LiveKit and Murf

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import logging
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import os
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from dotenv import load_dotenv
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from livekit.agents import (
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    Agent,
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    AgentServer,
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    AgentSession,
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    JobContext,
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    JobProcess,
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    MetricsCollectedEvent,
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    cli,
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    metrics,
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    room_io,
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)
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from livekit.plugins import silero, deepgram, murf
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from livekit.plugins import openai as openai_plugin
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logger = logging.getLogger("murf-agent")
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load_dotenv()
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class MyAgent(Agent):
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    def __init__(self) -> None:
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        super().__init__(
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            instructions="You are a voice agent built using Murf TTS. Keep responses short and natural."
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        )
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    async def on_enter(self):
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        await self.session.say(
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            "Hi, I am a voice agent powered by Murf, how can I help you?"
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        )
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server = AgentServer()
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def prewarm(proc: JobProcess):
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    proc.userdata["vad"] = silero.VAD.load()
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server.setup_fnc = prewarm
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@server.rtc_session()
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async def entrypoint(ctx: JobContext):
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    ctx.log_context_fields = {"room": ctx.room.name}
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    session = AgentSession(
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        stt="deepgram/nova-3",
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        llm="openai/gpt-4o",
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        tts=murf.TTS(voice="Matthew", style="Conversation"),
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        vad=ctx.proc.userdata["vad"],
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        preemptive_generation=True,
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        resume_false_interruption=True,
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        false_interruption_timeout=1.0,
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    )
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    usage_collector = metrics.UsageCollector()
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    @session.on("metrics_collected")
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    def on_metrics(ev: MetricsCollectedEvent):
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        metrics.log_metrics(ev.metrics)
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        usage_collector.collect(ev.metrics)
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    async def log_usage():
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        logger.info(f"Usage: {usage_collector.get_summary()}")
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    ctx.add_shutdown_callback(log_usage)
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    await session.start(
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        agent=MyAgent(),
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        room=ctx.room,
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        room_options=room_io.RoomOptions(
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            audio_input=room_io.AudioInputOptions()
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        ),
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    )
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if __name__ == "__main__":
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    cli.run_app(server)

1
import logging
2
import os
3
from dotenv import load_dotenv
4
from livekit.agents import (
5
    Agent,
6
    AgentServer,
7
    AgentSession,
8
    JobContext,
9
    JobProcess,
10
    MetricsCollectedEvent,
11
    cli,
12
    metrics,
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    room_io,
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)
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from livekit.plugins import silero, deepgram, murf
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from livekit.plugins import openai as openai_plugin
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logger = logging.getLogger("murf-agent")
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load_dotenv()
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class CustomAgent(Agent):
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    def __init__(self) -> None:
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        super().__init__(
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            instructions="You are a helpful assistant with a natural speaking style. Provide detailed but concise responses."
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        )
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    async def on_enter(self):
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        await self.session.say(
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            "Hello! I'm powered by Murf's high-quality voice synthesis. How can I assist you today?"
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        )
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server = AgentServer()
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def prewarm(proc: JobProcess):
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    proc.userdata["vad"] = silero.VAD.load()
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server.setup_fnc = prewarm
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@server.rtc_session()
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async def entrypoint(ctx: JobContext):
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    ctx.log_context_fields = {"room": ctx.room.name}
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    session = AgentSession(
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        stt="deepgram/nova-3",
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        llm="openai/gpt-4o",
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        tts=murf.TTS(
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            voice="Matthew",
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            style="Conversation",
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            rate=5,
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            pitch=0,
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            model="FALCON",
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            sample_rate=24000,
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        ),
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        vad=ctx.proc.userdata["vad"],
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        preemptive_generation=True,
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        resume_false_interruption=True,
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        false_interruption_timeout=1.0,
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    )
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    usage_collector = metrics.UsageCollector()
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    @session.on("metrics_collected")
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    def on_metrics(ev: MetricsCollectedEvent):
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        metrics.log_metrics(ev.metrics)
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        usage_collector.collect(ev.metrics)
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    async def log_usage():
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        summary = usage_collector.get_summary()
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        logger.info(f"Session usage: {summary}")
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    ctx.add_shutdown_callback(log_usage)
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    await session.start(
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        agent=CustomAgent(),
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        room=ctx.room,
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        room_options=room_io.RoomOptions(
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            audio_input=room_io.AudioInputOptions()
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        ),
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    )
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if __name__ == "__main__":
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    cli.run_app(server)

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from livekit.plugins import murf
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tts = murf.TTS(
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    voice="Matthew",
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    style="Conversation",
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    rate=10,
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    pitch=-5,
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    model="FALCON",
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    sample_rate=24000,
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    format="PCM",
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    language="en-US",
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)

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from dotenv import load_dotenv
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load_dotenv()