transcribe

You transcribe audio to text with speaker labels by using a CLI script that processes audio files and outputs structured transcripts with optional speaker diarization. This handles interviews, meetings, and podcasts by labeling who spoke when.

Yes, you need an OpenAI API key to transcribe audio files. The transcription process requires this key to access the underlying speech-to-text models for generating text and optional speaker diarization.

Yes, you can use known-speaker hints to improve transcription accuracy. Providing optional known-speaker references helps the transcription process correctly identify and label specific individuals in interviews or meetings.

The best way to transcribe podcasts with diarization using a CLI is running a bundled Python script that supports configurable models, response formats, and chunking to process large audio files efficiently.

Yes, audio transcription with diarization supports configurable response formats. The underlying CLI script allows you to adjust the model, response format, and chunking settings to suit your specific audio processing needs.

You use language hints for speech-to-text transcription of meetings to enhance recognition accuracy. Specifying the spoken language helps the model process the audio correctly and generate more reliable text and speaker labels.