The practical decision
A transcript for a one-person narration can be evaluated almost entirely on its words. Interviews require an additional dimension: identifying where one voice stops and another begins. OpenAI’s diarized transcription model produces a speaker-attributed output format, but the transcript should be treated as structured evidence requiring review, not a verified record of named identities.
What official documentation establishes
The Audio API documents a specific model name, gpt-4o-transcribe-diarize, and requires the diarized_json response format to receive speaker annotations. It also notes that inputs exceeding 30 seconds require a chunking strategy. This differs from assumptions carried over from other transcription models: the optional prompt field is not supported by this diarized model, and output format choices vary across model families.
A workflow that can be audited
For an interview archive, retain the source recording and create a transcription job containing the permitted audio format and appropriate chunking settings. Read returned segment timestamps and anonymous speaker labels into a review interface. A human editor should compare speaker changes against the waveform, correct proper nouns and tag audible overlaps. Attach actual speaker names only when those identities have been established by the recording owner.
Where the system can fail
Chunking can improve tractability but creates boundaries where a word is split or a speaker ID changes unexpectedly. Interruption, crosstalk and an interviewer speaking far from the microphone remain difficult conditions. A diarized speaker number is not biometric identification and should never be used by itself to accuse a person of speaking. Real-time session input transcription is also described as guidance, not necessarily an exact account of what the audio model perceived.
How to evaluate it without inventing tests
A robust evaluation splits errors into word accuracy, speaker assignment, time alignment and missed speech. For a small test, assemble consented two- and three-person recordings with known turn boundaries and include overlap. Measure diarization error separately from word error. Count speaker swaps even when every word is correct; accessibility captions can be confusing if speaker labels drift. No API calls or audio tests were executed in this editorial project.
Consent, licensing and operating costs
Production costs depend on the current API tariff, input duration and retries; do not assume a ChatGPT plan covers API requests. Handle recordings as potentially sensitive personal data, minimize retention and require an organizational processing policy. If automated decisions depend on speaker identity, use independently confirmed metadata and review, not diarization output alone.
Alternatives and the publication boundary
A local pyannote plus speech-recognition pipeline offers different control over hosting and components but requires integration work. A service with explicit diarized JSON can simplify downstream processing. The decisive factors are auditability, speaker error under actual recording conditions and access to the required region and endpoint.
Sources & Further Reading
- https://platform.openai.com/docs/api-reference/audio/json-object?lang=csharp (official documentation; checked 2026-10-25)
- https://platform.openai.com/docs/api-reference/realtime-client-events/session?lang=node.js (official documentation; checked 2026-10-25)
Editorial verification cutoff: 25 October 2026. First-party documentation, not hands-on testing. Current pricing and account eligibility must be confirmed before purchasing.