ScrAIbe grew out of a practical need in research projects: reliable transcripts for interviews, meetings, field recordings, and technical audio that generic speech-to-text tools did not handle well. I built it as an open-source pipeline for research audio, where noise, mixed German/English speech, and multiple speakers are common rather than exceptional.
ScrAIbe is a modular, multilingual transcription and speaker-processing pipeline:
- Whisper-based ASR for accurate transcription and optional segment-level translation.
- Speaker diarisation + recognition via Pyannote, with VoxCeleb embeddings for robust speaker separation.
- Automatic language identification using VoxLingua to handle mixed-language recordings cleanly.
- Multiple entry points: a Python API for full control, a CLI for batch jobs, and a lightweight Gradio app for quick local runs.
My goal with ScrAIbe is to make high-quality transcription reproducible, inspectable, and adaptable to project-specific requirements. For browser-based team workflows, the companion project ScrAIbe-WebUI provides a dedicated web interface on top of the same backend.