Building Audio Transcription on AWS with OpenAI Whisper

Build an audio transcription service on AWS with OpenAI Whisper that turns any uploaded recording into a full transcript, no clicks and no waiting, whether one file lands or forty. Drop a recording into a folder and a transcript appears in another one a minute later. No button, no typing, and the same effort for one file as for forty. This build takes that behavior apart piece by piece: the S3 event that fires on upload, the queue that absorbs bursts, and the small Whisper worker that was already listening. It stays calm under load because the upload and the transcription never wait on each other. 🧪 Try it yourself in the free lab: https://kode.wiki/4r0S3vG 📚 What you'll learn: 1️⃣ How an S3 upload event becomes a queued job through a single Lambda dispatcher 2️⃣ Why a 5-minute SQS visibility timeout is what lets a long job run exactly once 3️⃣ The prefix filter that stops the pipeline from transcribing its own output forever 4️⃣ How two IAM roles let a function and a server act on AWS with no keys on disk 5️⃣ How to fit a real ML model on a 1GB EC2 box using a bigger disk and a swap file 🚨 Start Your AI Journey with KodeKloud: https://kode.wiki/4qsrspX 🧪 Free hands-on lab: https://kode.wiki/4r0S3vG ⏰ Timestamps: 00:00 - How Automatic Audio Transcription Works 01:20 - Architecture Diagram 03:36 - Try Building using our Free hands-on lab 🤖 Learn System Design from this Playlist: https://www.youtube.com/watch?v=SE2KF-vxvS0&list=PLPOrzEadD7Ew 🔔 Subscribe for more event-driven AWS projects, one service at a time #Whisper #AWS #AudioTranscription #OpenAIWhisper #EventDriven #Serverless #AWSLambda #AmazonS3 #AmazonSQS #EC2 #Docker #DevOps #CloudComputing #SpeechToText #KodeKloud #AWSProjects #IAM #Python