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Guidance for American Sign Language (ASL) 3D Avatar Translator on AWS

Translate multiple languages into American Sign Language (ASL) with an avatar-based translator

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Overview

This Guidance demonstrates how to build an application that provides multimodal, high-fidelity translations from over 100 spoken languages into ASL through a realistic avatar. It uses generative artificial intelligence (AI) to transcribe and simplify input phrases from any supported language. Each simplified English phrase is then interpreted, rearranged, and translated directly into ASL. The application also generates a background image from Stable Diffusion to visually portray the input phrase.

How it works

These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.

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Dive deep into the implementation guide for additional customization options and service configurations to tailor to your specific needs.

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Ready to deploy? Review the sample code on GitHub for detailed deployment instructions to deploy as-is or customize to fit your needs. 

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Well-Architected Pillars

The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.

The AWS Cloud Development Kit (AWS CDK) and AWS CloudFormation stack output provide insights into the current deployment state. Amazon CloudWatch logs enable operational analysis and visibility into the workload. GitLab facilitates source code management, and the AWS CDK deploys changes to the AWS Cloud. Infrastructure can be re-provisioned through AWS CDK in case of failure and can be undeployed or redeployed as a unit using CloudFormation.

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AWS Identity and Access Management (IAM) roles and policies, Amazon Cognito-based authentication, and an API Gateway endpoint protect and manage resources. Amazon Cognito restricts access to a specific user pool, with access tokens expiring after one hour. The API Gateway endpoint provides encrypted web API access to specific paths and services, enforcing policies and API authorization. All AWS services use IAM for authentication and authorization, leveraging IAM roles for short-term credentials.

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The resilience of serverless services underpins the architecture. Lambda decouples low-level input processing from the Unreal Engine sample application's presentation layer. API Gateway throttles requests, queuing them in Amazon SQS. The Unreal Engine sample application retries failed AWS SDK operations, such as dequeuing from Amazon SQS. Standard CloudWatch logs and metrics store informational messages, error conditions, and enable notifications of errors.

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Amazon CloudFront caches web page data at edge locations, scaling based on traffic. API Gateway throttling mechanisms (such as steady-state rate and burst request limits) manage API requests. For Amazon S3, edge locations (such as Transfer Acceleration) decrease latency. Adjusting provisioned throughput from Amazon Bedrock optimizes performance.

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Serverless AWS services allow you to run code without provisioning or managing servers, paying only for specific on-demand usage. Costs scale according to the number of ASL translation requests, the amount of words translated into ASL in each request, and the quantity of sentence simplification operations. The serverless architecture avoids persistent AWS resources beyond S3 buckets when the overall solution is inactive. CloudFront's caching reduces requests to the web interface origin server hosted on Amazon S3. As ASL translation requests scale, Amazon SQS queues these requests as needed for later consumption.

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Resource utilization scales on demand according to the number of ASL translation requests, the quantity of words translated into ASL in each request, and the amount of sentence simplification operations. The serverless architecture reduces the overall utilization and energy consumption footprint, eliminating the need to provision and manage servers or physical machines.

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Disclaimer

The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.