";
    String modelId = "YOUR_MODEL_ID";
    String filePath = "path_to_local_file.txt";
    predict(projectId, modelId, filePath);
  }

  static void predict(String projectId, String modelId, String filePath) throws IOException {
    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (PredictionServiceClient client = PredictionServiceClient.create()) {
      // Get the full path of the model.
      ModelName name = ModelName.of(projectId, "us-central1", modelId);

      String content = new String(Files.readAllBytes(Paths.get(filePath)));

      TextSnippet textSnippet = TextSnippet.newBuilder().setContent(content).build();
      ExamplePayload payload = ExamplePayload.newBuilder().setTextSnippet(textSnippet).build();
      PredictRequest predictRequest =
          PredictRequest.newBuilder().setName(name.toString()).setPayload(payload).build();

      PredictResponse response = client.predict(predictRequest);
      TextSnippet translatedContent =
          response.getPayload(0).getTranslation().getTranslatedContent();
      System.out.format("Translated Content: %s\n", translatedContent.getContent());
    }
  }
}

Node.js

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const projectId = 'YOUR_PROJECT_ID';
// const location = 'us-central1';
// const modelId = 'YOUR_MODEL_ID';
// const filePath = 'path_to_local_file.txt';

// Imports the Google Cloud AutoML library
const {PredictionServiceClient} = require('@google-cloud/automl').v1;
const fs = require('fs');

// Instantiates a client
const client = new PredictionServiceClient();

// Read the file content for translation.
const content = fs.readFileSync(filePath, 'utf8');

async function predict() {
  // Construct request
  const request = {
    name: client.modelPath(projectId, location, modelId),
    payload: {
      textSnippet: {
        content: content,
      },
    },
  };

  const [response] = await client.predict(request);

  console.log(
    'Translated content: ',
    response.payload[0].translation.translatedContent.content
  );
}

predict();

Python

在运行此代码示例之前,您必须先安装 Python 客户端库

  • model_full_id 参数是模型的完整名称。例如:projects/434039606874/locations/us-central1/models/3745331181667467569
from google.cloud import automl

# TODO(developer): Uncomment and set the following variables
# project_id = "YOUR_PROJECT_ID"
# model_id = "YOUR_MODEL_ID"
# file_path = "path_to_local_file.txt"

prediction_client = automl.PredictionServiceClient()

# Get the full path of the model.
model_full_id = automl.AutoMlClient.model_path(project_id, "us-central1", model_id)

# Read the file content for translation.
with open(file_path, "rb") as content_file:
    content = content_file.read()
content.decode("utf-8")

text_snippet = automl.TextSnippet(content=content)
payload = automl.ExamplePayload(text_snippet=text_snippet)

response = prediction_client.predict(name=model_full_id, payload=payload)
translated_content = response.payload[0].translation.translated_content

print(u"Translated content: {}".format(translated_content.content))

其他语言

C#:请按照客户端库页面上的 C# 设置说明操作,然后访问 .NET 版 AutoML Translation 参考文档

PHP:请按照客户端库页面上的 PHP 设置说明操作,然后访问 PHP 版 AutoML Translation 参考文档

Ruby:请按照客户端库页面上的 Ruby 设置说明操作,然后访问 Ruby 版 AutoML Translation 参考文档