ruff formatting
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@@ -19,9 +19,10 @@ from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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from pipecat.processors.aggregators.llm_response import (
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LLMAssistantResponseAggregator, LLMUserResponseAggregator)
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from pipecat.processors.audio.audio_buffer_processor import \
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AudioBufferProcessor
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LLMAssistantResponseAggregator,
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LLMUserResponseAggregator,
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)
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from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor
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from pipecat.services.canonical import CanonicalMetricsService
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from pipecat.services.elevenlabs import ElevenLabsTTSService
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from pipecat.services.openai import OpenAILLMService
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@@ -58,18 +59,16 @@ async def main():
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# tier="nova",
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# model="2-general"
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# )
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)
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),
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)
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tts = ElevenLabsTTSService(
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api_key=os.getenv("ELEVENLABS_API_KEY"),
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#
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# English
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#
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voice_id="cgSgspJ2msm6clMCkdW9",
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aiohttp_session=session,
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#
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# Spanish
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#
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@@ -77,9 +76,7 @@ async def main():
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# voice_id="gD1IexrzCvsXPHUuT0s3",
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)
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llm = OpenAILLMService(
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api_key=os.getenv("OPENAI_API_KEY"),
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model="gpt-4o")
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
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messages = [
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{
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@@ -88,7 +85,6 @@ async def main():
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# English
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#
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"content": "You are Chatbot, a friendly, helpful robot. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way, but keep your responses brief. Start by introducing yourself. Keep all your responses to 12 words or fewer.",
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#
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# Spanish
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#
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@@ -114,16 +110,18 @@ async def main():
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assistant="pipecat-chatbot",
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assistant_speaks_first=True,
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)
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pipeline = Pipeline([
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transport.input(), # microphone
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user_response,
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llm,
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tts,
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transport.output(),
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audio_buffer_processor, # captures audio into a buffer
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canonical, # uploads audio buffer to Canonical AI for metrics
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assistant_response,
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])
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pipeline = Pipeline(
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[
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transport.input(), # microphone
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user_response,
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llm,
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tts,
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transport.output(),
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audio_buffer_processor, # captures audio into a buffer
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canonical, # uploads audio buffer to Canonical AI for metrics
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assistant_response,
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]
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)
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task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
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