Raw voice input
AI Models & Assistants / Knowledge library
GPT-Live-Transcribe
Low-latency speech-to-text model for realtime transcription
Why learn GPT-Live-Transcribe?
Offers the documented OpenAI model capabilities through hosted.
The problem it solves
Provides model inference for supported audio / text inputs.
Where it came from
GPT-Live-Transcribe: Low-latency speech-to-text model for realtime transcription Published aliases: gpt-live-transcribe. Input: audio, text. 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-Live-Transcribe does.
- Identify a use case for GPT-Live-Transcribe.
- Describe a limitation of GPT-Live-Transcribe.
- Name a compatible tool for GPT-Live-Transcribe.
- Compare GPT-Live-Transcribe 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
Multilingual Emergency Dispatch Coordination
Emergency dispatch centers managing high-volume, multi-language distress calls.
The business problem
Dispatchers struggle to maintain situational awareness when callers speak non-native languages, leading to critical delays in information relay and emergency response coordination.
How this topic solves it
The GPT-Live-Transcribe model provides a streaming speech-to-text interface that delivers low-latency transcript deltas, allowing dispatchers to read live translations and transcriptions of incoming audio in real-time.
- Initialize a session via the v1/realtime/transcription_sessions endpoint.
- Stream raw audio input from the caller to the model endpoint.
- Configure keyword hints specific to emergency terminology to improve accuracy.
- Process the returned text deltas to update the dispatcher console interface.
- Terminate the session once the emergency call concludes.
This diagram illustrates the flow of audio data from the caller to the dispatcher console.
Streaming transcription engine
Real-time text display
- Caller Audio → streams audio → GPT-Live-Transcribe
- GPT-Live-Transcribe → emits text deltas → Dispatcher Console
Expected business outcome
Dispatchers gain immediate, readable context from diverse callers, significantly reducing the cognitive load and time required to assess emergency situations.
Limitations and checks
The model relies on streaming audio, which can introduce latency if network conditions are unstable; developers should monitor the delta arrival rate to detect potential synchronization gaps.