Fixes

Author: bcherry

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 Deepgram served through LiveKit Cloud
+- A voice AI pipeline with [models](https://docs.livekit.io/agents/models) from OpenAI, Cartesia, and AssemblyAI 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

pyproject.toml

@@ -9,7 +9,7 @@ description = "Simple voice AI assistant built with LiveKit Agents for Python"
 requires-python = ">=3.9"
 
 dependencies = [
-    "livekit-agents[turn-detector,silero]~=1.2",
+    "livekit-agents[silero,turn-detector]~=1.2",
     "livekit-plugins-noise-cancellation~=0.2",
     "python-dotenv",
 ]

src/agent.py

@@ -2,17 +2,14 @@
 
 from dotenv import load_dotenv
 from livekit.agents import (
-    NOT_GIVEN,
     Agent,
-    AgentFalseInterruptionEvent,
     AgentSession,
     JobContext,
     JobProcess,
     MetricsCollectedEvent,
     RoomInputOptions,
     WorkerOptions,
     cli,
-    inference,
     metrics,
 )
 from livekit.plugins import noise_cancellation, silero
@@ -61,14 +58,14 @@ async def entrypoint(ctx: JobContext):
         "room": ctx.room.name,
     }
 
-    # Set up a voice AI pipeline using OpenAI, Cartesia, Deepgram, and the LiveKit turn detector
+    # Set up a voice AI pipeline using OpenAI, Cartesia, AssemblyAI, 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="assemblyai/universal-streaming",
         # 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="azure/gpt-4o-mini",
-        # 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(language="multi"),
         # 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-2:f786b574-daa5-4673-aa0c-cbe3e8534c02",
@@ -91,13 +88,6 @@ async def entrypoint(ctx: JobContext):
     #     llm=openai.realtime.RealtimeModel(voice="marin")
     # )
 
-    # sometimes background noise could interrupt the agent session, these are considered false positive interruptions
-    # when it's detected, you may resume the agent's speech
-    @session.on("agent_false_interruption")
-    def _on_agent_false_interruption(ev: AgentFalseInterruptionEvent):
-        logger.info("false positive interruption, resuming")
-        session.generate_reply(instructions=ev.extra_instructions or NOT_GIVEN)
-
     # Metrics collection, to measure pipeline performance
     # For more information, see https://docs.livekit.io/agents/build/metrics/
     usage_collector = metrics.UsageCollector()