QUICKSTART.md

Quick Start Guide

Get your Video Transcription Service running in 5 minutes!

🚀 Option 1: Automated Setup (Recommended)

# 1. Run the setup script
python setup.py

# 2. Activate virtual environment
# Windows:
venv\Scripts\activate
# macOS/Linux:
source venv/bin/activate

# 3. Start the service (robust startup prevents restarts)
python start_robust.py

🛠️ Option 2: Manual Setup

# 1. Create virtual environment
python -m venv venv

# 2. Activate virtual environment
# Windows:
venv\Scripts\activate
# macOS/Linux:
source venv/bin/activate

# 3. Install dependencies
pip install -r requirements.txt

# 4. Install FFmpeg
# Windows: Download from https://ffmpeg.org/download.html
# macOS: brew install ffmpeg
# Linux: sudo apt-get install ffmpeg

# 5. Start the service
python start_robust.py  # Prevents restarts
# OR
python main.py         # Standard startup

🧪 Test Your Service

Option A: Web Interface

1. Open http://localhost:8000/docs
2. Click "Try it out" on POST /transcribe
3. Upload a video file
4. Copy the returned ID
5. Use GET /transcribe/{id} to check status

Option B: Command Line

# Test with example client
python example_client.py your_video.mp4

# Or test the API directly
python test_api.py your_video.mp4

# Monitor transcription progress in real-time
python log_monitor.py upload your_video.mp4

Option C: cURL

# Upload video
curl -X POST "http://localhost:8000/transcribe" \
  -F "file=@your_video.mp4" \
  -F "language=en"

# Check status (replace 1 with your ID)
curl "http://localhost:8000/transcribe/1"

🌐 Deploy to Render.com

# 1. Push to GitHub
git init
git add .
git commit -m "Initial commit"
git remote add origin https://github.com/yourusername/your-repo.git
git push -u origin main

# 2. Go to render.com
# 3. Create new Web Service
# 4. Connect your GitHub repo
# 5. Deploy!

📋 What You Get

• Free transcription using OpenAI Whisper
• No API limits - completely free
• Multiple formats - MP4, AVI, MOV, etc.
• Auto language detection or specify language
• REST API with automatic documentation
• Rate limiting and error handling
• Ready for production deployment

🔧 Configuration

Edit config.py to customize:

• File size limits
• Supported formats
• Whisper model size
• Rate limiting
• Cleanup intervals

📊 Monitoring & Logging

Enable detailed logging:

DEBUG=true python main.py

Monitor transcription progress:

# Test service
python log_monitor.py test

# Upload and monitor
python log_monitor.py upload video.mp4

# Monitor existing transcription
python log_monitor.py monitor 123

Log to file:

LOG_TO_FILE=true python main.py

📖 Need Help?

• Full documentation: See README.md
• Deployment guide: See DEPLOYMENT.md
• API docs: http://localhost:8000/docs (when running)
• Health check: http://localhost:8000/health

🎯 Common Issues

"Service keeps restarting"

• Run: python start_robust.py for automatic optimization
• See: RESTART_TROUBLESHOOTING.md

"NumPy compatibility error"

• Run: python fix_numpy.py to fix automatically

"FFmpeg not found"

• Install FFmpeg for your OS (see setup instructions)

"File too large"

• Default limit is 100MB (configurable in config.py)

"Service sleeping on Render"

• Free tier sleeps after 15min inactivity (normal behavior)

"Slow first request"

• AI model loads on first use (30-60 seconds)

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Ready to transcribe? Your service is now running at http://localhost:8000! 🎉