Deposition recording
AI Models & Assistants / Knowledge library
GPT-4o Transcribe Diarize
Transcription model that identifies who's speaking when
Why learn GPT-4o Transcribe Diarize?
Offers the documented OpenAI model capabilities through hosted.
The problem it solves
Provides model inference for supported text / audio inputs.
Where it came from
GPT-4o Transcribe Diarize: Transcription model that identifies who's speaking when Published aliases: gpt-4o-transcribe-diarize. Input: text, audio. Output: text. Checked 2026-10-13. Model capabilities are distinct from tools available in a particular agent.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Explain what GPT-4o Transcribe Diarize does.
- Identify a use case for GPT-4o Transcribe Diarize.
- Describe a limitation of GPT-4o Transcribe Diarize.
- Name a compatible tool for GPT-4o Transcribe Diarize.
- Compare GPT-4o Transcribe Diarize against an alternative.
Documentation and sources
- Primary source / model card (developers.openai.com)
Catalog research notes
- See the AI Workbench for source dates, pricing evidence and configuration details.
Connect the ideas
Built on
Real World Example
Automated Legal Deposition Indexing
Legal firms conducting multi-party depositions.
The business problem
Manual review of long-form audio recordings to identify specific speaker contributions is time-consuming and prone to human error, leading to delays in case preparation.
How this topic solves it
The GPT-4o Transcribe Diarize model automates the conversion of audio to text while simultaneously tagging segments with unique speaker identifiers, enabling structured transcripts that map dialogue to specific participants.
- Capture high-fidelity audio of the deposition session.
- Submit the audio file to the Transcription API using the gpt-4o-transcribe-diarize model ID.
- Receive the structured output containing text segments mapped to speaker labels.
- Parse the diarized output to populate a searchable database for legal review.
The flow from audio input to structured legal data.
gpt-4o-transcribe-diarize
Text with speaker tags
Searchable testimony
- Audio Source → Uploads audio → Transcription API
- Transcription API → Generates segments → Diarized Transcript
- Diarized Transcript → Indexes content → Legal Database
Expected business outcome
Legal teams gain a searchable, chronologically accurate record of testimony that eliminates the need for manual transcription review.
Limitations and checks
The model may struggle with overlapping speech or high-noise environments, which can be verified by reviewing the confidence scores of the speaker segments in the output.