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World desk5 min

Building a Voice Layer for an App: TTS, ASR, or Realtime?

A practical guide to choosing a voice workflow, selecting WebRTC, WebSockets or SIP, handling live transcripts, protecting credentials and testing real-world audio.

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Choose the voice workflow before choosing its transport. For live, spoken back-and-forth, a direct speech-to-speech session is designed to handle the conversation without requiring separate speech-to-text and text-to-speech stages. Use a composed TTS-and-ASR pipeline when you need a transcript, want to retain an existing text agent, or are building a task such as captions or narration rather than a spoken conversation.

Which voice workflow does your app need?

“Voice” can mean several different jobs. Pick the one your feature actually performs; TTS and ASR are not mandatory stages for every voice application. OpenAI’s audio and voice overview describes separate workflows for these use cases and currently recommends GPT-Live for a new conversational voice application. The Realtime API remains an option when its session and tool model fits your needs.

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App feature Workflow to consider Why
Live spoken conversation with an assistant Direct speech-to-speech with a Realtime session, or the conversational workflow recommended in the audio overview The model handles voice-to-voice interaction without a required intermediate ASR or TTS step. The conversation guide says this can reduce latency and preserve tone and inflection information; it does not establish a universal end-to-end speed advantage.
Voice input for an existing text agent Speech-to-text → existing text-agent workflow → text-to-speech, if the agent should answer aloud The composed pipeline lets the existing text workflow remain in the middle and gives the app an explicit transcript, but the app must orchestrate the stages.
Live captions or speech input without an assistant speaking back Live transcription Use the transcription workflow when the desired output is text rather than an assistant voice turn.
Transcribe a recording File transcription This is a recorded-audio task, not a live conversational session.
Read prepared text aloud Text-to-speech Use speech generation directly when you need narration without a conversational agent.

These workflows are not interchangeable just because each involves audio. In particular, a direct speech-to-speech session and a cascade through a text agent have different control points. Measure complete user-perceived turn time in your target app rather than assuming one architecture will always be faster.

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Choose a transport for where audio runs

Once the workflow is clear, choose the transport that matches the app’s audio path. OpenAI’s audio overview points browser voice toward WebRTC, server-side audio pipelines toward WebSockets, and phone integrations toward SIP. These transports do not share an interchangeable handshake or event format; follow the connection guide for the specific API and transport you choose.

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Use case Transport What the app manages
Browser voice WebRTC Negotiated media tracks carry audio; a data channel carries application events, such as transcripts and session updates.
Server-side audio pipeline WebSockets The application sends and handles audio chunks and events.
Phone integration SIP Use the SIP connection path documented for the selected API.

The OpenAI Agents SDK’s voice-agent guide describes WebRTC as a lower-friction browser option that handles audio input and output. WebSockets offer more control, but the application must manage capture and playback. The right trade-off depends on whether you need that control and where you want session logic to execute.

Connect browser audio with WebRTC

In a browser WebRTC flow, keep the two channels distinct: negotiated media tracks carry audio, while the data channel carries JSON events. The WebRTC guide describes this connection outline:

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  1. Start from a user action. Request microphone permission in response to an explicit action, and handle the case where the user denies permission.
  2. Set up the peer connection. Add the microphone audio tracks and create a data channel. Register listeners for the session and transcript events your app needs.
  3. Send an SDP offer through your trusted application server. The server creates the API session; keep the project API key there rather than exposing it in browser code.
  4. Apply the SDP answer. Complete the negotiation using the answer returned for the session.
  5. Wait for the session-ready event before sending application commands. Treat setup as a sequence, not as permission to issue session updates as soon as the connection object exists.

For a WebSocket pipeline, the application is responsible for the audio chunks and event handling rather than handing browser media to negotiated WebRTC tracks. Use the matching API guide for the exact messages and turn-handling behavior; do not transfer the WebRTC handshake to a WebSocket implementation.

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Keep credentials and tools on the right side of the connection

A browser can participate in a voice session without being the trusted home for project credentials. Keep long-lived project API keys on a trusted server. Before choosing a client-side or backend session pattern, decide where the session and any callable tools will run. In the Agents SDK pattern, tools execute wherever the Realtime session runs, so tool placement is also a security and application-architecture decision—not just a transport detail. The Agents SDK guide also advises making model and tracing decisions up front: the model cannot change mid-conversation, and tracing must be decided at session setup.

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Handle live transcription as incremental output

For live captions or voice input that ends in text, the Realtime transcription guide describes transcript deltas arriving as speech comes in, followed by a final transcript when the app commits the audio turn. Treat deltas as provisional incremental text and the final transcript as the completed turn; design the interface so users can see the difference.

In the guide’s WebSocket transcription configuration, the application sends audio chunks and commits the turn at its end. Its example uses client-side voice activity detection for turn-end detection. That is a configuration-specific example, not a universal requirement for every audio workflow; follow the selected endpoint’s current instructions for deciding when a turn ends.

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Balance early partials against transcript quality

The transcription documentation describes qualitative delay presets from minimal through xhigh. Less delay can expose partial text sooner, while allowing more context can improve the final transcript. The docs do not promise fixed timing for a preset: timing can vary by model configuration. Tune against representative speech and the experience your app requires rather than treating a preset as a millisecond guarantee.

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Set session choices before the conversation starts

Some settings have session-lifecycle consequences. The Realtime conversations guide says the selected voice cannot be modified once the session has emitted audio. Together with the SDK guidance that the model cannot change mid-conversation, this makes voice and model choices initialization decisions. Check the current session reference when implementing, because API behavior can evolve.

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Test the complete voice experience

Do not infer real-world transcript quality or turn responsiveness from a clean demo alone. OpenAI’s transcription guide advises: “Don’t choose a setting from synthetic audio alone. Test with representative microphones, telephony audio, accents, background noise, code-switching, domain vocabulary, and long sessions.”

  • Exercise the microphones and acoustic conditions your users are likely to have, including noisy environments and telephony audio if calls are in scope.
  • Include accents, code-switching, and specialist vocabulary that occur in the app’s actual domain.
  • Run long sessions as well as short clips so that testing reflects the intended interaction.
  • Evaluate both incremental transcript behavior and the final committed transcript.
  • Measure complete user-perceived turn time in the target app before making latency claims; the available guidance does not establish a universal latency or accuracy winner.

For a first implementation, settle the task first, then place the session and tools where they can safely run. Use browser WebRTC for a browser media path, WebSockets when the server-side pipeline needs to own chunk handling, or the documented SIP path for phone integration. Keep credentials trusted, make session initialization decisions deliberately, and validate the resulting experience on representative audio.

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