A new transcription service integrated directly into Kagi's ecosystem that allows users to upload audio/video files for AI transcription, which can then seamlessly feed into existing Kagi tools like the Universal Summarizer and Assistant. The service would support multiple languages and include monthly usage limits to maintain service quality and prevent abuse.
Detailed Implementation & Usage Scenarios:
Core Functionality:
- Upload interface supporting common audio/video formats (.mp3, .wav, .mp4, etc.)
- Progress indicator showing transcription status
- Monthly quota system (e.g., 120 minutes per month for premium users)
- Language auto-detection with manual override option
- Export options (TXT, SRT, VTT formats)
Integration with Existing Kagi Services:
- Direct "Summarize Transcript" button to send to Universal Summarizer
- Option to analyze transcript with Kagi Assistant for deeper insights
- Searchable transcript archive in user's account
- Ability to share transcripts via secure links
User Workflows:
- Academic: Students transcribing lecture recordings for study notes
- Professional: Converting meeting recordings into searchable text
- Content Creators: Generating subtitles for videos
- Researchers: Transcribing interviews and field recordings
Similar Implementation Examples:
Like Alice App, the system could offer:
- Real-time transcription progress
- Speaker diarization (identifying different speakers)
- Punctuation and formatting
- Confidence scores for transcribed segments
- Edit interface for transcript correction
Technical Considerations:
- API rate limiting
- File size restrictions (e.g., max 2GB per file)
- Supported audio codecs
- Privacy/encryption of uploaded content
- Temporary file storage policy
Pricing Structure Integration:
- Include basic minutes in existing Kagi subscription tiers
- Additional minutes available for purchase
- Enterprise options for high-volume users
This feature would significantly enhance Kagi's existing tools by providing a complete solution for converting spoken content into analyzable text, making it more accessible and useful within their ecosystem.
