Use deepgram

README.md

@@ -9,7 +9,7 @@ A complete starter project for building voice AI apps with [LiveKit Agents for P
 The starter project includes:
 
 - A simple voice AI assistant, ready for extension and customization
-- A voice AI pipeline with [models](https://docs.livekit.io/agents/models) from OpenAI, Cartesia, and AssemblyAI served through LiveKit Cloud
+- A voice AI pipeline with [models](https://docs.livekit.io/agents/models) from OpenAI, Cartesia, and Deepgram served through LiveKit Cloud
   - 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)
 - Eval suite based on the LiveKit Agents [testing & evaluation framework](https://docs.livekit.io/agents/build/testing/)
 - [LiveKit Turn Detector](https://docs.livekit.io/agents/build/turns/turn-detector/) for contextually-aware speaker detection, with multilingual support

src/agent.py

@@ -65,11 +65,11 @@ async def my_agent(ctx: JobContext):
         "room": ctx.room.name,
     }
 
-    # Set up a voice AI pipeline using OpenAI, Cartesia, AssemblyAI, and the LiveKit turn detector
+    # Set up a voice AI pipeline using OpenAI, Cartesia, Deepgram, and the LiveKit turn detector
     session = AgentSession(
         # Speech-to-text (STT) is your agent's ears, turning the user's speech into text that the LLM can understand
         # See all available models at https://docs.livekit.io/agents/models/stt/
-        stt=inference.STT(model="assemblyai/universal-streaming", language="en"),
+        stt=inference.STT(model="deepgram/flux-general", language="en"),
         # A Large Language Model (LLM) is your agent's brain, processing user input and generating a response
         # See all available models at https://docs.livekit.io/agents/models/llm/
         llm=inference.LLM(model="openai/gpt-4.1-mini"),