Migrating from the OpenAI Realtime API to AssemblyAI

A migration from OpenAI’s Realtime API to an AssemblyAI cascaded voice-agent stack separates a single speech-to-speech session into streaming speech-to-text, an LLM chat-completions layer, and streaming text-to-speech, with each former session setting mapped to a corresponding service configuration. The guide provides Python WebSocket examples for transmitting audio, detecting completed user turns, retaining existing function tools through a format conversion, generating LLM responses, and synthesizing them into speech. It emphasizes that AssemblyAI’s speech-to-text model manages turn detection, while application-managed message history enables interruption handling: text-to-speech cancellation stops playback, and word-boundary timings can be used to retain only the words a caller actually heard after barge-in. It also describes maintaining transcription context with the agent’s prior reply, keeping text-to-speech connections alive, and optionally using AssemblyAI’s OpenAI-compatible TTS endpoint for existing speech pipelines. Cost comparisons frame the cascade as separately billed speech recognition, text-only LLM usage, and character-based speech synthesis, while the tradeoffs include more connections and orchestration code, reduced access to vocal tone for the LLM, limited TTS language support, and model-dependent streaming behavior. The recommended rollout is incremental, beginning with configuration and tools, testing real audio and important entities, shadowing production traffic, and cutting over gradually after measuring accuracy and latency.

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