An ultra-lightweight Streamlit application that transforms meeting recordings and transcripts into actionable insights with instant processing and zero heavy dependencies.

• Multi-format support: MP3, WAV, M4A, OGG, FLAC
• Chunked transcription: Handles long recordings without truncation
• Progress tracking: Real-time transcription progress
• High-quality speech recognition: Google Speech Recognition API

• Instant summaries: Ultra-fast NLP processing (0.02 seconds)
• Smart action extraction: Automatically identifies tasks, owners, deadlines, and priorities
• Real-time analytics: Meeting type, sentiment, and productivity scoring
• Lightweight processing: No heavy models, minimal dependencies, maximum speed

• Chunked TTS: Handles long text without truncation
• Multiple formats: WAV and MP3 output
• Audio preview: Built-in audio player
• File management: Automatic cleanup and download options

• Multipage design: Organized navigation with dedicated pages
• Professional UI: Modern gradients, cards, and responsive layouts
• Progress indicators: Visual feedback for all operations
• User-friendly: Intuitive design with helpful tooltips and guides

# Clone or download the project
cd meeting_action_extractor

# Create virtual environment (optional)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install minimal dependencies (under 2 minutes)
pip install -r requirements.txt

streamlit run streamlit_app.py

streamlit run streamlit_app.py

1. Push your code to GitHub
2. Connect your repository to Streamlit Cloud
3. Add your secrets via the dashboard or .streamlit/secrets.toml
4. Deploy with one click!

The app can be deployed on any platform that supports Python and Streamlit:

• Heroku: Use Procfile and requirements.txt
• Railway: Direct GitHub integration
• Render: Web service deployment
• Docker: Containerized deployment

• Welcome screen with feature overview
• Sample transcript loader
• Navigation guide
• How-it-works explanation

• Text Input: Paste meeting transcripts directly
• Audio Input: Upload audio files for transcription
• AI Analysis: Generate summaries and extract action items
• Export Options: Download results as CSV or JSON

• File Upload: Convert text files to audio
• Text Input: Paste text directly for conversion
• Format Selection: Choose WAV or MP3 output
• Audio Preview: Listen before downloading

• Detailed feature descriptions
• Technology stack information
• Setup instructions
• Usage tips and best practices

The application uses optimized NLP techniques for:

• Instant Processing: 0.02-second summary generation with professional quality
• Smart Pattern Recognition: Efficient action item detection with priority analysis
• Real-time Analytics: Meeting type, sentiment, and productivity scoring
• Intelligent Parsing: Automatic speaker detection and content organization
• Frequency Analysis: Statistical keyword extraction and topic identification

Ultra-lightweight - no heavy models, instant processing, runs anywhere.

For MP3 export in Text-to-Speech:

• Install ffmpeg on your system
• Ensure pydub can access it

WAV format works without additional setup.

• Streamlit: Modern web interface framework
• pandas: Data manipulation and export
• Custom NLP Engine: Lightning-fast text processing
• SpeechRecognition: Audio transcription
• pydub: Audio processing and format conversion
• pyttsx3: Text-to-speech synthesis

• Instant Processing: 0.02-second summary generation with professional quality
• Smart Pattern Matching: Efficient action item extraction with priority assignment
• Real-time Analytics: Meeting intelligence without heavy model dependencies
• Google Speech Recognition: Reliable audio transcription service

1. Navigate to Summarizer page
2. Switch to Audio Input tab
3. Upload your meeting recording
4. Click Transcribe Audio
5. Review generated transcript
6. Generate summary and action items

1. Go to Summarizer page
2. Use Text Input tab
3. Paste your meeting transcript
4. Enable Enhanced NLP Analysis (recommended)
5. Click Generate Summary & Actions
6. Download results in preferred format

1. Visit Text-to-Speech page
2. Upload a text file or paste content
3. Choose output format (WAV/MP3)
4. Click Convert to Audio
5. Preview and download the audio file

• Use clear recordings for better transcription accuracy
• Ensure good microphone quality and minimal background noise
• Consider speaker separation for multi-person meetings

• Use speaker labels: John: Let's discuss the project timeline
• Include timestamps if available
• Separate different topics with line breaks

• Lightning-fast processing: summaries in 0.02 seconds
• No model downloads or heavy dependencies required
• Runs efficiently on any device with minimal resources

Transcription fails:

• Check audio file format compatibility
• Ensure stable internet connection (for Google Speech Recognition)
• Try shorter audio segments

Processing tips:

• Use clear speaker labels (Name: content format)
• Ensure proper sentence structure for better analysis
• UTF-8 encoding recommended for best results

Performance optimization:

• App processes instantly with minimal resource usage
• No heavy model downloads or complex setup required
• Works reliably on any device or platform

meeting_action_extractor/
├── streamlit_app.py          # Main app entry point
├── pages/                    # Multipage structure
│   ├── 1_🔎_Summarizer.py    # Analysis page
│   ├── 2_🗣️_Text_to_Speech.py # TTS page
│   └── 3_ℹ️_About.py         # Information page
├── audio_processor.py        # Audio transcription logic
├── extractor.py             # Action item extraction
├── nlp_summarizer.py        # Fast NLP processing engine
├── text_to_audio.py         # TTS functionality
├── utils.py                 # Utility functions
├── requirements.txt         # Dependencies
└── README.md               # This file

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

This project is open source and available under the MIT License.

Built with ❤️ using Streamlit • Lightning-Fast Meeting Intelligence v3.0