v1.0.0 - Initial Release

Release Notes - v1.0.0 (Initial Release)

The First Stable Release 🎉
Production-Ready Audio Transcription & Text Reconstruction Service

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🚀 New Features

Core Functionality

• Whisper Model Integration: Full support for OpenAI's Whisper speech-to-text models (base, small, medium, large)
• Gemma Reconstruction: Text refinement using Google's Gemma-2b-it LLM
• Multi-Format Processing:
  • Direct file uploads via /transcribe endpoint
  • Remote URL processing via /pull endpoint

API Capabilities

• 🔐 API key authentication middleware
• 🚦 Rate limiting (10,000 requests/hour)
• 🌐 Multi-language support (Ukrainian/Russian primary focus)
• 🔍 Keyword spotting with confidence scoring
• ⏱️ Processing time metrics in all responses

Infrastructure

• 🧠 Smart model caching system with:
  • Automatic GPU/CPU fallback
  • Memory optimization
  • Concurrent request safety
• 📊 Detailed logging (app.log & request logs)
• 🐳 Production-ready Gunicorn configuration

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⚠️ Known Issues

Performance

• Initial model load time can be slow (~30-60s for large Whisper models)
• Gemma models requires authorization for downloading
• Gemma-2b-it requires >8GB GPU RAM for optimal performance
• No native AAC audio support - requires FFmpeg preprocessing

Limitations

• Maximum file size hard-capped at 50MB
• Keyword spotting accuracy decreases with homophones
• UI only supports basic upload functionality

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🛠️ Upgrade Guide

New Requirements

# Required system packages
sudo apt-get install ffmpeg python3-dev

Critical Configuration

# .env changes from pre-1.0 versions
API_KEY_ENABLED=true # Now required by default
MAX_CONTENT_LENGTH=52428800 # Explicit size limit

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📦 Installation

# For first-time users
git clone https://github.com/yourorg/audio-transcription-service.git
cd audio-transcription-service
pip install -r requirements.txt

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🙌 Acknowledgments

• OpenAI for Whisper speech recognition models
• Google Research for Gemma language models
• Flask & Torch communities for foundational libraries

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First production release marks completion of core feature set. Subsequent releases will focus on performance optimization and expanded language support.