Artificial Intelligence

Artificial Intelligence experiments and prototypes

VoiceNotes

Completed

Speech-to-notes conversion using Whisper and LLM processing for intelligent content organization.

Date

2024-03

Duration

3 weeks

Team

solo

Difficulty

medium

View Source Code

Project Story

VoiceNotes captures spoken ideas and converts them into structured Markdown notes using advanced speech recognition. This tool was designed to bridge the gap between spontaneous thoughts and organized documentation.

VoiceNotes showing speech-to-text conversion and organization

The project uses OpenAI's Whisper API for accurate speech-to-text conversion, then processes the transcript with an LLM to extract key points and format the output into structured, readable notes.

Technical Details

Tech Stack

Python OpenAI Whisper LLM Processing Markdown Audio Processing

Key Features

✓ Local audio processing

✓ Speaker identification

✓ Automatic summarization

✓ Action item extraction

✓ Markdown formatting

✓ Timestamp preservation

✓ Batch processing support

Challenges Faced

⚠ Audio quality variations

⚠ Multiple speaker detection

⚠ Context window limitations

⚠ Real-time processing demands

Key Learnings

💡 Whisper accuracy is impressive for multiple speakers

💡 LLM context windows matter for long transcripts

💡 Audio quality directly impacts transcription accuracy

💡 Post-processing is crucial for usable notes

💡 User feedback loops improve accuracy

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