transcribe

You can transcribe audio recordings locally with speaker labels by using the Whisper model on-device, applying optional pyannote diarization, and generating a Word document with timestamps and speaker attribution.

Yes, you can transcribe video files without uploading data to the cloud by running the Whisper model entirely on your local machine, ensuring no audio or video data leaves your environment during processing.

Speaker diarization for transcription in a Word document works by using pyannote to identify distinct speakers, adding timestamps, and formatting readable speaker turns directly within the generated .docx file.

Yes, you need faster-whisper and python-docx to generate transcripts with timestamps, along with pydub for audio processing, as these dependencies handle file resolution, model loading, transcription, and document generation.

The best way to format interview transcripts into a professional document is using local Whisper transcription with speaker diarization to output a .docx file containing metadata, executive notes, and the full transcript ready for review.

Processing long video recordings for local transcription may be limited by your machine's hardware resources, as running the Whisper model and diarization entirely on-device requires significant compute capacity for extended audio files.