Learn how AI-powered search lets you find content inside audio files. Scholaris indexes full content with semantic embeddings for meaning-based search.
The Problem with Traditional Audio Search
Audio files -- podcasts, recorded interviews, oral histories, field recordings -- are essentially black boxes for text-based search. You cannot search for a specific quote in a 2-hour interview recording without listening to the entire thing. Researchers working with qualitative data often spend more time finding specific segments than actually analyzing them. Manual transcription is expensive and slow; even automated tools often produce transcripts without timestamps or speaker labels.
How AI Search Works
AI audio search begins by converting speech to text using automatic speech recognition (ASR). Modern models like Parakeet TDT produce highly accurate transcripts with precise word-level timestamps. Speaker diarization then identifies who is speaking when. The resulting timestamped transcript is indexed with semantic embeddings, allowing you to search by meaning rather than exact words. Voice activity detection (Silero VAD) handles silence-aware chunking for long recordings.
Step-by-Step Workflow
1. **Upload your audio** -- Supports MP3, WAV, FLAC, OGG, M4A, and other common formats.
2. **Transcription with timestamps** -- Parakeet TDT generates a word-level transcript with accurate timestamps.
3. **Speaker identification** -- Speakers are automatically labeled (Speaker 1, Speaker 2, etc.).
4. **Semantic indexing** -- The transcript is chunked and embedded for meaning-based search.
5. **Search and navigate** -- Type your query and jump to the exact moment in the audio.
6. **Export transcript** -- Download the full transcript with timestamps and speaker labels.
Scholaris Capabilities
Scholaris transforms audio content into searchable, citable research material:
- **High-accuracy transcription**: Parakeet TDT provides excellent accuracy for English, with support for technical and academic vocabulary.
- **Word-level timestamps**: Every word is linked to its exact position in the audio.
- **Speaker diarization**: Automatically identify and label speakers.
- **Intelligent chunking**: Silero VAD detects speech boundaries for natural segmentation.
- **Semantic search**: Find content by meaning, not just keywords.
- **Long audio support**: Handles recordings of any length through silence-aware chunking.
Frequently Asked Questions
What languages does audio transcription support?
Parakeet TDT is optimized for English. For other languages, Scholaris falls back to Whisper Large V3, which supports 100+ languages.
Can I edit the generated transcript?
The transcript is generated for search and indexing purposes. You can export it and edit it in any text editor, then re-import the corrected version.
How does Scholaris handle background noise?
Silero VAD (Voice Activity Detection) filters out non-speech segments. The ASR model is trained on diverse audio conditions, but very noisy recordings may have lower transcription accuracy.
Search inside any document with AI
Scholaris uses AI-powered semantic search to find answers across PDFs, videos, audio, and more — all running locally on your machine.