AI Speech-to-Text Web App: Summary and Educational Value
In this final guide we summarize our speech-to-text web application, describe its features and show how it creates value in educational environments.
Project Summary
The application:
- Runs entirely locally without sending data externally
- Records audio via microphone
- Uses the Whisper model for real-time transcription
- Has a clean and responsive user interface
Functionality
Audio Recording
- Start and stop with one button
- Visual feedback during recording
Speech Recognition
- Support for multiple languages
- High accuracy even with different accents
User Interface
- Progress indicators
- Copy to clipboard and clear functions
- All processing happens locally
Technical Highlights
- Flask backend
- Whisper model
- Efficient memory management
Educational Uses
- Accessibility - captioned lectures and discussions
- Note-taking - automatic transcriptions for students
- Language learning - pronunciation practice and exercises
- Privacy - sensitive conversations stay on the device
- Efficiency - documentation of meetings and feedback
Limitations
- Specialized terminology may require manual correction
- Multiple simultaneous speakers reduce accuracy
- Long recordings require more system resources
Future Improvements
- Speaker identification (diarization)
- Continuous real-time transcription
- Export to PDF and Word
- Integrated translation
- Custom vocabulary
Conclusion
By combining locally running AI with a user-friendly interface, the application offers a powerful and privacy-secure tool for education.