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How to use the GPT-4o Realtime API for speech and audio (Preview)

  • Artikkeli

Note

This feature is currently in public preview. This preview is provided without a service-level agreement, and we don't recommend it for production workloads. Certain features might not be supported or might have constrained capabilities. For more information, see Supplemental Terms of Use for Microsoft Azure Previews.

Azure OpenAI GPT-4o Realtime API for speech and audio is part of the GPT-4o model family that supports low-latency, "speech in, speech out" conversational interactions. The GPT-4o Realtime API is designed to handle real-time, low-latency conversational interactions. Realtime API is a great fit for use cases involving live interactions between a user and a model, such as customer support agents, voice assistants, and real-time translators.

Most users of the Realtime API need to deliver and receive audio from an end-user in real time, including applications that use WebRTC or a telephony system. The Realtime API isn't designed to connect directly to end user devices and relies on client integrations to terminate end user audio streams.

Supported models

Currently only gpt-4o-realtime-preview version: 2024-10-01-preview supports real-time audio.

The gpt-4o-realtime-preview model is available for global deployments in East US 2 and Sweden Central regions.

Important

The system stores your prompts and completions as described in the "Data Use and Access for Abuse Monitoring" section of the service-specific Product Terms for Azure OpenAI Service, except that the Limited Exception does not apply. Abuse monitoring will be turned on for use of the gpt-4o-realtime-preview API even for customers who otherwise are approved for modified abuse monitoring.

API support

Support for the Realtime API was first added in API version 2024-10-01-preview.

Note

For more information about the API and architecture, see the Azure OpenAI GPT-4o real-time audio repository on GitHub.

Get started

Before you can use GPT-4o real-time audio, you need:

Here are some of the ways you can get started with the GPT-4o Realtime API for speech and audio:

Connection and authentication

The Realtime API (via /realtime) is built on the WebSockets API to facilitate fully asynchronous streaming communication between the end user and model.

Important

Device details like capturing and rendering audio data are outside the scope of the Realtime API. It should be used in the context of a trusted, intermediate service that manages both connections to end users and model endpoint connections. Don't use it directly from untrusted end user devices.

The Realtime API is accessed via a secure WebSocket connection to the /realtime endpoint of your Azure OpenAI resource.

You can construct a full request URI by concatenating:

  • The secure WebSocket (wss://) protocol
  • Your Azure OpenAI resource endpoint hostname, for example, my-aoai-resource.openai.azure.com
  • The openai/realtime API path
  • An api-version query string parameter for a supported API version such as 2024-10-01-preview
  • A deployment query string parameter with the name of your gpt-4o-realtime-preview model deployment

The following example is a well-constructed /realtime request URI:

wss://my-eastus2-openai-resource.openai.azure.com/openai/realtime?api-version=2024-10-01-preview&deployment=gpt-4o-realtime-preview-deployment-name

To authenticate:

  • Microsoft Entra (recommended): Use token-based authentication with the /realtime API for an Azure OpenAI Service resource with managed identity enabled. Apply a retrieved authentication token using a Bearer token with the Authorization header.
  • API key: An api-key can be provided in one of two ways:
    • Using an api-key connection header on the prehandshake connection. This option isn't available in a browser environment.
    • Using an api-key query string parameter on the request URI. Query string parameters are encrypted when using https/wss.

Realtime API architecture

Once the WebSocket connection session to /realtime is established and authenticated, the functional interaction takes place via events for sending and receiving WebSocket messages. These events each take the form of a JSON object.

Diagram of the Realtime API authentication and connection sequence.

Events can be sent and received in parallel and applications should generally handle them both concurrently and asynchronously.

  • A client-side caller establishes a connection to /realtime, which starts a new session.
  • A session automatically creates a default conversation. Multiple concurrent conversations aren't supported.
  • The conversation accumulates input signals until a response is started, either via a direct event by the caller or automatically by voice activity detection (VAD).
  • Each response consists of one or more items, which can encapsulate messages, function calls, and other information.
  • Each message item has content_part, allowing multiple modalities (text and audio) to be represented across a single item.
  • The session manages configuration of caller input handling (for example, user audio) and common output generation handling.
  • Each caller-initiated response.create can override some of the output response behavior, if desired.
  • Server-created item and the content_part in messages can be populated asynchronously and in parallel. For example, receiving audio, text, and function information concurrently in a round robin fashion.

Session configuration

Often, the first event sent by the caller on a newly established /realtime session is a session.update payload. This event controls a wide set of input and output behavior, with output and response generation properties then later overridable using the response.create event.

The session.update event can be used to configure the following aspects of the session:

  • Transcription of user input audio is opted into via the session's input_audio_transcription property. Specifying a transcription model (whisper-1) in this configuration enables the delivery of conversation.item.audio_transcription.completed events.
  • Turn handling is controlled by the turn_detection property. This property can be set to none or server_vad as described in the input audio buffer and turn handling section.
  • Tools can be configured to enable the server to call out to external services or functions to enrich the conversation. Tools are defined as part of the tools property in the session configuration.

An example session.update that configures several aspects of the session, including tools, follows. All session parameters are optional and can be omitted if not needed.

{
  "type": "session.update",
  "session": {
    "voice": "alloy",
    "instructions": "Call provided tools if appropriate for the user's input.",
    "input_audio_format": "pcm16",
    "input_audio_transcription": {
      "model": "whisper-1"
    },
    "turn_detection": {
      "threshold": 0.4,
      "silence_duration_ms": 600,
      "type": "server_vad"
    },
    "tools": [
      {
        "type": "function",
        "name": "get_weather_for_location",
        "description": "gets the weather for a location",
        "parameters": {
          "type": "object",
          "properties": {
            "location": {
              "type": "string",
              "description": "The city and state such as San Francisco, CA"
            },
            "unit": {
              "type": "string",
              "enum": [
                "c",
                "f"
              ]
            }
          },
          "required": [
            "location",
            "unit"
          ]
        }
      }
    ]
  }
}

Input audio buffer and turn handling

The server maintains an input audio buffer containing client-provided audio that has not yet been committed to the conversation state.

One of the key session-wide settings is turn_detection, which controls how data flow is handled between the caller and model. The turn_detection setting can be set to none or server_vad (to use server-side voice activity detection).

Without server decision mode

By default, the session is configured with the turn_detection type effectively set to none.

The session relies on caller-initiated input_audio_buffer.commit and response.create events to progress conversations and produce output. This setting is useful for push-to-talk applications or situations that have external audio flow control (such as caller-side VAD component). These manual signals can still be used in server_vad mode to supplement VAD-initiated response generation.

Diagram of the Realtime API input audio sequence without server decision mode.

Server decision mode

The session can be configured with the turn_detection type set to server_vad. In this case, the server evaluates user audio from the client (as sent via input_audio_buffer.append) using a voice activity detection (VAD) component. The server automatically uses that audio to initiate response generation on applicable conversations when an end of speech is detected. Silence detection for the VAD can be configured when specifying server_vad detection mode.

Diagram of the Realtime API input audio sequence with server decision mode.

Conversation and response generation

You can have one active conversation per session. The conversation accumulates input signals until a response is started, either via a direct event by the caller or automatically by voice activity detection (VAD).

Optionally, the client can truncate or delete items in the conversation:

Diagram of the Realtime API conversation item sequence.

Response interuption

The client response.cancel event is used to cancel an in-progress response.

A user might want to interrupt the assistant's response or ask the assistant to stop talking. The server produces audio faster than realtime. The client can send a conversation.item.truncate event to truncate the audio before it's played.

  • The server's understanding of the audio with the client's playback is synchronized.
  • Truncating audio deletes the server-side text transcript to ensure there isn't text in the context that the user doesn't know about.
  • The server responds with a conversation.item.truncated event.

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