Migrate from Deepgram

Deepgram and Bland both return raw binary audio over a persistent WebSocket. Most of the migration is a control-message rename plus one important change: Bland handles text buffering without requiring Flush after each useful fragment. Start with the Realtime TTS Quickstart.

Message mapping

Deepgram streaming TTS	Bland `/v2/tts/ws`
Voice model and audio settings in URL query parameters	Voice UUID and audio settings in `init`
`{ "type": "Speak", "text": ... }`	`{ "type": "speak", "context_id": ..., "text": ... }`
`{ "type": "Flush" }`	Usually unnecessary; use `end_of_turn` only when the LLM finishes
`{ "type": "Clear" }`	`cancel`, or start a replacement context to preempt
`{ "type": "Close" }`	`close` after the final `utterance_end`
`Flushed`	No direct event; use `utterance_end` for the turn terminal
Binary audio	Binary audio

for await (const token of llmTextStream) {
  ws.send(
    JSON.stringify({
      type: "speak",
      context_id: turnId,
      text: token,
    }),
  );
}

ws.send(JSON.stringify({ type: "end_of_turn", context_id: turnId }));

Do not translate every Deepgram Flush into a new Bland turn. Keep one Bland context for the entire LLM response. Bland begins synthesis at natural boundaries while more tokens continue to arrive. Deepgram’s Clear acknowledges cleared server state. Bland reports utterance_end with reason cancelled or preempted. In both cases, clear any unplayed audio in your local playback queue. See the Deepgram streaming TTS reference and Bland realtime concepts.

Docs for agents: llms.txt

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Realtime Speech

HTTP Speech

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Voice Cloning

Voices

Discovery

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Generation History

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Tooling

Migrate from Deepgram

Message mapping

​Message mapping

Message mapping