Automatic Speech Recognition with Gemma



I've created a complete ASR (Automatic Speech Recognition) demo using Docker Compose with the following architecture:

🏗️ Architecture Overview

3 Microservices:

  1. Ollama Service - Runs Gemma 2:2B model for text enhancement
  2. ASR Service - FastAPI backend with Whisper for transcription
  3. Web UI - Nginx-served interactive frontend

🚀 Key Features

Audio Input:

  • ✅ Browser-based recording with microphone
  • ✅ File upload with drag & drop (MP3, WAV, M4A, OGG)

Processing Pipeline:

  • ✅ Whisper (tiny model) for fast speech-to-text
  • ✅ Ollama Gemma 2:2B for text enhancement and correction
  • ✅ Processing time tracking

User Experience:

  • ✅ Real-time recording with timer
  • ✅ Health status monitoring
  • ✅ Side-by-side comparison of raw vs enhanced text
  • ✅ Responsive modern UI

📁 Quick Setup

  1. Create project structure:
mkdir asr-demo && cd asr-demo

  1. Save all files to their respective directories:

    • docker-compose.yml in root
    • ASR service files in asr-service/
    • Web UI files in web-ui/

  2. Start services:

chmod +x startup.sh
./startup.sh start
# OR
docker-compose up --build -d
  1. Access demo: http://localhost:3000

🎯 Demo Optimizations

  • Small footprint - Uses Whisper tiny model and Gemma 2B
  • Fast startup - Optimized Docker layers
  • Resource efficient - ~4GB RAM requirement
  • Development friendly - Hot reload support

The demo showcases a complete speech-to-text pipeline with AI enhancement, perfect for understanding how modern ASR systems work with LLMs for text improvement!

You can find the code here.

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