Translating content

Using Cloud Translation - Advanced API

After having successfully trained your model, you can translate content using the Cloud Translation - Advanced API translateText method. The Cloud Translation - Advanced API supports using glossaries and Batch translation requests.

REST

Note: The sample command below uses the gcloud auth application-default print-access-token command to obtain a valid authorization token for the request. Make sure that you have set the GOOGLE_APPLICATION_CREDENTIALS environment variable before you execute this command.

Make sure you have enabled the Cloud AutoML API for your project. This is necessary when using AutoML models with AutoML API. See this Getting started document to enable the API.

Before using any of the request data, make the following replacements:

• project-number-or-id: your Google Cloud project number or ID

HTTP method and URL:

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

Request JSON body:

{
  "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."]
}

To send your request, choose one of these options:

curl

Note: Ensure you have set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path for your service account private key file.

Save the request body in a file called request.json, and execute the following command:

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

Note: Ensure you have set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path for your service account private key file.

Save the request body in a file called request.json, and execute the following command:

$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

You should receive a JSON response similar to the following:

{
  "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

View on GitHub

import (
	"context"
	"fmt"
	"io"

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

// 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

View on GitHub

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

View on GitHub

/**
 * 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

View on GitHub

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))

Additional languages

C#: Please follow the C# setup instructions on the client libraries page and then visit the AutoML Translation reference documentation for .NET.

PHP: Please follow the PHP setup instructions on the client libraries page and then visit the AutoML Translation reference documentation for PHP.

Ruby: Please follow the Ruby setup instructions on the client libraries page and then visit the AutoML Translation reference documentation for Ruby.

Using AutoML Translation

You can also use AutoML Translation to translate content by using custom models.

Web UI

1. Go to the AutoML Translation models page in the Google Cloud console.

2. If the model that you want to use is located in another project, select the project from the project selector in the title bar.

3. From the list of models, select the one you will use to translate text.

4. Click the model's Predict tab.

5. In the text box, enter the content to translate and then click Translate.

   You can compare the results from your custom model to the base model (Google NMT model), which Cloud Translation - Advanced uses by default.

REST

Note: The sample command below uses the gcloud auth application-default print-access-token command to obtain a valid authorization token for the request. Make sure that you have set the GOOGLE_APPLICATION_CREDENTIALS environment variable before you execute this command.

Before using any of the request data, make the following replacements:

• model-name: the full name of your model. The full name of your model includes your project name and location. A model name looks similar to the following example: projects/project-id/locations/us-central1/models/model-id.
• source-language-text: the text that you want to translate from the source language to the target language
• project-id: your Google Cloud Platform project ID

HTTP method and URL:

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

Request JSON body:

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

To send your request, choose one of these options:

curl

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by executing gcloud init or gcloud auth login, or by using Cloud Shell, which automatically logs you into the gcloud CLI. You can check the currently active account by executing gcloud auth list.

Save the request body in a file called request.json, and execute the following command:

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

PowerShell

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by executing gcloud init or gcloud auth login, or by using Cloud Shell, which automatically logs you into the gcloud CLI. You can check the currently active account by executing gcloud auth list.

Save the request body in a file called request.json, and execute the following command:

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

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

You should receive a JSON response similar to the following:

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

Go

View on GitHub

import (
	"context"
	"fmt"
	"io"

	automl "cloud.google.com/go/automl/apiv1"
	"cloud.google.com/go/automl/apiv1/automlpb"
)

// 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

Open in Editor View on GitHub

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

Open in Editor View on GitHub

/**
 * 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

Before you can run this code example, you must install the Python Client Libraries.

• The model_full_id parameter is the full name of your model. For example: projects/434039606874/locations/us-central1/models/3745331181667467569.

View on GitHub

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))

Additional languages

C#: Please follow the C# setup instructions on the client libraries page and then visit the AutoML Translation reference documentation for .NET.

PHP: Please follow the PHP setup instructions on the client libraries page and then visit the AutoML Translation reference documentation for PHP.

Ruby: Please follow the Ruby setup instructions on the client libraries page and then visit the AutoML Translation reference documentation for Ruby.