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Use deepgram
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README.md

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@@ -9,7 +9,7 @@ A complete starter project for building voice AI apps with [LiveKit Agents for P
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The starter project includes:
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- A simple voice AI assistant, ready for extension and customization
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- A voice AI pipeline with [models](https://docs.livekit.io/agents/models) from OpenAI, Cartesia, and AssemblyAI served through LiveKit Cloud
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- A voice AI pipeline with [models](https://docs.livekit.io/agents/models) from OpenAI, Cartesia, and Deepgram served through LiveKit Cloud
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- Easily integrate your preferred [LLM](https://docs.livekit.io/agents/models/llm/), [STT](https://docs.livekit.io/agents/models/stt/), and [TTS](https://docs.livekit.io/agents/models/tts/) instead, or swap to a realtime model like the [OpenAI Realtime API](https://docs.livekit.io/agents/models/realtime/openai)
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- Eval suite based on the LiveKit Agents [testing & evaluation framework](https://docs.livekit.io/agents/build/testing/)
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- [LiveKit Turn Detector](https://docs.livekit.io/agents/build/turns/turn-detector/) for contextually-aware speaker detection, with multilingual support

src/agent.py

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"room": ctx.room.name,
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}
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# Set up a voice AI pipeline using OpenAI, Cartesia, AssemblyAI, and the LiveKit turn detector
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# Set up a voice AI pipeline using OpenAI, Cartesia, Deepgram, and the LiveKit turn detector
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session = AgentSession(
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# Speech-to-text (STT) is your agent's ears, turning the user's speech into text that the LLM can understand
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# See all available models at https://docs.livekit.io/agents/models/stt/
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stt=inference.STT(model="assemblyai/universal-streaming", language="en"),
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stt=inference.STT(model="deepgram/flux-general", language="en"),
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# A Large Language Model (LLM) is your agent's brain, processing user input and generating a response
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# See all available models at https://docs.livekit.io/agents/models/llm/
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llm=inference.LLM(model="openai/gpt-4.1-mini"),

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