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Make your audio and video files searchable using Amazon Transcribe and Amazon Kendra

The demand for audio and video media content is growing at an unprecedented rate. Organizations are using media to engage with their audiences like never before. Product documentation is increasingly published in video form, and podcasts are increasingly produced in place of blog posts. The recent explosion in the use of virtual workplaces has resulted…



The demand for audio and video media content is growing at an unprecedented rate. Organizations are using media to engage with their audiences like never before. Product documentation is increasingly published in video form, and podcasts are increasingly produced in place of blog posts. The recent explosion in the use of virtual workplaces has resulted in content that is encapsulated in the form of recorded meetings, calls, and voicemails. Contact centers also generate media content such as support calls, screen share recordings, or post-call surveys.

Amazon Machine Learning services help you find answers and extract valuable insights from the content of your audio and video files as well as your text files.

In this post, we introduce a new open-source solution, MediaSearch, built to make your media files searchable, and consumable in search results. It uses Amazon Transcribe to convert media audio tracks to text, and Amazon Kendra to provide intelligent search. Your users can find the content they’re looking for, even when it’s embedded in the sound track of your audio or video files. The solution also provides an enhanced Amazon Kendra query application that lets users play the relevant section of original media files, directly from the search results page.

Solution overview

MediaSearch is easy to install and try out! Use it to enable your customers to find answers to their questions from your podcast recordings and presentations, or for your students to find answers from your educational videos or lecture recordings, in addition to text documents.

The MediaSearch solution has two components, as illustrated in the following diagram.

The first component, the MediaSearch indexer, finds and transcribes audio and video files stored in an Amazon Simple Storage Service (Amazon S3) bucket. It prepares the transcriptions by embedding time markers at the start of each sentence, and it indexes each prepared transcription in a new or existing Amazon Kendra index. It runs the first time when you install it, and subsequently runs on an interval that you specify, maintaining the index to reflect any new, modified, or deleted files.

The second component, the MediaSearch finder, is a sample web search client that you use to search for content in your Amazon Kendra index. It has all the features of a standard Amazon Kendra search page, but it also includes in-line embedded media players in the search result, so you can not only see the relevant section of the transcript, but also play the corresponding section from the original media without navigating away from the search page (see the following screenshot).

In the sections that follow, we discuss several topics:

  • How to deploy the solution to your AWS account
  • How to use it to index and search sample media files
  • How to use the solution with your own media files
  • How the solution works under the hood
  • The costs involved
  • How to monitor usage and troubleshoot problems
  • Options to customize and tune the solution
  • How to uninstall and clean up when you’re done experimenting

Deploy the MediaSearch solution

In this section, we walk through deploying the two solution components: the indexer and the finder. We use an AWS CloudFormation stack to deploy the necessary resources in the us-east-1 (N. Virginia) AWS Region.

The source code is available in our GitHub repository. Follow the directions in the README to deploy MediaSearch to additional Regions supported by Amazon Kendra.

Deploy the indexer component

To deploy the indexer component, complete the following steps:

  1. Choose Launch Stack:
  2. Change the stack name if required to ensure that it’s unique.
  3. For ExistingIndexId, leave blank to create a new Amazon Kendra index (Developer Edition), otherwise provide the IndexId (not the index name) for an existing index in your account and Region (Amazon Kendra Enterprise Edition should be used for production workloads).
  4. For MediaBucket and MediaFolderPrefix, use the defaults initially to transcribe and index sample audio and video files.
  5. For now, use the default values for the other parameters.
  6. Select the acknowledgement check boxes, and choose Create stack.
  7. When the stack is created (after approximately 15 minutes), choose the Outputs tab, and copy the value of IndexId—you need it to deploy the finder component in the next step.

The newly installed indexer runs automatically to find, transcribe, and index the sample audio and video files. Later you can provide a different bucket name and prefix to index your own media files. If you have media files in multiple buckets, you can deploy multiple instances of the indexer, each with a unique stack name.

Deploy the finder component

To deploy the finder web application component, complete the following steps:

  1. Choose Launch Stack:
  2. For IndexId, use the Amazon Kendra index copied from the MediaSearch indexer stack outputs.
  3. For MediaBucketNames, use the default initially to allow the search page to access media files from the sample file bucket.
  4. When the stack is created (after approximately 5 minutes), choose the Outputs tab and use the link for MediaSearchFinderURL to open the new media search application page in your browser.

If the application isn’t ready when you first open the page, don’t worry! The initial application build and deployment (using AWS Amplify) takes about 10 minutes, so it will work when you try again a little later. If for any reason the application still doesn’t open, refer to the README in the GitHub repo for troubleshooting steps.

And that’s all there is to the deployment! Next, let’s run some search queries to see it in action.

Test with the sample media files

By the time the MediaSearch finder application is deployed and ready to use, the indexer should have completed processing the sample media files (selected AWS Podcast episodes and AWS Knowledge center videos). You can now run your first MediaSearch query.

  1. Open the MediaSearch finder application in your browser as described in the previous section.
  2. In the query box, enter What’s an interface VPC Endpoint?

The query returns multiple results, sourced from the transcripts of the sample media files.

  1. Observe the time markers at the beginning of each sentence in the answer text. This indicates where the answer is to be found in the original media file.
  2. Use the embedded video player to play the original video inline. Observe that the media playback starts at the relevant section of the video based on the time marker.
  3. To play the video full screen in a new browser tab, use the Fullscreen menu option in the player, or choose the media file hyperlink shown above the answer text.
  4. Choose the video file hyperlink (right-click), copy the URL, and paste it into a text editor. It looks something like the following:…. #t=253.52

This is a presigned S3 URL that provides your browser with temporary read access to the media file referenced in the search result. Using presigned URLs means you don’t need to provide permanent public access to all of your indexed media files.

  1. Scroll down the page, and observe that some search results are from audio (MP3) files, and some are from video (MP4) files.

You can mix and match media types in the same index. You could include other data source types as well, such as documents, webpages, and other file types supported by available Amazon Kendra data sources, and search across them all, allowing Amazon Kendra to find the best content to answer your query.

  1. Experiment with additional queries, such as What does a solutions architect do? or What is Kendra?, or try your own questions.

Index and search your own media files

To index media files stored in your own S3 bucket, replace the MediaBucket and MediaFolderPrefix parameters with your own bucket name and prefix when you install or update the indexer component stack, and modify the MediaBucketNames parameter with your own bucket name when you install or update the finder component stack.

  1. Create a new MediaSearch indexer stack using an existing Amazon Kendra IndexId to add files stored in the new location. To deploy a new indexer, follow the directions in the Deploy the indexer component section in this post, but this time replace the defaults to specify the media bucket name and prefix for your own media files.
  2. Alternatively, update an existing MediaSearch indexer stack to replace the previously indexed files with files from the new location:
    1. Select the stack on the CloudFormation console, choose Update, then Use current template, then Next.
    2. Modify the media bucket name and prefix parameter values as needed.
    3. Choose Next twice, select the acknowledgement check box, and choose Update stack.
  3. Update an existing MediaSearch finder stack to change bucket names or add additional bucket names to the MediaBucketNames parameter.

When the MediaSearch indexer stack is successfully created or updated, the indexer automatically finds, transcribes, and indexes the media files stored in your S3 bucket. When it’s complete, you can submit queries and find answers from the audio tracks of your own audio and video files.

You have the option to provide metadata for any or all of your media files. Use metadata to assign values to index attributes for sorting, filtering, and faceting your search results, or to specify access control lists to govern access to the files. Metadata files can be in the same S3 folder as your media files (default), or in a parallel folder structure specified by the optional indexer parameter MetadataFolderPrefix. For more information about how to create metadata files, see S3 document metadata.

You can also provide customized transcription options for any or all of your media files. This allows you to take full advantage of Amazon Transcribe features such as custom vocabularies, automatic content redaction, and custom language models. For more information, refer to the README in the GitHub repo.

How the MediaSearch solution works

Let’s take a quick look under the hood to see how the solution works, as illustrated in the following diagram.

The MediaSearch solution has an event-driven serverless computing architecture with the following steps:

  1. You provide an S3 bucket containing the audio and video files you want to index and search.
  2. Amazon EventBridge generates events on a repeating interval (such as every 2 hours, every 6 hours, and so on)
  3. These events invoke an AWS Lambda function. The function is invoked initially when the CloudFormation stack is first deployed, and then subsequently by the scheduled events from EventBridge. An Amazon Kendra data source sync job is started. The Lambda function lists all the supported media files (FLAC, MP3, MP4, Ogg, WebM, AMR, or WAV) and associated metadata and Transcribe options stored in the user-provided S3 bucket.
  4. Each new file is added to the Amazon DynamoDB tracking table and submitted to be transcribed by a Transcribe job. Any file that has been previously transcribed is submitted for transcription again only if it has been modified since it was previously transcribed, or if associated Transcribe options have been updated. The DynamoDB table is updated to reflect the transcription status and last modified timestamp of each file. Any tracked files that no longer exist in the S3 bucket are removed from the DynamoDB table and from the Amazon Kendra index. If no new or updated files are discovered, the Amazon Kendra data source sync job is immediately stopped. The DynamoDB table holds a record for each media file with attributes to track transcription job names and status, and last modified timestamps.
  5. As each Transcribe job completes, EventBridge generates a Job Complete event, which invokes an instance of another Lambda function.
  6. The Lambda function processes the transcription job output, generating a modified transcription that has a time marker inserted at the start of each sentence. This modified transcription is indexed in Amazon Kendra, and the job status for the file is updated in the DynamoDB table. When the last file has been transcribed and indexed, the Amazon Kendra data source sync job is stopped.
  7. The index is populated and kept in sync with the transcriptions of all the media files in the S3 bucket monitored by the MediaSearch indexer component, integrated with any additional content from any other provisioned data sources. The media transcriptions are used by Amazon Kendra’s intelligent query processing, which allows users to find content and answers to their questions.
  8. The sample finder client application enhances users’ search experience by embedding an inline media player with each Amazon Kendra answer that is based on a transcribed media file. The client uses the time markers embedded in the transcript to start media playback at the relevant section of the original media file.

Estimate costs

In addition to Amazon S3 costs associated with storing your media, the MediaSearch solution incurs usage costs from Amazon Kendra and Transcribe. Additional minor (usually not significant) costs are incurred by the other services mentioned after free tier allowances have been used. For more information, see the pricing documentation for Amazon Kendra, Transcribe, Lambda, DynamoDB, and EventBridge.

Pricing example: Index the sample media files

The sample dataset has 25 media files—13 audio podcast and 12 video files—containing a total of around 480 minutes or 29,000 seconds of audio.

If you don’t provide an existing Amazon Kendra IndexId when you install MediaSearch, a new Amazon Kendra Developer Edition index is automatically created for you so you can test the solution. After you use your free tier allowance (up to 750 hours in the first 30 days), the index costs $1.125 per hour.

Transcribe pricing is based on the number of seconds of audio transcribed, with a free tier allowance of 60 minutes of audio per month for the first 12 months. After the free tier is used, the cost is $0.00040 for each second of audio transcribed. If you’re no longer free tier eligible, the cost to transcribe the sample files is as follows:

  • Total seconds of audio = 29,000
  • Transcription price per second = $0.00040
  • Total cost for Transcribe = [number of seconds] x [cost per second] = 29,000 x $0.00040 = $11.60

Monitor and troubleshoot

To see the details of each media file transcript job, navigate to the Transcription jobs page on the Transcribe console.

Each media file is transcribed only one time, unless the file is modified. Modified files are re-transcribed and re-indexed to reflect the changes.

Choose any transcription job to review the transcription and examine additional job details.

On the Indexes page of the Amazon Kendra console, choose the index used by MediaSearch to examine the index details.

Choose Data sources in the navigation pane to examine the MediaSearch indexer data source, and observe the data source sync run history. The data source syncs when the indexer runs every interval specified in the CloudFormation stack parameters when you deployed or last updated the solution.

On the DynamoDB console, choose Tables in the navigation pane. Use your MediaSearch stack name as a filter to display the MediaSearch DynamoDB table, and examine the items showing each indexed media file and corresponding status. The table has one record for each media file, and contains attributes with information about the file and its processing status.

On the Functions page of the Lambda console, use your MediaSearch stack name as a filter to list the two MediaSearch indexer functions described earlier.

Choose either of the functions to examine the function details, including environment variables, source code, and more. Choose Monitor & View logs in CloudWatch to examine the output of each function invocation and troubleshoot any issues.

Customize and enhance the solution

You can fork the MediaSearch GitHub repository, enhance the code, and send us pull requests so we can incorporate and share your improvements!

The following are a few suggestions for features you might want to implement:

Clean up

When you’re finished experimenting with this solution, clean up your resources by using the AWS CloudFormation console to delete the indexer and finder stacks that you deployed. This deletes all the resources, including any Amazon Kendra indexes that were created by deploying the solution. Pre-existing indexes aren’t deleted. However, media files that were indexed by the solution are removed from the pre-existing index when you delete the indexer stack.


The combination of Amazon Transcribe and Amazon Kendra enable a scalable, cost-effective solution to make your media files discoverable. You can use the content of your media files to find accurate answers to your users’ questions, whether they’re from text documents or media files, and consume them in their native format. In other words, this solution is a leap in bringing media files on par with text documents as containers of information.

The sample MediaSearch application is provided as open source—use it as a starting point for your own solution, and help us make it better by contributing back fixes and features via GitHub pull requests. For expert assistance, AWS Professional Services and other Amazon partners are here to help.

We’d love to hear from you. Let us know what you think in the comments section, or using the issues forum in the MediaSearch GitHub repository.

About the Authors

Bob StrahanBob Strahan is a Principal Solutions Architect in the AWS Language AI Services team.





Abhinav JawadekarAbhinav Jawadekar is a Senior Partner Solutions Architect at Amazon Web Services. Abhinav works with AWS Partners to help them in their cloud journey.



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Customize pronunciation using lexicons in Amazon Polly

Amazon Polly is a text-to-speech service that uses advanced deep learning technologies to synthesize natural-sounding human speech. It is used in a variety of use cases, such as contact center systems, delivering conversational user experiences with human-like voices for automated real-time status check, automated account and billing inquiries, and by news agencies like The Washington…




Amazon Polly is a text-to-speech service that uses advanced deep learning technologies to synthesize natural-sounding human speech. It is used in a variety of use cases, such as contact center systems, delivering conversational user experiences with human-like voices for automated real-time status check, automated account and billing inquiries, and by news agencies like The Washington Post to allow readers to listen to news articles.

As of today, Amazon Polly provides over 60 voices in 30+ language variants. Amazon Polly also uses context to pronounce certain words differently based upon the verb tense and other contextual information. For example, “read” in “I read a book” (present tense) and “I will read a book” (future tense) is pronounced differently.

However, in some situations you may want to customize the way Amazon Polly pronounces a word. For example, you may need to match the pronunciation with local dialect or vernacular. Names of things (e.g., Tomato can be pronounced as tom-ah-to or tom-ay-to), people, streets, or places are often pronounced in many different ways.

In this post, we demonstrate how you can leverage lexicons for creating custom pronunciations. You can apply lexicons for use cases such as publishing, education, or call centers.

Customize pronunciation using SSML tag

Let’s say you stream a popular podcast from Australia and you use the Amazon Polly Australian English (Olivia) voice to convert your script into human-like speech. In one of your scripts, you want to use words that are unknown to Amazon Polly voice. For example, you want to send Mātariki (Māori New Year) greetings to your New Zealand listeners. For such scenarios, Amazon Polly supports phonetic pronunciation, which you can use to achieve a pronunciation that is close to the correct pronunciation in the foreign language.

You can use the Speech Synthesis Markup Language (SSML) tag to suggest a phonetic pronunciation in the ph attribute. Let me show you how you can use SSML tag.

First, login into your AWS console and search for Amazon Polly in the search bar at the top. Select Amazon Polly and then choose Try Polly button.

In the Amazon Polly console, select Australian English from the language dropdown and enter following text in the Input text box and then click on Listen to test the pronunciation.

I’m wishing you all a very Happy Mātariki.

Sample speech without applying phonetic pronunciation:

If you hear the sample speech above, you can notice that the pronunciation of Mātariki – a word which is not part of Australian English – isn’t quite spot-on. Now, let’s look at how in such scenarios we can use phonetic pronunciation using SSML tag to customize the speech produced by Amazon Polly.

To use SSML tags, turn ON the SSML option in Amazon Polly console. Then copy and paste following SSML script containing phonetic pronunciation for Mātariki specified inside the ph attribute of the tag.

I’m wishing you all a very Happy Mātariki.

With the tag, Amazon Polly uses the pronunciation specified by the ph attribute instead of the standard pronunciation associated by default with the language used by the selected voice.

Sample speech after applying phonetic pronunciation:

If you hear the sample sound, you’ll notice that we opted for a different pronunciation for some of vowels (e.g., ā) to make Amazon Polly synthesize the sounds that are closer to the correct pronunciation. Now you might have a question, how do I generate the phonetic transcription “” for the word Mātariki?

You can create phonetic transcriptions by referring to the Phoneme and Viseme tables for the supported languages. In the example above we have used the phonemes for Australian English.

Amazon Polly offers support in two phonetic alphabets: IPA and X-Sampa. Benefit of X-Sampa is that they are standard ASCII characters, so it is easier to type the phonetic transcription with a normal keyboard. You can use either of IPA or X-Sampa to generate your transcriptions, but make sure to stay consistent with your choice, especially when you use a lexicon file which we’ll cover in the next section.

Each phoneme in the phoneme table represents a speech sound. The bolded letters in the “Example” column of the Phoneme/Viseme table in the Australian English page linked above represent the part of the word the “Phoneme” corresponds to. For example, the phoneme /j/ represents the sound that an Australian English speaker makes when pronouncing the letter “y” in “yes.”

Customize pronunciation using lexicons

Phoneme tags are suitable for one-off situations to customize isolated cases, but these are not scalable. If you process huge volume of text, managed by different editors and reviewers, we recommend using lexicons. Using lexicons, you can achieve consistency in adding custom pronunciations and simultaneously reduce manual effort of inserting phoneme tags into the script.

A good practice is that after you test the custom pronunciation on the Amazon Polly console using the tag, you create a library of customized pronunciations using lexicons. Once lexicons file is uploaded, Amazon Polly will automatically apply phonetic pronunciations specified in the lexicons file and eliminate the need to manually provide a tag.

Create a lexicon file

A lexicon file contains the mapping between words and their phonetic pronunciations. Pronunciation Lexicon Specification (PLS) is a W3C recommendation for specifying interoperable pronunciation information. The following is an example PLS document:

Matariki Mātariki NZ New Zealand

Make sure that you use correct value for the xml:lang field. Use en-AU if you’re uploading the lexicon file to use with the Amazon Polly Australian English voice. For a complete list of supported languages, refer to Languages Supported by Amazon Polly.

To specify a custom pronunciation, you need to add a element which is a container for a lexical entry with one or more element and one or more pronunciation information provided inside element.

The element contains the text describing the orthography of the element. You can use a element to specify the word whose pronunciation you want to customize. You can add multiple elements to specify all word variations, for example with or without macrons. The element is case-sensitive, and during speech synthesis Amazon Polly string matches the words inside your script that you’re converting to speech. If a match is found, it uses the element, which describes how the is pronounced to generate phonetic transcription.

You can also use for commonly used abbreviations. In the preceding example of a lexicon file, NZ is used as an alias for New Zealand. This means that whenever Amazon Polly comes across “NZ” (with matching case) in the body of the text, it’ll read those two letters as “New Zealand”.

For more information on lexicon file format, see Pronunciation Lexicon Specification (PLS) Version 1.0 on the W3C website.

You can save a lexicon file with as a .pls or .xml file before uploading it to Amazon Polly.

Upload and apply the lexicon file

Upload your lexicon file to Amazon Polly using the following instructions:

  1. On the Amazon Polly console, choose Lexicons in the navigation pane.
  2. Choose Upload lexicon.
  3. Enter a name for the lexicon and then choose a lexicon file.
  4. Choose the file to upload.
  5. Choose Upload lexicon.

If a lexicon by the same name (whether a .pls or .xml file) already exists, uploading the lexicon overwrites the existing lexicon.

Now you can apply the lexicon to customize pronunciation.

  1. Choose Text-to-Speech in the navigation pane.
  2. Expand Additional settings.
  3. Turn on Customize pronunciation.
  4. Choose the lexicon on the drop-down menu.

You can also choose Upload lexicon to upload a new lexicon file (or a new version).

It’s a good practice to version control the lexicon file in a source code repository. Keeping the custom pronunciations in a lexicon file ensures that you can consistently refer to phonetic pronunciations for certain words across the organization. Also, keep in mind the pronunciation lexicon limits mentioned on Quotas in Amazon Polly page.

Test the pronunciation after applying the lexicon

Let’s perform quick test using “Wishing all my listeners in NZ, a very Happy Mātariki” as the input text.

We can compare the audio files before and after applying the lexicon.

Before applying the lexicon:

After applying the lexicon:


In this post, we discussed how you can customize pronunciations of commonly used acronyms or words not found in the selected language in Amazon Polly. You can use SSML tag which is great for inserting one-off customizations or testing purposes. We recommend using Lexicon to create a consistent set of pronunciations for frequently used words across your organization. This enables your content writers to spend time on writing instead of the tedious task of adding phonetic pronunciations in the script repetitively. You can try this in your AWS account on the Amazon Polly console.

Summary of resources

About the Authors

Ratan Kumar is a Solutions Architect based out of Auckland, New Zealand. He works with large enterprise customers helping them design and build secure, cost-effective, and reliable internet scale applications using the AWS cloud. He is passionate about technology and likes sharing knowledge through blog posts and twitch sessions.

Maciek Tegi is a Principal Audio Designer and a Product Manager for Polly Brand Voices. He has worked in professional capacity in the tech industry, movies, commercials and game localization. In 2013, he was the first audio engineer hired to the Alexa Text-To- Speech team. Maciek was involved in releasing 12 Alexa TTS voices across different countries, over 20 Polly voices, and 4 Alexa celebrity voices. Maciek is a triathlete, and an avid acoustic guitar player.


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AWS Week in Review – May 16, 2022

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS! I had been on the road for the last five weeks and attended many of the AWS Summits in Europe. It was great to talk to so many of you…




This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

I had been on the road for the last five weeks and attended many of the AWS Summits in Europe. It was great to talk to so many of you in person. The Serverless Developer Advocates are going around many of the AWS Summits with the Serverlesspresso booth. If you attend an event that has the booth, say “Hi ” to my colleagues, and have a coffee while asking all your serverless questions. You can find all the upcoming AWS Summits in the events section at the end of this post.

Last week’s launches
Here are some launches that got my attention during the previous week.

AWS Step Functions announced a new console experience to debug your state machine executions – Now you can opt-in to the new console experience of Step Functions, which makes it easier to analyze, debug, and optimize Standard Workflows. The new page allows you to inspect executions using three different views: graph, table, and event view, and add many new features to enhance the navigation and analysis of the executions. To learn about all the features and how to use them, read Ben’s blog post.

Example on how the Graph View looks

Example on how the Graph View looks

AWS Lambda now supports Node.js 16.x runtime – Now you can start using the Node.js 16 runtime when you create a new function or update your existing functions to use it. You can also use the new container image base that supports this runtime. To learn more about this launch, check Dan’s blog post.

AWS Amplify announces its Android library designed for Kotlin – The Amplify Android library has been rewritten for Kotlin, and now it is available in preview. This new library provides better debugging capacities and visibility into underlying state management. And it is also using the new AWS SDK for Kotlin that was released last year in preview. Read the What’s New post for more information.

Three new APIs for batch data retrieval in AWS IoT SiteWise – With this new launch AWS IoT SiteWise now supports batch data retrieval from multiple asset properties. The new APIs allow you to retrieve current values, historical values, and aggregated values. Read the What’s New post to learn how you can start using the new APIs.

AWS Secrets Manager now publishes secret usage metrics to Amazon CloudWatch – This launch is very useful to see the number of secrets in your account and set alarms for any unexpected increase or decrease in the number of secrets. Read the documentation on Monitoring Secrets Manager with Amazon CloudWatch for more information.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS News
Some other launches and news that you may have missed:

IBM signed a deal with AWS to offer its software portfolio as a service on AWS. This allows customers using AWS to access IBM software for automation, data and artificial intelligence, and security that is built on Red Hat OpenShift Service on AWS.

Podcast Charlas Técnicas de AWS – If you understand Spanish, this podcast is for you. Podcast Charlas Técnicas is one of the official AWS podcasts in Spanish. This week’s episode introduces you to Amazon DynamoDB and shares stories on how different customers use this database service. You can listen to all the episodes directly from your favorite podcast app or the podcast web page.

AWS Open Source News and Updates – Ricardo Sueiras, my colleague from the AWS Developer Relation team, runs this newsletter. It brings you all the latest open-source projects, posts, and more. Read edition #112 here.

Upcoming AWS Events
It’s AWS Summits season and here are some virtual and in-person events that might be close to you:

You can register for re:MARS to get fresh ideas on topics such as machine learning, automation, robotics, and space. The conference will be in person in Las Vegas, June 21–24.

That’s all for this week. Check back next Monday for another Week in Review!

— Marcia


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Personalize your machine translation results by using fuzzy matching with Amazon Translate

A person’s vernacular is part of the characteristics that make them unique. There are often countless different ways to express one specific idea. When a firm communicates with their customers, it’s critical that the message is delivered in a way that best represents the information they’re trying to convey. This becomes even more important when…




A person’s vernacular is part of the characteristics that make them unique. There are often countless different ways to express one specific idea. When a firm communicates with their customers, it’s critical that the message is delivered in a way that best represents the information they’re trying to convey. This becomes even more important when it comes to professional language translation. Customers of translation systems and services expect accurate and highly customized outputs. To achieve this, they often reuse previous translation outputs—called translation memory (TM)—and compare them to new input text. In computer-assisted translation, this technique is known as fuzzy matching. The primary function of fuzzy matching is to assist the translator by speeding up the translation process. When an exact match can’t be found in the TM database for the text being translated, translation management systems (TMSs) often have the option to search for a match that is less than exact. Potential matches are provided to the translator as additional input for final translation. Translators who enhance their workflow with machine translation capabilities such as Amazon Translate often expect fuzzy matching data to be used as part of the automated translation solution.

In this post, you learn how to customize output from Amazon Translate according to translation memory fuzzy match quality scores.

Translation Quality Match

The XML Localization Interchange File Format (XLIFF) standard is often used as a data exchange format between TMSs and Amazon Translate. XLIFF files produced by TMSs include source and target text data along with match quality scores based on the available TM. These scores—usually expressed as a percentage—indicate how close the translation memory is to the text being translated.

Some customers with very strict requirements only want machine translation to be used when match quality scores are below a certain threshold. Beyond this threshold, they expect their own translation memory to take precedence. Translators often need to apply these preferences manually either within their TMS or by altering the text data. This flow is illustrated in the following diagram. The machine translation system processes the translation data—text and fuzzy match scores— which is then reviewed and manually edited by translators, based on their desired quality thresholds. Applying thresholds as part of the machine translation step allows you to remove these manual steps, which improves efficiency and optimizes cost.

Machine Translation Review Flow

Figure 1: Machine Translation Review Flow

The solution presented in this post allows you to enforce rules based on match quality score thresholds to drive whether a given input text should be machine translated by Amazon Translate or not. When not machine translated, the resulting text is left to the discretion of the translators reviewing the final output.

Solution Architecture

The solution architecture illustrated in Figure 2 leverages the following services:

  • Amazon Simple Storage Service – Amazon S3 buckets contain the following content:
    • Fuzzy match threshold configuration files
    • Source text to be translated
    • Amazon Translate input and output data locations
  • AWS Systems Manager – We use Parameter Store parameters to store match quality threshold configuration values
  • AWS Lambda – We use two Lambda functions:
    • One function preprocesses the quality match threshold configuration files and persists the data into Parameter Store
    • One function automatically creates the asynchronous translation jobs
  • Amazon Simple Queue Service – An Amazon SQS queue triggers the translation flow as a result of new files coming into the source bucket

Solution Architecture Diagram

Figure 2: Solution Architecture

You first set up quality thresholds for your translation jobs by editing a configuration file and uploading it into the fuzzy match threshold configuration S3 bucket. The following is a sample configuration in CSV format. We chose CSV for simplicity, although you can use any format. Each line represents a threshold to be applied to either a specific translation job or as a default value to any job.

default, 75 SourceMT-Test, 80

The specifications of the configuration file are as follows:

  • Column 1 should be populated with the name of the XLIFF file—without extension—provided to the Amazon Translate job as input data.
  • Column 2 should be populated with the quality match percentage threshold. For any score below this value, machine translation is used.
  • For all XLIFF files whose name doesn’t match any name listed in the configuration file, the default threshold is used—the line with the keyword default set in Column 1.

Auto-generated parameter in Systems Manager Parameter Store

Figure 3: Auto-generated parameter in Systems Manager Parameter Store

When a new file is uploaded, Amazon S3 triggers the Lambda function in charge of processing the parameters. This function reads and stores the threshold parameters into Parameter Store for future usage. Using Parameter Store avoids performing redundant Amazon S3 GET requests each time a new translation job is initiated. The sample configuration file produces the parameter tags shown in the following screenshot.

The job initialization Lambda function uses these parameters to preprocess the data prior to invoking Amazon Translate. We use an English-to-Spanish translation XLIFF input file, as shown in the following code. It contains the initial text to be translated, broken down into what is referred to as segments, represented in the source tags.

Consent Form CONSENT FORM FORMULARIO DE CONSENTIMIENTO Screening Visit: Screening Visit Selección

The source text has been pre-matched with the translation memory beforehand. The data contains potential translation alternatives—represented as tags—alongside a match quality attribute, expressed as a percentage. The business rule is as follows:

  • Segments received with alternative translations and a match quality below the threshold are untouched or empty. This signals to Amazon Translate that they must be translated.
  • Segments received with alternative translations with a match quality above the threshold are pre-populated with the suggested target text. Amazon Translate skips those segments.

Let’s assume the quality match threshold configured for this job is 80%. The first segment with 99% match quality isn’t machine translated, whereas the second segment is, because its match quality is below the defined threshold. In this configuration, Amazon Translate produces the following output:

Consent Form FORMULARIO DE CONSENTIMIENTO CONSENT FORM FORMULARIO DE CONSENTIMIENTO Screening Visit: Visita de selección Screening Visit Selección

In the second segment, Amazon Translate overwrites the target text initially suggested (Selección) with a higher quality translation: Visita de selección.

One possible extension to this use case could be to reuse the translated output and create our own translation memory. Amazon Translate supports customization of machine translation using translation memory thanks to the parallel data feature. Text segments previously machine translated due to their initial low-quality score could then be reused in new translation projects.

In the following sections, we walk you through the process of deploying and testing this solution. You use AWS CloudFormation scripts and data samples to launch an asynchronous translation job personalized with a configurable quality match threshold.


For this walkthrough, you must have an AWS account. If you don’t have an account yet, you can create and activate one.

Launch AWS CloudFormation stack

  1. Choose Launch Stack:
  2. For Stack name, enter a name.
  3. For ConfigBucketName, enter the S3 bucket containing the threshold configuration files.
  4. For ParameterStoreRoot, enter the root path of the parameters created by the parameters processing Lambda function.
  5. For QueueName, enter the SQS queue that you create to post new file notifications from the source bucket to the job initialization Lambda function. This is the function that reads the configuration file.
  6. For SourceBucketName, enter the S3 bucket containing the XLIFF files to be translated. If you prefer to use a preexisting bucket, you need to change the value of the CreateSourceBucket parameter to No.
  7. For WorkingBucketName, enter the S3 bucket Amazon Translate uses for input and output data.
  8. Choose Next.

    Figure 4: CloudFormation stack details

  9. Optionally on the Stack Options page, add key names and values for the tags you may want to assign to the resources about to be created.
  10. Choose Next.
  11. On the Review page, select I acknowledge that this template might cause AWS CloudFormation to create IAM resources.
  12. Review the other settings, then choose Create stack.

AWS CloudFormation takes several minutes to create the resources on your behalf. You can watch the progress on the Events tab on the AWS CloudFormation console. When the stack has been created, you can see a CREATE_COMPLETE message in the Status column on the Overview tab.

Test the solution

Let’s go through a simple example.

  1. Download the following sample data.
  2. Unzip the content.

There should be two files: an .xlf file in XLIFF format, and a threshold configuration file with .cfg as the extension. The following is an excerpt of the XLIFF file.

English to French sample file extract

Figure 5: English to French sample file extract

  1. On the Amazon S3 console, upload the quality threshold configuration file into the configuration bucket you specified earlier.

The value set for test_En_to_Fr is 75%. You should be able to see the parameters on the Systems Manager console in the Parameter Store section.

  1. Still on the Amazon S3 console, upload the .xlf file into the S3 bucket you configured as source. Make sure the file is under a folder named translate (for example, /translate/test_En_to_Fr.xlf).

This starts the translation flow.

  1. Open the Amazon Translate console.

A new job should appear with a status of In Progress.

Auto-generated parameter in Systems Manager Parameter Store

Figure 6: In progress translation jobs on Amazon Translate console

  1. Once the job is complete, click into the job’s link and consult the output. All segments should have been translated.

All segments should have been translated. In the translated XLIFF file, look for segments with additional attributes named lscustom:match-quality, as shown in the following screenshot. These custom attributes identify segments where suggested translation was retained based on score.

Custom attributes identifying segments where suggested translation was retained based on score

Figure 7: Custom attributes identifying segments where suggested translation was retained based on score

These were derived from the translation memory according to the quality threshold. All other segments were machine translated.

You have now deployed and tested an automated asynchronous translation job assistant that enforces configurable translation memory match quality thresholds. Great job!


If you deployed the solution into your account, don’t forget to delete the CloudFormation stack to avoid any unexpected cost. You need to empty the S3 buckets manually beforehand.


In this post, you learned how to customize your Amazon Translate translation jobs based on standard XLIFF fuzzy matching quality metrics. With this solution, you can greatly reduce the manual labor involved in reviewing machine translated text while also optimizing your usage of Amazon Translate. You can also extend the solution with data ingestion automation and workflow orchestration capabilities, as described in Speed Up Translation Jobs with a Fully Automated Translation System Assistant.

About the Authors

Narcisse Zekpa is a Solutions Architect based in Boston. He helps customers in the Northeast U.S. accelerate their adoption of the AWS Cloud, by providing architectural guidelines, design innovative, and scalable solutions. When Narcisse is not building, he enjoys spending time with his family, traveling, cooking, and playing basketball.

Dimitri Restaino is a Solutions Architect at AWS, based out of Brooklyn, New York. He works primarily with Healthcare and Financial Services companies in the North East, helping to design innovative and creative solutions to best serve their customers. Coming from a software development background, he is excited by the new possibilities that serverless technology can bring to the world. Outside of work, he loves to hike and explore the NYC food scene.


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