Contributing to ElevenLabs' AI Voice Demo

Why I Jumped In

ElevenLabs’ AI voice tech is incredible, so I dove into their open-source repo to make it easier for developers and hobbyists to test it. My goal? Build a tool that cuts demo setup time by ~30%, letting anyone explore their text-to-speech API on AWS Free Tier without battling complex configs. This project showcases my AWS and Terraform skills for junior cloud security or DevOps roles, while aligning with ElevenLabs’ mission to advance AI audio. It’s also a step in my journey from politics to IT—check out my portfolio at trisoncloudresume.com for more.

What I Built

I submitted a pull request to the elevenlabs-nextjs-starter repo to simplify testing ElevenLabs’ AI voice API. The tool saves ~30% of setup time, letting developers and hobbyists focus on creating with AI audio instead of wrestling AWS. Here’s what’s included:

• A Terraform script (main.tf) that sets up an AWS Lambda, S3 bucket, and IAM roles, all optimized for the Free Tier.
• A Python script (index.py) that calls ElevenLabs’ API to generate a “Hey ElevenLabs” MP3 and saves it to S3, with dependencies like requests included.
• Dynamic bucket naming using environment variables to avoid hardcoding errors.
• An updated README.md with clear, beginner-friendly steps: add your API key, run Terraform, get your voice clip.

This setup makes experimenting with ElevenLabs’ tech fast, accessible, and fun, with no cloud expertise required.

What I Learned

This project deepened my understanding of cloud and open-source work, while highlighting why tools like this matter:

• Terraform’s Value: It’s a great tool for automating AWS, though it can be frustrating when configs need tweaking.
• Lambda Dependencies: Missing libraries like requests or idna causes crashes. Zipping them all up is critical.
• Dynamic Configs: Environment variables for bucket names saved tons of rework, making the tool more reliable.
• Open-Source Impact: Clear docs and a clean PR ensure others can use this to explore AI audio easily.

Challenges and Solutions

Building this had its challenges, but I tackled them to keep the tool user-friendly:

• Bucket Name Issues: Random bucket names used uppercase letters, which AWS rejected. I fixed it by enforcing lowercase in Terraform.
• Lambda Errors: “Unhandled” errors, likely from an API key or voice ID issue, are still under investigation. I’m testing the API locally to pinpoint the cause.
• Missing Dependencies: Lambda failed without requests and other libraries. I included them in the ZIP file.
• Log Access: CloudWatch threw a “ResourceNotFoundException,” possibly due to permissions. I’m exploring workarounds like local testing.

Using AWS CLI and lessons from my Cloud Resume Challenge helped me resolve most issues, ensuring the tool streamlines AI voice testing.

Why It’s a Win

This project showcases my ability to build efficient, secure cloud solutions with AWS and Terraform. It cuts demo setup time by ~30%, letting developers focus on creating with ElevenLabs’ AI voice tech instead of battling AWS configs. For hobbyists, it’s a plug-and-play way to explore the API’s potential with zero cloud expertise. By aligning with ElevenLabs’ mission to advance AI audio, this contribution highlights my technical skills and problem-solving mindset, honed through my transition from politics to IT. See more at trisoncloudresume.com. I’m excited to contribute to innovative teams and welcome feedback to grow in cloud security or DevOps!