fix

src/agent.py

@@ -10,6 +10,7 @@ from livekit.agents import (
     RoomInputOptions,
     WorkerOptions,
     cli,
+    inference,
     metrics,
 )
 from livekit.plugins import noise_cancellation, silero
@@ -28,7 +29,7 @@ class Assistant(Agent):
             Your responses are concise, to the point, and without any complex formatting or punctuation including emojis, asterisks, or other symbols.
             You are curious, friendly, and have a sense of humor.""",
         )
-
+        
     # To add tools, use the @function_tool decorator.
     # Here's an example that adds a simple weather tool.
     # You also have to add `from livekit.agents import function_tool, RunContext` to the top of this file
@@ -62,13 +63,15 @@ async def entrypoint(ctx: JobContext):
     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="assemblyai/universal-streaming:en",
+        stt=inference.STT(model="assemblyai/universal-streaming", 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="openai/gpt-4.1-mini",
+        llm=inference.LLM(model="openai/gpt-4.1-mini"),
         # Text-to-speech (TTS) is your agent's voice, turning the LLM's text into speech that the user can hear
         # See all available models as well as voice selections at https://docs.livekit.io/agents/models/tts/
-        tts="cartesia/sonic-3:9626c31c-bec5-4cca-baa8-f8ba9e84c8bc",
+        tts=inference.TTS(
+            model="cartesia/sonic-3", voice="9626c31c-bec5-4cca-baa8-f8ba9e84c8bc"
+        ),
         # VAD and turn detection are used to determine when the user is speaking and when the agent should respond
         # See more at https://docs.livekit.io/agents/build/turns
         turn_detection=MultilingualModel(),