CypherMonkeyAI omni-channel lab

A lifelong-learner lab on AI omni-channel agents, including voice and the contact center. Free to read. Nothing sold.

Voice AI agent stack · L1 · 20 min

Speech in, a model, speech out

Learn in five minutes

  • LiveKit's pipeline page names a cascaded pipeline: speech-to-text, then a language model, then text-to-speech.
  • The same page also names turn-based and full-duplex realtime models, which consume and produce speech in one model.
  • Pipecat's overview lists a transport, speech recognition, a language model, speech synthesis, and a transport again.
  • The Voice overview on docs.x.ai separates Speech to Speech, Text to Speech, and Speech to Text as different APIs.
  • Draw the hearing box apart from the language-model box. They are not the same stage.

LiveKit says you can swap a stage in a cascaded pipeline. A realtime model does not give you that split. Say which shape you mean before you compare them.

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Visual

T9-R1 diagram

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Name the boxes

  1. Write STT, model, and TTS on three cards.
  2. Write 'one speech-to-speech model' on a fourth card and set it beside the row, not inside it.
  3. Point at the card your sentence is about.
  4. If you cannot point, you are mixing the two shapes.

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Quiz

What order does LiveKit give for a cascaded pipeline?

That is the order on the pipeline page.

What does a realtime model skip, on that page?

The page says speech goes in and speech comes out of one model.

Are speech-to-speech, text-to-speech, and speech-to-text the same API on that Voice overview?

The overview gives each its own section.

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Ecosystem

Cascaded pipeline
Speech-to-text, a language model, and text-to-speech, in that order, as LiveKit's pipeline page describes it.

Sisters: Speech-to-text, Language model, Text-to-speech, WebRTC, SIP.

A cascaded pipeline and a speech-to-speech model are two shapes the LiveKit docs name. The xAI Speech to Speech API is a hosted speech-to-speech session.

Pin the model id, the voice, and the date you read the docs.

Config

  • Write the goal in one sentence.
  • Name the input and the allowed output.
  • Save the trace next to the result.

Troubleshooting

  • The answer looks fluent and is wrong. Check whether a source passage actually contains the claim.
  • The loop never stops. Set a step cap and stop when the tool result repeats.

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Sources and go deeper

sources.json