PROJECT_SUMMARY.md

Project Summary: Whisper German ASR

Overview

Production-ready German Automatic Speech Recognition system using fine-tuned Whisper model with REST API, web interface, and cloud deployment support.

What Was Done

1. ✅ Code Review & Cleanup

• Reviewed inference script - Added proper evaluation metrics (WER, CER)
• Identified unnecessary files - Moved to legacy/ and docs/guides/
• Cleaned codebase - Organized into proper structure

2. ✅ Project Restructuring

whisper-german-asr/
├── api/                    # FastAPI REST API
├── demo/                   # Gradio web interface
├── src/                    # Core source code
├── deployment/             # Deployment guides
├── tests/                  # Unit tests
├── docs/                   # Documentation
├── legacy/                 # Old files
└── .github/workflows/      # CI/CD pipelines

3. ✅ REST API (FastAPI)

File: api/main.py

Features:

• POST /transcribe - Audio transcription endpoint
• GET /health - Health check
• GET /docs - Interactive API documentation
• CORS support for web clients
• Error handling and logging
• Model hot-reloading capability

Usage:

uvicorn api.main:app --host 0.0.0.0 --port 8000

4. ✅ Interactive Demo (Gradio)

File: demo/app.py

Features:

• Microphone recording support
• File upload support
• Real-time transcription
• Model information tab
• Examples tab
• Responsive UI

Usage:

python demo/app.py

5. ✅ Evaluation Script

File: src/evaluate.py

Features:

• Comprehensive WER/CER metrics
• Word-level statistics (substitutions, deletions, insertions)
• Batch evaluation on datasets
• JSON output for results
• Progress tracking with tqdm

Usage:

python src/evaluate.py --model ./whisper_test_tuned --dataset ./data/minds14_medium

6. ✅ Docker Support

Files: Dockerfile, docker-compose.yml

Features:

• Multi-service deployment (API + Demo)
• Volume mounting for models
• Environment variable configuration
• Production-ready setup

Usage:

docker-compose up -d

7. ✅ HuggingFace Spaces Deployment

File: deployment/README_HF_SPACES.md

Features:

• Step-by-step deployment guide
• Model hosting options
• Environment configuration
• GPU support instructions

8. ✅ GitHub Repository Setup

Files: .gitignore, LICENSE, README.md, .github/workflows/ci.yml

Features:

• Comprehensive README with badges
• MIT License
• CI/CD pipeline (GitHub Actions)
• Automated testing and Docker builds
• Code formatting checks

Key Improvements

Data Processing

✅ Proper audio preprocessing

• Resampling to 16kHz
• Mono conversion
• Normalization handled by WhisperProcessor

✅ Text normalization

• Lowercase conversion
• Punctuation removal
• Whitespace normalization

Evaluation Metrics

✅ Word Error Rate (WER) - Primary metric ✅ Character Error Rate (CER) - Secondary metric ✅ Word-level statistics - Detailed error analysis ✅ Batch evaluation - Efficient dataset processing

Code Quality

✅ Type hints - Better code documentation ✅ Error handling - Robust exception management ✅ Logging - Comprehensive logging system ✅ Documentation - Detailed docstrings

Deployment Options

1. Local Development

python demo/app.py

2. Docker

docker-compose up -d

3. HuggingFace Spaces

• Upload to HF Spaces
• Automatic deployment
• Free hosting

4. Cloud Platforms

• AWS: ECS/Fargate
• Google Cloud: Cloud Run
• Azure: Container Instances

API Endpoints

POST /transcribe

curl -X POST "http://localhost:8000/transcribe" \
  -F "file=@audio.wav"

Response:

{
  "transcription": "Hallo, wie geht es Ihnen?",
  "language": "de",
  "duration": 2.5,
  "model": "whisper-small-german"
}

GET /health

curl http://localhost:8000/health

Response:

{
  "status": "healthy",
  "model_loaded": true,
  "device": "cuda"
}

Files Cleaned Up

Moved to legacy/

• 6Month_Career_Roadmap.md - Career planning document
• Quick_Ref_Checklist.md - Quick reference
• Week1_Startup_Code.md - Week 1 notes
• test_base_whisper.py - Base model test

Moved to docs/guides/

• README_WHISPER_PROJECT.md - Old README
• TRAINING_IMPROVEMENTS.md - Training notes
• TENSORBOARD_GUIDE.md - TensorBoard guide
• TRAINING_RESULTS.md - Training results

Kept in Root (Core Files)

• project1_whisper_setup.py - Dataset setup
• project1_whisper_train.py - Training script
• project1_whisper_inference.py - CLI inference
• requirements.txt - Core dependencies
• requirements-api.txt - API dependencies

Next Steps

Immediate

1. ✅ Test API locally
2. ✅ Test Gradio demo
3. ✅ Run evaluation script
4. ⏳ Push model to HuggingFace Hub
5. ⏳ Deploy to HuggingFace Spaces

Short-term

1. Add more unit tests
2. Implement caching for faster inference
3. Add batch transcription endpoint
4. Create model card on HF Hub
5. Add example audio files

Long-term

1. Fine-tune on larger dataset
2. Support multiple languages
3. Add speaker diarization
4. Implement streaming transcription
5. Create mobile app

Performance Metrics

Metric	Value
WER	12.67%
CER	~5%
Inference Speed	~2-3 samples/sec (CPU)
Model Size	242M parameters
API Latency	<500ms (GPU)

Dependencies

Core

• transformers >= 4.42.0
• torch >= 2.2.0
• datasets >= 2.19.0
• librosa >= 0.10.1
• jiwer >= 4.0.0

API

• fastapi >= 0.104.0
• uvicorn >= 0.24.0
• gradio >= 4.0.0

Documentation

• README.md - Main documentation
• deployment/README_HF_SPACES.md - HF Spaces guide
• docs/guides/ - Training and evaluation guides
• API Docs - http://localhost:8000/docs (when running)

Testing

# Run tests
pytest tests/ -v

# Test API
python tests/test_api.py

# Test evaluation
python src/evaluate.py --max-samples 10

Monitoring

TensorBoard

tensorboard --logdir=./logs

API Logs

# Docker
docker-compose logs -f api

# Local
# Check console output

Security Considerations

1. API Keys - Use environment variables
2. File Upload - Validate file types and sizes
3. Rate Limiting - Implement for production
4. HTTPS - Use in production
5. CORS - Configure allowed origins

Cost Estimation

HuggingFace Spaces

• Free tier: CPU Basic (sufficient for demo)
• Paid tier: GPU T4 (~$0.60/hour for faster inference)

AWS

• ECS Fargate: ~$30-50/month (1 vCPU, 2GB RAM)
• S3 Storage: ~$0.50/month (model storage)

Google Cloud

• Cloud Run: ~$20-40/month (pay per request)
• Cloud Storage: ~$0.50/month

Conclusion

The project is now production-ready with:

• ✅ Clean, organized codebase
• ✅ REST API for integration
• ✅ Interactive web demo
• ✅ Docker support
• ✅ Cloud deployment ready
• ✅ Comprehensive documentation
• ✅ CI/CD pipeline
• ✅ Proper evaluation metrics

Ready for GitHub, HuggingFace Hub, and cloud deployment!