# Scenarios for Amazon Translate using AWS SDKs The following code examples show you how to implement common scenarios in Amazon Translate with AWS SDKs. These scenarios show you how to accomplish specific tasks by calling multiple functions within Amazon Translate or combined with other AWS services. Each scenario includes a link to the complete source code, where you can find instructions on how to set up and run the code. Scenarios target an intermediate level of experience to help you understand service actions in context. **Topics** + [Build an Amazon Transcribe streaming app](example_cross_TranscriptionStreamingApp_section.md) + [Building an Amazon Lex chatbot](example_cross_LexChatbotLanguages_section.md) + [Building an Amazon SNS application](example_cross_SnsPublishSubscription_section.md) + [Create an application to analyze customer feedback](example_cross_FSA_section.md) + [Get started with translate jobs](example_translate_Scenario_GettingStarted_section.md) # Build an Amazon Transcribe streaming app The following code example shows how to build an app that records, transcribes, and translates live audio in real-time, and emails the results. ------ #### [ JavaScript ] **SDK for JavaScript (v3)** Shows how to use Amazon Transcribe to build an app that records, transcribes, and translates live audio in real-time, and emails the results using Amazon Simple Email Service (Amazon SES). For complete source code and instructions on how to set up and run, see the full example on [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javascriptv3/example_code/cross-services/transcribe-streaming-app). **Services used in this example** + Amazon Comprehend + Amazon SES + Amazon Transcribe + Amazon Translate ------ For a complete list of AWS SDK developer guides and code examples, see [Using this service with an AWS SDK](sdk-general-information-section.md). This topic also includes information about getting started and details about previous SDK versions. # Create an Amazon Lex chatbot to engage your website visitors The following code examples show how to create a chatbot to engage your website visitors. ------ #### [ Java ] **SDK for Java 2.x** Shows how to use the Amazon Lex API to create a Chatbot within a web application to engage your web site visitors. For complete source code and instructions on how to set up and run, see the full example on [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/usecases/creating_lex_chatbot). **Services used in this example** + Amazon Comprehend + Amazon Lex + Amazon Translate ------ #### [ JavaScript ] **SDK for JavaScript (v3)** Shows how to use the Amazon Lex API to create a Chatbot within a web application to engage your web site visitors. For complete source code and instructions on how to set up and run, see the full example [Building an Amazon Lex chatbot](https://docs.aws.amazon.com/sdk-for-javascript/v3/developer-guide/lex-bot-example.html) in the AWS SDK for JavaScript developer guide. **Services used in this example** + Amazon Comprehend + Amazon Lex + Amazon Translate ------ For a complete list of AWS SDK developer guides and code examples, see [Using this service with an AWS SDK](sdk-general-information-section.md). This topic also includes information about getting started and details about previous SDK versions. # Build a publish and subscription application that translates messages The following code examples show how to create an application that has subscription and publish functionality and translates messages. ------ #### [ .NET ] **SDK for .NET** Shows how to use the Amazon Simple Notification Service .NET API to create a web application that has subscription and publish functionality. In addition, this example application also translates messages. For complete source code and instructions on how to set up and run, see the full example on [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/cross-service/SubscribePublishTranslate). **Services used in this example** + Amazon SNS + Amazon Translate ------ #### [ Java ] **SDK for Java 2.x** Shows how to use the Amazon Simple Notification Service Java API to create a web application that has subscription and publish functionality. In addition, this example application also translates messages. For complete source code and instructions on how to set up and run, see the full example on [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/usecases/creating_sns_sample_app). For complete source code and instructions on how to set up and run the example that uses the Java Async API, see the full example on [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/usecases/creating_sns_async). **Services used in this example** + Amazon SNS + Amazon Translate ------ #### [ Kotlin ] **SDK for Kotlin** Shows how to use the Amazon SNS Kotlin API to create an application that has subscription and publish functionality. In addition, this example application also translates messages. For complete source code and instructions on how to create a web app, see the full example on [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/usecases/subpub_app). For complete source code and instructions on how to create a native Android app, see the full example on [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/usecases/subpub_app_android). **Services used in this example** + Amazon SNS + Amazon Translate ------ For a complete list of AWS SDK developer guides and code examples, see [Using this service with an AWS SDK](sdk-general-information-section.md). This topic also includes information about getting started and details about previous SDK versions. # Create an application that analyzes customer feedback and synthesizes audio The following code examples show how to create an application that analyzes customer comment cards, translates them from their original language, determines their sentiment, and generates an audio file from the translated text. ------ #### [ .NET ] **SDK for .NET** This example application analyzes and stores customer feedback cards. Specifically, it fulfills the need of a fictitious hotel in New York City. The hotel receives feedback from guests in various languages in the form of physical comment cards. That feedback is uploaded into the app through a web client. After an image of a comment card is uploaded, the following steps occur: + Text is extracted from the image using Amazon Textract. + Amazon Comprehend determines the sentiment of the extracted text and its language. + The extracted text is translated to English using Amazon Translate. + Amazon Polly synthesizes an audio file from the extracted text. The full app can be deployed with the AWS CDK. For source code and deployment instructions, see the project in [ GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/cross-service/FeedbackSentimentAnalyzer). **Services used in this example** + Amazon Comprehend + Lambda + Amazon Polly + Amazon Textract + Amazon Translate ------ #### [ Java ] **SDK for Java 2.x** This example application analyzes and stores customer feedback cards. Specifically, it fulfills the need of a fictitious hotel in New York City. The hotel receives feedback from guests in various languages in the form of physical comment cards. That feedback is uploaded into the app through a web client. After an image of a comment card is uploaded, the following steps occur: + Text is extracted from the image using Amazon Textract. + Amazon Comprehend determines the sentiment of the extracted text and its language. + The extracted text is translated to English using Amazon Translate. + Amazon Polly synthesizes an audio file from the extracted text. The full app can be deployed with the AWS CDK. For source code and deployment instructions, see the project in [ GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/usecases/creating_fsa_app). **Services used in this example** + Amazon Comprehend + Lambda + Amazon Polly + Amazon Textract + Amazon Translate ------ #### [ JavaScript ] **SDK for JavaScript (v3)** This example application analyzes and stores customer feedback cards. Specifically, it fulfills the need of a fictitious hotel in New York City. The hotel receives feedback from guests in various languages in the form of physical comment cards. That feedback is uploaded into the app through a web client. After an image of a comment card is uploaded, the following steps occur: + Text is extracted from the image using Amazon Textract. + Amazon Comprehend determines the sentiment of the extracted text and its language. + The extracted text is translated to English using Amazon Translate. + Amazon Polly synthesizes an audio file from the extracted text. The full app can be deployed with the AWS CDK. For source code and deployment instructions, see the project in [ GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javascriptv3/example_code/cross-services/feedback-sentiment-analyzer). The following excerpts show how the AWS SDK for JavaScript is used inside of Lambda functions. ``` import { ComprehendClient, DetectDominantLanguageCommand, DetectSentimentCommand, } from "@aws-sdk/client-comprehend"; /** * Determine the language and sentiment of the extracted text. * * @param {{ source_text: string}} extractTextOutput */ export const handler = async (extractTextOutput) => { const comprehendClient = new ComprehendClient({}); const detectDominantLanguageCommand = new DetectDominantLanguageCommand({ Text: extractTextOutput.source_text, }); // The source language is required for sentiment analysis and // translation in the next step. const { Languages } = await comprehendClient.send( detectDominantLanguageCommand, ); const languageCode = Languages[0].LanguageCode; const detectSentimentCommand = new DetectSentimentCommand({ Text: extractTextOutput.source_text, LanguageCode: languageCode, }); const { Sentiment } = await comprehendClient.send(detectSentimentCommand); return { sentiment: Sentiment, language_code: languageCode, }; }; ``` ``` import { DetectDocumentTextCommand, TextractClient, } from "@aws-sdk/client-textract"; /** * Fetch the S3 object from the event and analyze it using Amazon Textract. * * @param {import("@types/aws-lambda").EventBridgeEvent<"Object Created">} eventBridgeS3Event */ export const handler = async (eventBridgeS3Event) => { const textractClient = new TextractClient(); const detectDocumentTextCommand = new DetectDocumentTextCommand({ Document: { S3Object: { Bucket: eventBridgeS3Event.bucket, Name: eventBridgeS3Event.object, }, }, }); // Textract returns a list of blocks. A block can be a line, a page, word, etc. // Each block also contains geometry of the detected text. // For more information on the Block type, see https://docs.aws.amazon.com/textract/latest/dg/API_Block.html. const { Blocks } = await textractClient.send(detectDocumentTextCommand); // For the purpose of this example, we are only interested in words. const extractedWords = Blocks.filter((b) => b.BlockType === "WORD").map( (b) => b.Text, ); return extractedWords.join(" "); }; ``` ``` import { PollyClient, SynthesizeSpeechCommand } from "@aws-sdk/client-polly"; import { S3Client } from "@aws-sdk/client-s3"; import { Upload } from "@aws-sdk/lib-storage"; /** * Synthesize an audio file from text. * * @param {{ bucket: string, translated_text: string, object: string}} sourceDestinationConfig */ export const handler = async (sourceDestinationConfig) => { const pollyClient = new PollyClient({}); const synthesizeSpeechCommand = new SynthesizeSpeechCommand({ Engine: "neural", Text: sourceDestinationConfig.translated_text, VoiceId: "Ruth", OutputFormat: "mp3", }); const { AudioStream } = await pollyClient.send(synthesizeSpeechCommand); const audioKey = `${sourceDestinationConfig.object}.mp3`; // Store the audio file in S3. const s3Client = new S3Client(); const upload = new Upload({ client: s3Client, params: { Bucket: sourceDestinationConfig.bucket, Key: audioKey, Body: AudioStream, ContentType: "audio/mp3", }, }); await upload.done(); return audioKey; }; ``` ``` import { TranslateClient, TranslateTextCommand, } from "@aws-sdk/client-translate"; /** * Translate the extracted text to English. * * @param {{ extracted_text: string, source_language_code: string}} textAndSourceLanguage */ export const handler = async (textAndSourceLanguage) => { const translateClient = new TranslateClient({}); const translateCommand = new TranslateTextCommand({ SourceLanguageCode: textAndSourceLanguage.source_language_code, TargetLanguageCode: "en", Text: textAndSourceLanguage.extracted_text, }); const { TranslatedText } = await translateClient.send(translateCommand); return { translated_text: TranslatedText }; }; ``` **Services used in this example** + Amazon Comprehend + Lambda + Amazon Polly + Amazon Textract + Amazon Translate ------ #### [ Ruby ] **SDK for Ruby** This example application analyzes and stores customer feedback cards. Specifically, it fulfills the need of a fictitious hotel in New York City. The hotel receives feedback from guests in various languages in the form of physical comment cards. That feedback is uploaded into the app through a web client. After an image of a comment card is uploaded, the following steps occur: + Text is extracted from the image using Amazon Textract. + Amazon Comprehend determines the sentiment of the extracted text and its language. + The extracted text is translated to English using Amazon Translate. + Amazon Polly synthesizes an audio file from the extracted text. The full app can be deployed with the AWS CDK. For source code and deployment instructions, see the project in [ GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/ruby/cross_service_examples/feedback_sentiment_analyzer). **Services used in this example** + Amazon Comprehend + Lambda + Amazon Polly + Amazon Textract + Amazon Translate ------ For a complete list of AWS SDK developer guides and code examples, see [Using this service with an AWS SDK](sdk-general-information-section.md). This topic also includes information about getting started and details about previous SDK versions. # Get started with Amazon Translate jobs using an AWS SDK The following code example shows how to: + Start an asynchronous batch translation job. + Wait for the asynchronous job to complete. + Describe the asynchronous job. ------ #### [ SAP ABAP ] **SDK for SAP ABAP** There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/xl8#code-examples). ``` DATA lo_inputdataconfig TYPE REF TO /aws1/cl_xl8inputdataconfig. DATA lo_outputdataconfig TYPE REF TO /aws1/cl_xl8outputdataconfig. DATA lt_targetlanguagecodes TYPE /aws1/cl_xl8tgtlanguagecodes00=>tt_targetlanguagecodestrlist. DATA lo_targetlanguagecodes TYPE REF TO /aws1/cl_xl8tgtlanguagecodes00. "Create an ABAP object for the input data config." lo_inputdataconfig = NEW #( iv_s3uri = iv_input_data_s3uri iv_contenttype = iv_input_data_contenttype ). "Create an ABAP object for the output data config." lo_outputdataconfig = NEW #( iv_s3uri = iv_output_data_s3uri ). "Create an internal table for target languages." lo_targetlanguagecodes = NEW #( iv_value = iv_targetlanguagecode ). INSERT lo_targetlanguagecodes INTO TABLE lt_targetlanguagecodes. TRY. DATA(lo_translationjob_result) = lo_xl8->starttexttranslationjob( io_inputdataconfig = lo_inputdataconfig io_outputdataconfig = lo_outputdataconfig it_targetlanguagecodes = lt_targetlanguagecodes iv_dataaccessrolearn = iv_dataaccessrolearn iv_jobname = iv_jobname iv_sourcelanguagecode = iv_sourcelanguagecode ). MESSAGE 'Translation job started.' TYPE 'I'. CATCH /aws1/cx_xl8internalserverex. MESSAGE 'An internal server error occurred. Retry your request.' TYPE 'E'. CATCH /aws1/cx_xl8invparamvalueex. MESSAGE 'The value of the parameter is not valid.' TYPE 'E'. CATCH /aws1/cx_xl8invalidrequestex. MESSAGE 'The request that you made is not valid.' TYPE 'E'. CATCH /aws1/cx_xl8resourcenotfoundex. MESSAGE 'The resource you are looking for has not been found.' TYPE 'E'. CATCH /aws1/cx_xl8toomanyrequestsex. MESSAGE 'You have made too many requests within a short period of time. ' TYPE 'E'. CATCH /aws1/cx_xl8unsuppedlanguage00. MESSAGE 'Amazon Translate does not support translation from the language of the source text into the requested target language.' TYPE 'E'. ENDTRY. "Get the job ID." DATA(lv_jobid) = lo_translationjob_result->get_jobid( ). "Wait for translate job to complete." DATA(lo_des_translation_result) = lo_xl8->describetexttranslationjob( iv_jobid = lv_jobid ). WHILE lo_des_translation_result->get_textxlationjobproperties( )->get_jobstatus( ) <> 'COMPLETED'. IF sy-index = 30. EXIT. "Maximum 900 seconds." ENDIF. WAIT UP TO 30 SECONDS. lo_des_translation_result = lo_xl8->describetexttranslationjob( iv_jobid = lv_jobid ). ENDWHILE. TRY. oo_result = lo_xl8->describetexttranslationjob( "oo_result is returned for testing purposes." iv_jobid = lv_jobid ). MESSAGE 'Job description retrieved.' TYPE 'I'. CATCH /aws1/cx_xl8internalserverex. MESSAGE 'An internal server error occurred. Retry your request.' TYPE 'E'. CATCH /aws1/cx_xl8resourcenotfoundex. MESSAGE 'The resource you are looking for has not been found.' TYPE 'E'. CATCH /aws1/cx_xl8toomanyrequestsex. MESSAGE 'You have made too many requests within a short period of time.' TYPE 'E'. ENDTRY. ``` + For API details, see the following topics in *AWS SDK for SAP ABAP API reference*. + [DescribeTextTranslationJob](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html) + [StartTextTranslationJob](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html) ------ For a complete list of AWS SDK developer guides and code examples, see [Using this service with an AWS SDK](sdk-general-information-section.md). This topic also includes information about getting started and details about previous SDK versions.