批量请求（高级）

借助批量翻译，您可以通过离线命令将大量文本（一次最多可批量处理 100 个文件）翻译为多达 10 种不同的目标语言。总内容大小应小于等于 100M Unicode 代码点，并且必须使用 UTF-8 编码。

准备工作

在开始使用 Cloud Translation API 之前，您必须具有启用了 Cloud Translation API 的项目，并且必须具有适当的凭据。您还可以安装常用编程语言的客户端库，以便调用 API。如需了解详情，请参阅设置页面。

权限

对于批量翻译，除了 Cloud Translation 权限之外，您还必须具有 Cloud Storage 存储分区的访问权限。从 Cloud Storage 存储分区读取批量翻译输入文件，并将输出文件写入 Cloud Storage 存储分区。例如，要从存储分区读取输入文件，您必须至少拥有存储分区的读取对象权限（由角色 roles/storage.objectViewer 提供）。如需详细了解 Cloud Storage 角色，请参阅 Cloud Storage 文档。

输入文件

仅支持两种 MIME 类型：text/html (HTML) 和 text/plain（.tsv 和 .txt）。

使用 TSV 文件

如果文件扩展名为 TSV，那么该文件可以包含一列或两列。第一列（可选）是文本请求的 ID。如果缺少第一列，Google 会将输入文件中的行号（从 0 开始）用作输出文件中的 ID。第二列是实际要翻译的文本。为了获得最佳结果，每行应小于或等于 10K Unicode 代码点，否则可能会返回错误。

使用文本或 HTML

其他受支持的文件扩展名为文本文件 (.txt) 或 HTML，此类文件会被视为单个大文本块。

批量请求

借助批量翻译请求，您可以提供包含要翻译内容的输入配置文件 (InputConfig) 的路径，以及最终译文所在输出位置 (OutputConfig) 的路径。您至少需要两个不同的 Cloud Storage 存储分区。源存储分区包含要翻译的内容，目标存储分区将包含生成的翻译文件。在翻译过程开始之前，目标文件夹必须是空的。

在处理请求时，我们会将结果实时写入输出位置。即使您中途取消请求，系统仍会在输出 Cloud Storage 位置生成输入文件级部分输出。因此，翻译的字符数仍会计费。

REST

以下示例展示了发送给系统进行翻译的两个输入文件。

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 项目的数字或字母数字 ID

HTTP 方法和网址：

POST https://translation.googleapis.com/v3/projects/PROJECT_NUMBER_OR_ID/locations/us-central1:batchTranslateText

请求 JSON 正文：

{
  "sourceLanguageCode": "en",
  "targetLanguageCodes": ["es", "fr"],
  "inputConfigs": [
   {
      "gcsSource": {
        "inputUri": "gs://bucket-name-source/input-file-name1"
      }
    },
    {
      "gcsSource": {
        "inputUri": "gs://bucket-name-source/input-file-name2"
      }
    }
  ],
  "outputConfig": {
      "gcsDestination": {
        "outputUriPrefix": "gs://bucket-name-destination/"
      }
   }
}

如需发送您的请求，请展开以下选项之一：

您应该收到类似以下内容的 JSON 响应：

{
  "name": "projects/project-number/locations/us-central1/operations/20191107-08251564068323-5d3895ce-0000-2067-864c-001a1136fb06",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.translation.v3.BatchTranslateMetadata",
    "state": "RUNNING"
  }
}
响应包含长时间运行的操作的 ID。

Go

试用此示例之前，请按照 Cloud Translation 快速入门：使用客户端库中的 Go 设置说明进行操作。如需了解详情，请参阅 Cloud Translation Go API 参考文档。

如需向 Cloud Translation 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。 

import (
	"context"
	"fmt"
	"io"

	translate "cloud.google.com/go/translate/apiv3"
	"cloud.google.com/go/translate/apiv3/translatepb"
)

// batchTranslateText translates a large volume of text in asynchronous batch mode.
func batchTranslateText(w io.Writer, projectID string, location string, inputURI string, outputURI string, sourceLang string, targetLang string) error {
	// projectID := "my-project-id"
	// location := "us-central1"
	// inputURI := "gs://cloud-samples-data/text.txt"
	// outputURI := "gs://YOUR_BUCKET_ID/path_to_store_results/"
	// sourceLang := "en"
	// targetLang := "ja"

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

	req := &translatepb.BatchTranslateTextRequest{
		Parent:              fmt.Sprintf("projects/%s/locations/%s", projectID, location),
		SourceLanguageCode:  sourceLang,
		TargetLanguageCodes: []string{targetLang},
		InputConfigs: []*translatepb.InputConfig{
			{
				Source: &translatepb.InputConfig_GcsSource{
					GcsSource: &translatepb.GcsSource{InputUri: inputURI},
				},
				// Optional. Can be "text/plain" or "text/html".
				MimeType: "text/plain",
			},
		},
		OutputConfig: &translatepb.OutputConfig{
			Destination: &translatepb.OutputConfig_GcsDestination{
				GcsDestination: &translatepb.GcsDestination{
					OutputUriPrefix: outputURI,
				},
			},
		},
	}

	// The BatchTranslateText operation is async.
	op, err := client.BatchTranslateText(ctx, req)
	if err != nil {
		return fmt.Errorf("BatchTranslateText: %w", err)
	}
	fmt.Fprintf(w, "Processing operation name: %q\n", op.Name())

	resp, err := op.Wait(ctx)
	if err != nil {
		return fmt.Errorf("Wait: %w", err)
	}

	fmt.Fprintf(w, "Total characters: %v\n", resp.GetTotalCharacters())
	fmt.Fprintf(w, "Translated characters: %v\n", resp.GetTranslatedCharacters())

	return nil
}

Java

试用此示例之前，请按照 Cloud Translation 快速入门：使用客户端库中的 Java 设置说明进行操作。如需了解详情，请参阅 Cloud Translation Java API 参考文档。

如需向 Cloud Translation 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。 

import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.translate.v3.BatchTranslateMetadata;
import com.google.cloud.translate.v3.BatchTranslateResponse;
import com.google.cloud.translate.v3.BatchTranslateTextRequest;
import com.google.cloud.translate.v3.GcsDestination;
import com.google.cloud.translate.v3.GcsSource;
import com.google.cloud.translate.v3.InputConfig;
import com.google.cloud.translate.v3.LocationName;
import com.google.cloud.translate.v3.OutputConfig;
import com.google.cloud.translate.v3.TranslationServiceClient;
import java.io.IOException;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.ThreadLocalRandom;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;

public class BatchTranslateText {

  public static void batchTranslateText()
      throws InterruptedException, ExecutionException, IOException, TimeoutException {
    // 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 inputUri = "gs://your-gcs-bucket/path/to/input/file.txt";
    String outputUri = "gs://your-gcs-bucket/path/to/results/";
    batchTranslateText(projectId, sourceLanguage, targetLanguage, inputUri, outputUri);
  }

  // Batch translate text
  public static void batchTranslateText(
      String projectId,
      String sourceLanguage,
      String targetLanguage,
      String inputUri,
      String outputUri)
      throws IOException, ExecutionException, InterruptedException, TimeoutException {

    // 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: `us-central1`
      LocationName parent = LocationName.of(projectId, "us-central1");

      GcsSource gcsSource = GcsSource.newBuilder().setInputUri(inputUri).build();
      // Supported Mime Types: https://cloud.google.com/translate/docs/supported-formats
      InputConfig inputConfig =
          InputConfig.newBuilder().setGcsSource(gcsSource).setMimeType("text/plain").build();

      GcsDestination gcsDestination =
          GcsDestination.newBuilder().setOutputUriPrefix(outputUri).build();
      OutputConfig outputConfig =
          OutputConfig.newBuilder().setGcsDestination(gcsDestination).build();

      BatchTranslateTextRequest request =
          BatchTranslateTextRequest.newBuilder()
              .setParent(parent.toString())
              .setSourceLanguageCode(sourceLanguage)
              .addTargetLanguageCodes(targetLanguage)
              .addInputConfigs(inputConfig)
              .setOutputConfig(outputConfig)
              .build();

      OperationFuture<BatchTranslateResponse, BatchTranslateMetadata> future =
          client.batchTranslateTextAsync(request);

      System.out.println("Waiting for operation to complete...");

      // random number between 300 - 450 (maximum allowed seconds)
      long randomNumber = ThreadLocalRandom.current().nextInt(450, 600);
      BatchTranslateResponse response = future.get(randomNumber, TimeUnit.SECONDS);

      System.out.printf("Total Characters: %s\n", response.getTotalCharacters());
      System.out.printf("Translated Characters: %s\n", response.getTranslatedCharacters());
    }
  }
}

Node.js

试用此示例之前，请按照 Cloud Translation 快速入门：使用客户端库中的 Node.js 设置说明进行操作。如需了解详情，请参阅 Cloud Translation Node.js API 参考文档。

如需向 Cloud Translation 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。 

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const projectId = 'YOUR_PROJECT_ID';
// const location = 'us-central1';
// const inputUri = 'gs://cloud-samples-data/text.txt';
// const outputUri = 'gs://YOUR_BUCKET_ID/path_to_store_results/';

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

// Instantiates a client
const translationClient = new TranslationServiceClient();
async function batchTranslateText() {
  // Construct request
  const request = {
    parent: `projects/${projectId}/locations/${location}`,
    sourceLanguageCode: 'en',
    targetLanguageCodes: ['ja'],
    inputConfigs: [
      {
        mimeType: 'text/plain', // mime types: text/plain, text/html
        gcsSource: {
          inputUri: inputUri,
        },
      },
    ],
    outputConfig: {
      gcsDestination: {
        outputUriPrefix: outputUri,
      },
    },
  };

  // Setup timeout for long-running operation. Timeout specified in ms.
  const options = {timeout: 240000};
  // Batch translate text using a long-running operation with a timeout of 240000ms.
  const [operation] = await translationClient.batchTranslateText(
    request,
    options
  );

  // Wait for operation to complete.
  const [response] = await operation.promise();

  console.log(`Total Characters: ${response.totalCharacters}`);
  console.log(`Translated Characters: ${response.translatedCharacters}`);
}

batchTranslateText();

Python

试用此示例之前，请按照 Cloud Translation 快速入门：使用客户端库中的 Python 设置说明进行操作。如需了解详情，请参阅 Cloud Translation Python API 参考文档。

如需向 Cloud Translation 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。 

from google.cloud import translate

def batch_translate_text(
    input_uri: str = "gs://YOUR_BUCKET_ID/path/to/your/file.txt",
    output_uri: str = "gs://YOUR_BUCKET_ID/path/to/save/results/",
    project_id: str = "YOUR_PROJECT_ID",
    timeout: int = 180,
) -> translate.TranslateTextResponse:
    """Translates a batch of texts on GCS and stores the result in a GCS location.

    Args:
        input_uri: The input URI of the texts to be translated.
        output_uri: The output URI of the translated texts.
        project_id: The ID of the project that owns the destination bucket.
        timeout: The timeout for this batch translation operation.

    Returns:
        The translated texts.
    """

    client = translate.TranslationServiceClient()

    location = "us-central1"
    # Supported file types: https://cloud.google.com/translate/docs/supported-formats
    gcs_source = {"input_uri": input_uri}

    input_configs_element = {
        "gcs_source": gcs_source,
        "mime_type": "text/plain",  # Can be "text/plain" or "text/html".
    }
    gcs_destination = {"output_uri_prefix": output_uri}
    output_config = {"gcs_destination": gcs_destination}
    parent = f"projects/{project_id}/locations/{location}"

    # Supported language codes: https://cloud.google.com/translate/docs/languages
    operation = client.batch_translate_text(
        request={
            "parent": parent,
            "source_language_code": "en",
            "target_language_codes": ["ja"],  # Up to 10 language codes here.
            "input_configs": [input_configs_element],
            "output_config": output_config,
        }
    )

    print("Waiting for operation to complete...")
    response = operation.result(timeout)

    print(f"Total Characters: {response.total_characters}")
    print(f"Translated Characters: {response.translated_characters}")

    return response

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 Cloud 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 Cloud 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 Cloud Translation 参考文档。

使用 AutoML 模型发出批量请求

您可以对批量请求使用自定义模型。在很多情况下，翻译会涉及多种目标语言。

为目标语言指定 AutoML 模型

REST

以下示例展示了如何为目标语言指定自定义模型。

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 项目的数字或字母数字 ID

HTTP 方法和网址：

POST https://translation.googleapis.com/v3/projects/PROJECT_NUMBER_OR_ID/locations/us-central1:batchTranslateText

请求 JSON 正文：

{
  "models":{"es":"projects/PROJECT_NUMBER_OR_ID/locations/us-central1/models/model-id"},
  "sourceLanguageCode": "en",
  "targetLanguageCodes": ["es"],
  "inputConfigs": [
   {
      "gcsSource": {
        "inputUri": "gs://bucket-name-source/input-file-name1"
      }
    },
    {
      "gcsSource": {
        "inputUri": "gs://bucket-name-source/input-file-name2"
      }
    }
  ],
  "outputConfig": {
      "gcsDestination": {
        "outputUriPrefix": "gs://bucket-name-destination/"
      }
   }
}

如需发送您的请求，请展开以下选项之一：

您应该收到类似以下内容的 JSON 响应：

{
  "name": "projects/project-number/locations/us-central1/operations/20190725-08251564068323-5d3895ce-0000-2067-864c-001a1136fb06",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.translation.v3.BatchTranslateMetadata",
    "state": "RUNNING"
  }
}
响应包含长时间运行的操作的 ID。

Go

试用此示例之前，请按照 Cloud Translation 快速入门：使用客户端库中的 Go 设置说明进行操作。如需了解详情，请参阅 Cloud Translation Go API 参考文档。

如需向 Cloud Translation 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。 

import (
	"context"
	"fmt"
	"io"

	translate "cloud.google.com/go/translate/apiv3"
	"cloud.google.com/go/translate/apiv3/translatepb"
)

// batchTranslateTextWithModel translates a large volume of text in asynchronous batch mode.
func batchTranslateTextWithModel(w io.Writer, projectID string, location string, inputURI string, outputURI string, sourceLang string, targetLang string, modelID string) error {
	// projectID := "my-project-id"
	// location := "us-central1"
	// inputURI := "gs://cloud-samples-data/text.txt"
	// outputURI := "gs://YOUR_BUCKET_ID/path_to_store_results/"
	// sourceLang := "en"
	// targetLang := "de"
	// modelID := "your-model-id"

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

	req := &translatepb.BatchTranslateTextRequest{
		Parent:              fmt.Sprintf("projects/%s/locations/%s", projectID, location),
		SourceLanguageCode:  sourceLang,
		TargetLanguageCodes: []string{targetLang},
		InputConfigs: []*translatepb.InputConfig{
			{
				Source: &translatepb.InputConfig_GcsSource{
					GcsSource: &translatepb.GcsSource{InputUri: inputURI},
				},
				// Optional. Can be "text/plain" or "text/html".
				MimeType: "text/plain",
			},
		},
		OutputConfig: &translatepb.OutputConfig{
			Destination: &translatepb.OutputConfig_GcsDestination{
				GcsDestination: &translatepb.GcsDestination{
					OutputUriPrefix: outputURI,
				},
			},
		},
		Models: map[string]string{
			targetLang: fmt.Sprintf("projects/%s/locations/%s/models/%s", projectID, location, modelID),
		},
	}

	// The BatchTranslateText operation is async.
	op, err := client.BatchTranslateText(ctx, req)
	if err != nil {
		return fmt.Errorf("BatchTranslateText: %w", err)
	}
	fmt.Fprintf(w, "Processing operation name: %q\n", op.Name())

	resp, err := op.Wait(ctx)
	if err != nil {
		return fmt.Errorf("Wait: %w", err)
	}

	fmt.Fprintf(w, "Total characters: %v\n", resp.GetTotalCharacters())
	fmt.Fprintf(w, "Translated characters: %v\n", resp.GetTranslatedCharacters())

	return nil
}

Java

试用此示例之前，请按照 Cloud Translation 快速入门：使用客户端库中的 Java 设置说明进行操作。如需了解详情，请参阅 Cloud Translation Java API 参考文档。

如需向 Cloud Translation 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。 

import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.translate.v3.BatchTranslateMetadata;
import com.google.cloud.translate.v3.BatchTranslateResponse;
import com.google.cloud.translate.v3.BatchTranslateTextRequest;
import com.google.cloud.translate.v3.GcsDestination;
import com.google.cloud.translate.v3.GcsSource;
import com.google.cloud.translate.v3.InputConfig;
import com.google.cloud.translate.v3.LocationName;
import com.google.cloud.translate.v3.OutputConfig;
import com.google.cloud.translate.v3.TranslationServiceClient;
import java.io.IOException;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.ThreadLocalRandom;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;

public class BatchTranslateTextWithModel {

  public static void batchTranslateTextWithModel()
      throws InterruptedException, ExecutionException, IOException, TimeoutException {
    // 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 inputUri = "gs://your-gcs-bucket/path/to/input/file.txt";
    String outputUri = "gs://your-gcs-bucket/path/to/results/";
    String modelId = "YOUR-MODEL-ID";
    batchTranslateTextWithModel(
        projectId, sourceLanguage, targetLanguage, inputUri, outputUri, modelId);
  }

  // Batch translate text using AutoML Translation model
  public static void batchTranslateTextWithModel(
      String projectId,
      String sourceLanguage,
      String targetLanguage,
      String inputUri,
      String outputUri,
      String modelId)
      throws IOException, ExecutionException, InterruptedException, TimeoutException {

    // 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);

      // Configure the source of the file from a GCS bucket
      GcsSource gcsSource = GcsSource.newBuilder().setInputUri(inputUri).build();
      // Supported Mime Types: https://cloud.google.com/translate/docs/supported-formats
      InputConfig inputConfig =
          InputConfig.newBuilder().setGcsSource(gcsSource).setMimeType("text/plain").build();

      // Configure where to store the output in a GCS bucket
      GcsDestination gcsDestination =
          GcsDestination.newBuilder().setOutputUriPrefix(outputUri).build();
      OutputConfig outputConfig =
          OutputConfig.newBuilder().setGcsDestination(gcsDestination).build();

      // Configure the model used in the request
      String modelPath =
          String.format("projects/%s/locations/%s/models/%s", projectId, location, modelId);

      // Build the request that will be sent to the API
      BatchTranslateTextRequest request =
          BatchTranslateTextRequest.newBuilder()
              .setParent(parent.toString())
              .setSourceLanguageCode(sourceLanguage)
              .addTargetLanguageCodes(targetLanguage)
              .addInputConfigs(inputConfig)
              .setOutputConfig(outputConfig)
              .putModels(targetLanguage, modelPath)
              .build();

      // Start an asynchronous request
      OperationFuture<BatchTranslateResponse, BatchTranslateMetadata> future =
          client.batchTranslateTextAsync(request);

      System.out.println("Waiting for operation to complete...");

      // random number between 300 - 450 (maximum allowed seconds)
      long randomNumber = ThreadLocalRandom.current().nextInt(450, 600);
      BatchTranslateResponse response = future.get(randomNumber, TimeUnit.SECONDS);

      // Display the translation for each input text provided
      System.out.printf("Total Characters: %s\n", response.getTotalCharacters());
      System.out.printf("Translated Characters: %s\n", response.getTranslatedCharacters());
    }
  }
}

Node.js

试用此示例之前，请按照 Cloud Translation 快速入门：使用客户端库中的 Node.js 设置说明进行操作。如需了解详情，请参阅 Cloud Translation Node.js API 参考文档。

如需向 Cloud Translation 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。 

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const projectId = 'YOUR_PROJECT_ID';
// const location = 'us-central1';
// const inputUri = 'gs://cloud-samples-data/text.txt';
// const outputUri = 'gs://YOUR_BUCKET_ID/path_to_store_results/';
// const modelId = 'YOUR_MODEL_ID';

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

// Instantiates a client
const client = new TranslationServiceClient();
async function batchTranslateTextWithModel() {
  // Construct request
  const request = {
    parent: `projects/${projectId}/locations/${location}`,
    sourceLanguageCode: 'en',
    targetLanguageCodes: ['ja'],
    inputConfigs: [
      {
        mimeType: 'text/plain', // mime types: text/plain, text/html
        gcsSource: {
          inputUri: inputUri,
        },
      },
    ],
    outputConfig: {
      gcsDestination: {
        outputUriPrefix: outputUri,
      },
    },
    models: {
      ja: `projects/${projectId}/locations/${location}/models/${modelId}`,
    },
  };

  const options = {timeout: 240000};
  // Create a job using a long-running operation
  const [operation] = await client.batchTranslateText(request, options);

  // Wait for the operation to complete
  const [response] = await operation.promise();

  // Display the translation for each input text provided
  console.log(`Total Characters: ${response.totalCharacters}`);
  console.log(`Translated Characters: ${response.translatedCharacters}`);
}

batchTranslateTextWithModel();

Python

试用此示例之前，请按照 Cloud Translation 快速入门：使用客户端库中的 Python 设置说明进行操作。如需了解详情，请参阅 Cloud Translation Python API 参考文档。

如需向 Cloud Translation 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。 

from google.cloud import translate

def batch_translate_text_with_model(
    input_uri: str = "gs://YOUR_BUCKET_ID/path/to/your/file.txt",
    output_uri: str = "gs://YOUR_BUCKET_ID/path/to/save/results/",
    project_id: str = "YOUR_PROJECT_ID",
    model_id: str = "YOUR_MODEL_ID",
) -> translate.TranslationServiceClient:
    """Batch translate text using Translation model.
    Model can be AutoML or General[built-in] model.

    Args:
        input_uri: The input file to translate.
        output_uri: The output file to save the translation results.
        project_id: The ID of the GCP project that owns the model.
        model_id: The model ID.

    Returns:
        The response from the batch translation API.
    """

    client = translate.TranslationServiceClient()

    # Supported file types: https://cloud.google.com/translate/docs/supported-formats
    gcs_source = {"input_uri": input_uri}
    location = "us-central1"

    input_configs_element = {
        "gcs_source": gcs_source,
        "mime_type": "text/plain",  # Can be "text/plain" or "text/html".
    }
    gcs_destination = {"output_uri_prefix": output_uri}
    output_config = {"gcs_destination": gcs_destination}
    parent = f"projects/{project_id}/locations/{location}"

    model_path = "projects/{}/locations/{}/models/{}".format(
        project_id, location, model_id  # The location of AutoML model.
    )

    # Supported language codes: https://cloud.google.com/translate/docs/languages
    models = {"ja": model_path}  # takes a target lang as key.

    operation = client.batch_translate_text(
        request={
            "parent": parent,
            "source_language_code": "en",
            "target_language_codes": ["ja"],  # Up to 10 language codes here.
            "input_configs": [input_configs_element],
            "output_config": output_config,
            "models": models,
        }
    )

    print("Waiting for operation to complete...")
    response = operation.result()

    # Display the translation for each input text provided.
    print(f"Total Characters: {response.total_characters}")
    print(f"Translated Characters: {response.translated_characters}")

    return response

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 Cloud 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 Cloud 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 Cloud Translation 参考文档。

为多种目标语言指定 AutoML 模型

REST

如果您有多种目标语言，则可以为每种目标语言指定自定义模型。

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 项目的数字或字母数字 ID

HTTP 方法和网址：

POST https://translation.googleapis.com/v3/projects/PROJECT_NUMBER_OR_ID/locations/us-central1:batchTranslateText

请求 JSON 正文：

{
  "models":{
    "es":"projects/PROJECT_NUMBER_OR_ID/locations/us-central1/models/model-id1",
    "fr":"projects/PROJECT_NUMBER_OR_ID/locations/us-central1/models/model-id2"},
  "sourceLanguageCode": "en",
  "targetLanguageCodes": ["es", "fr"],
  "inputConfigs": [
   {
      "gcsSource": {
        "inputUri": "gs://bucket-name-source/input-file-name1"
      }
    },
    {
      "gcsSource": {
        "inputUri": "gs://bucket-name-source/input-file-name2"
      }
    }
  ],
  "outputConfig": {
      "gcsDestination": {
        "outputUriPrefix": "gs://bucket-name-destination/"
      }
   }
 }

如需发送您的请求，请展开以下选项之一：

您应该收到类似以下内容的 JSON 响应：

{
  "name": "projects/project-number/locations/us-central1/operations/20191105-08251564068323-5d3895ce-0000-2067-864c-001a1136fb06",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.translation.v3.BatchTranslateMetadata",
    "state": "RUNNING"
  }
}
响应包含长时间运行的操作的 ID。

为某种目标语言指定 AutoML 模型，而不为其他目标语言指定 AutoML 模型

您可以为某种特定目标语言指定自定义模型，而不为其他目标语言指定模型。使用为多种目标语言指定自定义模型的代码，只需修改 models 字段以指定模型的目标语言（在以下示例中为 es），并且不指定 fr：

  • "models": {'es':'projects/PROJECT_NUMBER_OR_ID/locations/us-central1/models/model-id'},

其中 PROJECT_NUMBER_OR_ID 是您的 Google Cloud 项目编号或 ID，model-id 是您为 AutoML 模型指定的名称。

使用术语库翻译文本

REST

以下示例展示了如何为目标语言指定术语库。

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 项目的数字或字母数字 ID
  • glossary-id：您的术语库 ID，例如“ my-en-to-es-glossary”

HTTP 方法和网址：

POST https://translation.googleapis.com/v3/projects/PROJECT_NUMBER_OR_ID/locations/us-central1:batchTranslateText

请求 JSON 正文： 

{
  "sourceLanguageCode": "en",
  "targetLanguageCodes": ["es"],
  "glossaries": {
    "es": {
      "glossary": "projects/PROJECT_NUMBER_OR_ID/locations/us-central1/glossaries/glossary-id"
    }
  },
  "inputConfigs": [{
      "gcsSource": {
        "inputUri": "gs://bucket-name-source/input-file-name1"
      }
    },
    {
      "gcsSource": {
        "inputUri": "gs://bucket-name-source/input-file-name2"
      }
    }
  ],
  "outputConfig": {
    "gcsDestination": {
      "outputUriPrefix": "gs://bucket-name-destination/"
    }
  }
}

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 print-access-token)" \
     -H "x-goog-user-project: PROJECT_NUMBER_OR_ID" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://translation.googleapis.com/v3/projects/PROJECT_NUMBER_OR_ID/locations/us-central1:batchTranslateText"

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 print-access-token
$headers = @{ "Authorization" = "Bearer $cred"; "x-goog-user-project" = "PROJECT_NUMBER_OR_ID" }

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:batchTranslateText" | Select-Object -Expand Content

您应该收到类似以下内容的 JSON 响应：

{
  "name": "projects/project-number/locations/us-central1/operations/operation-id",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.translation.v3.BatchTranslateMetadata",
    "state": "RUNNING"
  }
}

Go

试用此示例之前，请按照 Cloud Translation 快速入门：使用客户端库中的 Go 设置说明进行操作。如需了解详情，请参阅 Cloud Translation Go API 参考文档。

如需向 Cloud Translation 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。 

import (
	"context"
	"fmt"
	"io"

	translate "cloud.google.com/go/translate/apiv3"
	"cloud.google.com/go/translate/apiv3/translatepb"
)

// batchTranslateTextWithGlossary translates a large volume of text in asynchronous batch mode.
func batchTranslateTextWithGlossary(w io.Writer, projectID string, location string, inputURI string, outputURI string, sourceLang string, targetLang string, glossaryID string) error {
	// projectID := "my-project-id"
	// location := "us-central1"
	// inputURI := "gs://cloud-samples-data/text.txt"
	// outputURI := "gs://YOUR_BUCKET_ID/path_to_store_results/"
	// sourceLang := "en"
	// targetLang := "ja"
	// glossaryID := "your-glossary-id"

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

	req := &translatepb.BatchTranslateTextRequest{
		Parent:              fmt.Sprintf("projects/%s/locations/%s", projectID, location),
		SourceLanguageCode:  sourceLang,
		TargetLanguageCodes: []string{targetLang},
		InputConfigs: []*translatepb.InputConfig{
			{
				Source: &translatepb.InputConfig_GcsSource{
					GcsSource: &translatepb.GcsSource{InputUri: inputURI},
				},
				// Optional. Can be "text/plain" or "text/html".
				MimeType: "text/plain",
			},
		},
		Glossaries: map[string]*translatepb.TranslateTextGlossaryConfig{
			targetLang: {
				Glossary: fmt.Sprintf("projects/%s/locations/%s/glossaries/%s", projectID, location, glossaryID),
			},
		},
		OutputConfig: &translatepb.OutputConfig{
			Destination: &translatepb.OutputConfig_GcsDestination{
				GcsDestination: &translatepb.GcsDestination{
					OutputUriPrefix: outputURI,
				},
			},
		},
	}

	// The BatchTranslateText operation is async.
	op, err := client.BatchTranslateText(ctx, req)
	if err != nil {
		return fmt.Errorf("BatchTranslateText: %w", err)
	}
	fmt.Fprintf(w, "Processing operation name: %q\n", op.Name())

	resp, err := op.Wait(ctx)
	if err != nil {
		return fmt.Errorf("Wait: %w", err)
	}

	fmt.Fprintf(w, "Total characters: %v\n", resp.GetTotalCharacters())
	fmt.Fprintf(w, "Translated characters: %v\n", resp.GetTranslatedCharacters())

	return nil
}

Java

试用此示例之前，请按照 Cloud Translation 快速入门：使用客户端库中的 Java 设置说明进行操作。如需了解详情，请参阅 Cloud Translation Java API 参考文档。

如需向 Cloud Translation 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。 

import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.translate.v3.BatchTranslateMetadata;
import com.google.cloud.translate.v3.BatchTranslateResponse;
import com.google.cloud.translate.v3.BatchTranslateTextRequest;
import com.google.cloud.translate.v3.GcsDestination;
import com.google.cloud.translate.v3.GcsSource;
import com.google.cloud.translate.v3.GlossaryName;
import com.google.cloud.translate.v3.InputConfig;
import com.google.cloud.translate.v3.LocationName;
import com.google.cloud.translate.v3.OutputConfig;
import com.google.cloud.translate.v3.TranslateTextGlossaryConfig;
import com.google.cloud.translate.v3.TranslationServiceClient;
import java.io.IOException;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.ThreadLocalRandom;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;

public class BatchTranslateTextWithGlossary {

  public static void batchTranslateTextWithGlossary()
      throws InterruptedException, ExecutionException, IOException, TimeoutException {
    // 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 inputUri = "gs://your-gcs-bucket/path/to/input/file.txt";
    String outputUri = "gs://your-gcs-bucket/path/to/results/";
    String glossaryId = "your-glossary-display-name";
    batchTranslateTextWithGlossary(
        projectId, sourceLanguage, targetLanguage, inputUri, outputUri, glossaryId);
  }

  // Batch Translate Text with a Glossary.
  public static void batchTranslateTextWithGlossary(
      String projectId,
      String sourceLanguage,
      String targetLanguage,
      String inputUri,
      String outputUri,
      String glossaryId)
      throws IOException, ExecutionException, InterruptedException, TimeoutException {

    // 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);

      // Configure the source of the file from a GCS bucket
      GcsSource gcsSource = GcsSource.newBuilder().setInputUri(inputUri).build();
      // Supported Mime Types: https://cloud.google.com/translate/docs/supported-formats
      InputConfig inputConfig =
          InputConfig.newBuilder().setGcsSource(gcsSource).setMimeType("text/plain").build();

      // Configure where to store the output in a GCS bucket
      GcsDestination gcsDestination =
          GcsDestination.newBuilder().setOutputUriPrefix(outputUri).build();
      OutputConfig outputConfig =
          OutputConfig.newBuilder().setGcsDestination(gcsDestination).build();

      // Configure the glossary used in the request
      GlossaryName glossaryName = GlossaryName.of(projectId, location, glossaryId);
      TranslateTextGlossaryConfig glossaryConfig =
          TranslateTextGlossaryConfig.newBuilder().setGlossary(glossaryName.toString()).build();

      // Build the request that will be sent to the API
      BatchTranslateTextRequest request =
          BatchTranslateTextRequest.newBuilder()
              .setParent(parent.toString())
              .setSourceLanguageCode(sourceLanguage)
              .addTargetLanguageCodes(targetLanguage)
              .addInputConfigs(inputConfig)
              .setOutputConfig(outputConfig)
              .putGlossaries(targetLanguage, glossaryConfig)
              .build();

      // Start an asynchronous request
      OperationFuture<BatchTranslateResponse, BatchTranslateMetadata> future =
          client.batchTranslateTextAsync(request);

      System.out.println("Waiting for operation to complete...");

      // random number between 300 - 450 (maximum allowed seconds)
      long randomNumber = ThreadLocalRandom.current().nextInt(450, 600);
      BatchTranslateResponse response = future.get(randomNumber, TimeUnit.SECONDS);

      // Display the translation for each input text provided
      System.out.printf("Total Characters: %s\n", response.getTotalCharacters());
      System.out.printf("Translated Characters: %s\n", response.getTranslatedCharacters());
    }
  }
}

Node.js

试用此示例之前，请按照 Cloud Translation 快速入门：使用客户端库中的 Node.js 设置说明进行操作。如需了解详情，请参阅 Cloud Translation Node.js API 参考文档。

如需向 Cloud Translation 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。 

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const projectId = 'YOUR_PROJECT_ID';
// const location = 'us-central1';
// const inputUri = 'gs://cloud-samples-data/text.txt';
// const outputUri = 'gs://YOUR_BUCKET_ID/path_to_store_results/';
// const glossaryId = 'YOUR_GLOSSARY_ID';

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

// Instantiates a client
const client = new TranslationServiceClient();
async function batchTranslateTextWithGlossary() {
  // Construct request
  const request = {
    parent: `projects/${projectId}/locations/${location}`,
    sourceLanguageCode: 'en',
    targetLanguageCodes: ['es'],
    inputConfigs: [
      {
        mimeType: 'text/plain', // mime types: text/plain, text/html
        gcsSource: {
          inputUri: inputUri,
        },
      },
    ],
    outputConfig: {
      gcsDestination: {
        outputUriPrefix: outputUri,
      },
    },
    glossaries: {
      es: {
        glossary: `projects/${projectId}/locations/${location}/glossaries/${glossaryId}`,
      },
    },
  };

  const options = {timeout: 240000};
  // Create a job using a long-running operation
  const [operation] = await client.batchTranslateText(request, options);

  // Wait for the operation to complete
  const [response] = await operation.promise();

  // Display the translation for each input text provided
  console.log(`Total Characters: ${response.totalCharacters}`);
  console.log(`Translated Characters: ${response.translatedCharacters}`);
}

batchTranslateTextWithGlossary();

Python

试用此示例之前，请按照 Cloud Translation 快速入门：使用客户端库中的 Python 设置说明进行操作。如需了解详情，请参阅 Cloud Translation Python API 参考文档。

如需向 Cloud Translation 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。 

from google.cloud import translate

def batch_translate_text_with_glossary(
    input_uri: str = "gs://YOUR_BUCKET_ID/path/to/your/file.txt",
    output_uri: str = "gs://YOUR_BUCKET_ID/path/to/save/results/",
    project_id: str = "YOUR_PROJECT_ID",
    glossary_id: str = "YOUR_GLOSSARY_ID",
    timeout: int = 320,
) -> translate.TranslateTextResponse:
    """Translates a batch of texts on GCS and stores the result in a GCS location.
    Glossary is applied for translation.

    Args:
        input_uri (str): The input file to translate.
        output_uri (str): The output file to save the translations to.
        project_id (str): The ID of the GCP project that owns the location.
        glossary_id (str): The ID of the glossary to use.
        timeout (int): The amount of time, in seconds, to wait for the operation to complete.

    Returns:
        The response from the batch.
    """

    client = translate.TranslationServiceClient()

    # Supported language codes: https://cloud.google.com/translate/docs/languages
    location = "us-central1"

    # Supported file types: https://cloud.google.com/translate/docs/supported-formats
    gcs_source = {"input_uri": input_uri}

    input_configs_element = {
        "gcs_source": gcs_source,
        "mime_type": "text/plain",  # Can be "text/plain" or "text/html".
    }
    gcs_destination = {"output_uri_prefix": output_uri}
    output_config = {"gcs_destination": gcs_destination}

    parent = f"projects/{project_id}/locations/{location}"

    # glossary is a custom dictionary Translation API uses
    # to translate the domain-specific terminology.
    glossary_path = client.glossary_path(
        project_id, "us-central1", glossary_id  # The location of the glossary
    )

    glossary_config = translate.TranslateTextGlossaryConfig(glossary=glossary_path)

    glossaries = {"ja": glossary_config}  # target lang as key

    operation = client.batch_translate_text(
        request={
            "parent": parent,
            "source_language_code": "en",
            "target_language_codes": ["ja"],  # Up to 10 language codes here.
            "input_configs": [input_configs_element],
            "glossaries": glossaries,
            "output_config": output_config,
        }
    )

    print("Waiting for operation to complete...")
    response = operation.result(timeout)

    print(f"Total Characters: {response.total_characters}")
    print(f"Translated Characters: {response.translated_characters}")

    return response

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 Cloud 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 Cloud 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 Cloud Translation 参考文档。

使用 AutoML Translation 自定义模型和术语库翻译文本

REST

以下示例展示了如何为目标语言指定自定义模型和术语库。

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 项目的数字或字母数字 ID

HTTP 方法和网址：

POST https://translation.googleapis.com/v3/projects/PROJECT_NUMBER_OR_ID/locations/us-central1:batchTranslateText

请求 JSON 正文： 

{
  "models": {
    "es": "projects/project_number_or_id/locations/us-central1/models/model-id"
  },
  "sourceLanguageCode": "en",
  "targetLanguageCodes": ["es"],
  "glossaries": {
    "es": {
      "glossary": "projects/project_number_or_id/locations/us-central1/glossaries/glossary-id"
    }
  },
  "inputConfigs": [{
      "gcsSource": {
        "inputUri": "gs://bucket-name-source/input-file-name"
      }
    },
    {
      "gcsSource": {
      "inputUri": "gs://bucket-name-source/input-file-name2"
      }
    }
  ],
  "outputConfig": {
    "gcsDestination": {
      "outputUriPrefix": "gs://bucket-name-destination/"
    }
  }
}

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 print-access-token)" \
     -H "x-goog-user-project: PROJECT_NUMBER_OR_ID" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://translation.googleapis.com/v3/projects/PROJECT_NUMBER_OR_ID/locations/us-central1:batchTranslateText"

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 print-access-token
$headers = @{ "Authorization" = "Bearer $cred"; "x-goog-user-project" = "PROJECT_NUMBER_OR_ID" }

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:batchTranslateText" | Select-Object -Expand Content

您应该收到类似以下内容的 JSON 响应：

{
  "name": "projects/project-number/locations/us-central1/operations/operation-id",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.translation.v3.BatchTranslateMetadata",
    "state": "RUNNING"
  }
}

Go

试用此示例之前，请按照 Cloud Translation 快速入门：使用客户端库中的 Go 设置说明进行操作。如需了解详情，请参阅 Cloud Translation Go API 参考文档。

如需向 Cloud Translation 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。 

import (
	"context"
	"fmt"
	"io"

	translate "cloud.google.com/go/translate/apiv3"
	"cloud.google.com/go/translate/apiv3/translatepb"
)

// batchTranslateTextWithGlossaryAndModel translates a large volume of text in asynchronous batch mode.
func batchTranslateTextWithGlossaryAndModel(w io.Writer, projectID string, location string, inputURI string, outputURI string, sourceLang string, targetLang string, glossaryID string, modelID string) error {
	// projectID := "my-project-id"
	// location := "us-central1"
	// inputURI := "gs://cloud-samples-data/text.txt"
	// outputURI := "gs://YOUR_BUCKET_ID/path_to_store_results/"
	// sourceLang := "en"
	// targetLang := "ja"
	// glossaryID := "your-glossary-id"
	// modelID := "your-model-id"

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

	req := &translatepb.BatchTranslateTextRequest{
		Parent:              fmt.Sprintf("projects/%s/locations/%s", projectID, location),
		SourceLanguageCode:  sourceLang,
		TargetLanguageCodes: []string{targetLang},
		InputConfigs: []*translatepb.InputConfig{
			{
				Source: &translatepb.InputConfig_GcsSource{
					GcsSource: &translatepb.GcsSource{InputUri: inputURI},
				},
				// Optional. Can be "text/plain" or "text/html".
				MimeType: "text/plain",
			},
		},
		Glossaries: map[string]*translatepb.TranslateTextGlossaryConfig{
			targetLang: {
				Glossary: fmt.Sprintf("projects/%s/locations/%s/glossaries/%s", projectID, location, glossaryID),
			},
		},
		OutputConfig: &translatepb.OutputConfig{
			Destination: &translatepb.OutputConfig_GcsDestination{
				GcsDestination: &translatepb.GcsDestination{
					OutputUriPrefix: outputURI,
				},
			},
		},
		Models: map[string]string{
			targetLang: fmt.Sprintf("projects/%s/locations/%s/models/%s", projectID, location, modelID),
		},
	}

	// The BatchTranslateText operation is async.
	op, err := client.BatchTranslateText(ctx, req)
	if err != nil {
		return fmt.Errorf("BatchTranslateText: %w", err)
	}
	fmt.Fprintf(w, "Processing operation name: %q\n", op.Name())

	resp, err := op.Wait(ctx)
	if err != nil {
		return fmt.Errorf("Wait: %w", err)
	}

	fmt.Fprintf(w, "Total characters: %v\n", resp.GetTotalCharacters())
	fmt.Fprintf(w, "Translated characters: %v\n", resp.GetTranslatedCharacters())

	return nil
}

Java

试用此示例之前，请按照 Cloud Translation 快速入门：使用客户端库中的 Java 设置说明进行操作。如需了解详情，请参阅 Cloud Translation Java API 参考文档。

如需向 Cloud Translation 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。 

import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.translate.v3.BatchTranslateMetadata;
import com.google.cloud.translate.v3.BatchTranslateResponse;
import com.google.cloud.translate.v3.BatchTranslateTextRequest;
import com.google.cloud.translate.v3.GcsDestination;
import com.google.cloud.translate.v3.GcsSource;
import com.google.cloud.translate.v3.GlossaryName;
import com.google.cloud.translate.v3.InputConfig;
import com.google.cloud.translate.v3.LocationName;
import com.google.cloud.translate.v3.OutputConfig;
import com.google.cloud.translate.v3.TranslateTextGlossaryConfig;
import com.google.cloud.translate.v3.TranslationServiceClient;
import java.io.IOException;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.ThreadLocalRandom;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;

public class BatchTranslateTextWithGlossaryAndModel {

  public static void batchTranslateTextWithGlossaryAndModel()
      throws InterruptedException, ExecutionException, IOException, TimeoutException {
    // 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 inputUri = "gs://your-gcs-bucket/path/to/input/file.txt";
    String outputUri = "gs://your-gcs-bucket/path/to/results/";
    String glossaryId = "your-glossary-display-name";
    String modelId = "YOUR-MODEL-ID";
    batchTranslateTextWithGlossaryAndModel(
        projectId, sourceLanguage, targetLanguage, inputUri, outputUri, glossaryId, modelId);
  }

  // Batch translate text with Model and Glossary
  public static void batchTranslateTextWithGlossaryAndModel(
      String projectId,
      String sourceLanguage,
      String targetLanguage,
      String inputUri,
      String outputUri,
      String glossaryId,
      String modelId)
      throws IOException, ExecutionException, InterruptedException, TimeoutException {

    // 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);

      // Configure the source of the file from a GCS bucket
      GcsSource gcsSource = GcsSource.newBuilder().setInputUri(inputUri).build();
      // Supported Mime Types: https://cloud.google.com/translate/docs/supported-formats
      InputConfig inputConfig =
          InputConfig.newBuilder().setGcsSource(gcsSource).setMimeType("text/plain").build();

      // Configure where to store the output in a GCS bucket
      GcsDestination gcsDestination =
          GcsDestination.newBuilder().setOutputUriPrefix(outputUri).build();
      OutputConfig outputConfig =
          OutputConfig.newBuilder().setGcsDestination(gcsDestination).build();

      // Configure the glossary used in the request
      GlossaryName glossaryName = GlossaryName.of(projectId, location, glossaryId);
      TranslateTextGlossaryConfig glossaryConfig =
          TranslateTextGlossaryConfig.newBuilder().setGlossary(glossaryName.toString()).build();

      // Configure the model used in the request
      String modelPath =
          String.format("projects/%s/locations/%s/models/%s", projectId, location, modelId);

      // Build the request that will be sent to the API
      BatchTranslateTextRequest request =
          BatchTranslateTextRequest.newBuilder()
              .setParent(parent.toString())
              .setSourceLanguageCode(sourceLanguage)
              .addTargetLanguageCodes(targetLanguage)
              .addInputConfigs(inputConfig)
              .setOutputConfig(outputConfig)
              .putGlossaries(targetLanguage, glossaryConfig)
              .putModels(targetLanguage, modelPath)
              .build();

      // Start an asynchronous request
      OperationFuture<BatchTranslateResponse, BatchTranslateMetadata> future =
          client.batchTranslateTextAsync(request);

      System.out.println("Waiting for operation to complete...");

      // random number between 300 - 450 (maximum allowed seconds)
      long randomNumber = ThreadLocalRandom.current().nextInt(450, 600);
      BatchTranslateResponse response = future.get(randomNumber, TimeUnit.SECONDS);

      // Display the translation for each input text provided
      System.out.printf("Total Characters: %s\n", response.getTotalCharacters());
      System.out.printf("Translated Characters: %s\n", response.getTranslatedCharacters());
    }
  }
}

Node.js

试用此示例之前，请按照 Cloud Translation 快速入门：使用客户端库中的 Node.js 设置说明进行操作。如需了解详情，请参阅 Cloud Translation Node.js API 参考文档。

如需向 Cloud Translation 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。 

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const projectId = 'YOUR_PROJECT_ID';
// const location = 'us-central1';
// const inputUri = 'gs://cloud-samples-data/text.txt';
// const outputUri = 'gs://YOUR_BUCKET_ID/path_to_store_results/';
// const glossaryId = 'YOUR_GLOSSARY_ID';
// const modelId = 'YOUR_MODEL_ID';

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

// Instantiates a client
const client = new TranslationServiceClient();
async function batchTranslateTextWithGlossaryAndModel() {
  // Construct request
  const request = {
    parent: `projects/${projectId}/locations/${location}`,
    sourceLanguageCode: 'en',
    targetLanguageCodes: ['ja'],
    inputConfigs: [
      {
        mimeType: 'text/plain', // mime types: text/plain, text/html
        gcsSource: {
          inputUri: inputUri,
        },
      },
    ],
    outputConfig: {
      gcsDestination: {
        outputUriPrefix: outputUri,
      },
    },
    glossaries: {
      ja: {
        glossary: `projects/${projectId}/locations/${location}/glossaries/${glossaryId}`,
      },
    },
    models: {
      ja: `projects/${projectId}/locations/${location}/models/${modelId}`,
    },
  };

  const options = {timeout: 240000};
  // Create a job using a long-running operation
  const [operation] = await client.batchTranslateText(request, options);

  // Wait for operation to complete
  const [response] = await operation.promise();

  // Display the translation for each input text provided
  console.log(`Total Characters: ${response.totalCharacters}`);
  console.log(`Translated Characters: ${response.translatedCharacters}`);
}

batchTranslateTextWithGlossaryAndModel();

Python

试用此示例之前，请按照 Cloud Translation 快速入门：使用客户端库中的 Python 设置说明进行操作。如需了解详情，请参阅 Cloud Translation Python API 参考文档。

如需向 Cloud Translation 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。 

from google.cloud import translate

def batch_translate_text_with_glossary_and_model(
    input_uri: str,
    output_uri: str,
    project_id: str,
    model_id: str,
    glossary_id: str,
) -> translate.TranslateTextResponse:
    """Batch translate text with Glossary and Translation model.
    Args:
        input_uri: The input text to be translated.
        output_uri: The output text to be translated.
        project_id: The ID of the GCP project that owns the model.
        model_id: The ID of the model
        glossary_id: The ID of the glossary

    Returns:
        The translated text.
    """

    client = translate.TranslationServiceClient()

    # Supported language codes: https://cloud.google.com/translate/docs/languages
    location = "us-central1"

    target_language_codes = ["ja"]
    gcs_source = {"input_uri": input_uri}

    # Optional. Can be "text/plain" or "text/html".
    mime_type = "text/plain"
    input_configs_element = {"gcs_source": gcs_source, "mime_type": mime_type}
    input_configs = [input_configs_element]
    gcs_destination = {"output_uri_prefix": output_uri}
    output_config = {"gcs_destination": gcs_destination}
    parent = f"projects/{project_id}/locations/{location}"
    model_path = "projects/{}/locations/{}/models/{}".format(
        project_id, "us-central1", model_id
    )
    models = {"ja": model_path}

    glossary_path = client.glossary_path(
        project_id, "us-central1", glossary_id  # The location of the glossary
    )

    glossary_config = translate.TranslateTextGlossaryConfig(glossary=glossary_path)
    glossaries = {"ja": glossary_config}  # target lang as key

    operation = client.batch_translate_text(
        request={
            "parent": parent,
            "source_language_code": "en",
            "target_language_codes": target_language_codes,
            "input_configs": input_configs,
            "output_config": output_config,
            "models": models,
            "glossaries": glossaries,
        }
    )

    print("Waiting for operation to complete...")
    response = operation.result()

    # Display the translation for each input text provided
    print(f"Total Characters: {response.total_characters}")
    print(f"Translated Characters: {response.translated_characters}")

    return response

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 Cloud 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 Cloud 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 Cloud Translation 参考文档。

操作状态

批量请求属于一项长时间运行的操作，可能需要大量时间才能完成。您可以轮询此操作的状态以查看它是否已完成，也可以取消此操作。

如需了解详情，请参阅长时间运行的操作。

其他资源

  • 如需有关解决常见问题或错误的帮助，请参阅问题排查页面。