149 lines
5.4 KiB
Python
149 lines
5.4 KiB
Python
#
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# Copyright (c) 2024–2025, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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import asyncio
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import os
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import sys
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import uuid
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import aiohttp
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from dotenv import load_dotenv
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from loguru import logger
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from runner import configure
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import EndFrame
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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.openai_llm_context import OpenAILLMContext
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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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from pipecat.transports.services.daily import DailyParams, DailyTransport
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load_dotenv(override=True)
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logger.remove(0)
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logger.add(sys.stderr, level="DEBUG")
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async def main():
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async with aiohttp.ClientSession() as session:
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(room_url, token) = await configure(session)
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transport = DailyTransport(
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room_url,
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token,
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"Chatbot",
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DailyParams(
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audio_out_enabled=True,
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audio_in_enabled=True,
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camera_out_enabled=False,
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vad_enabled=True,
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vad_audio_passthrough=True,
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vad_analyzer=SileroVADAnalyzer(),
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transcription_enabled=True,
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#
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# Spanish
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#
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# transcription_settings=DailyTranscriptionSettings(
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# language="es",
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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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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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# model="eleven_multilingual_v2",
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# voice_id="gD1IexrzCvsXPHUuT0s3",
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)
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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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"role": "system",
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#
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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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# "content": "Eres Chatbot, un amigable y útil robot. Tu objetivo es demostrar tus capacidades de una manera breve. Tus respuestas se convertiran a audio así que nunca no debes incluir caracteres especiales. Contesta a lo que el usuario pregunte de una manera creativa, útil y breve. Empieza por presentarte a ti mismo.",
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},
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]
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context = OpenAILLMContext(messages)
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context_aggregator = llm.create_context_aggregator(context)
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"""
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CanonicalMetrics uses AudioBufferProcessor under the hood to buffer the audio. On
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call completion, CanonicalMetrics will send the audio buffer to Canonical for
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analysis. Visit https://voice.canonical.chat to learn more.
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"""
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audio_buffer_processor = AudioBufferProcessor(num_channels=2)
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canonical = CanonicalMetricsService(
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audio_buffer_processor=audio_buffer_processor,
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aiohttp_session=session,
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api_key=os.getenv("CANONICAL_API_KEY"),
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call_id=str(uuid.uuid4()),
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assistant="pipecat-chatbot",
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assistant_speaks_first=True,
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context=context,
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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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context_aggregator.user(),
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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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context_aggregator.assistant(),
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]
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)
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task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
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@transport.event_handler("on_first_participant_joined")
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async def on_first_participant_joined(transport, participant):
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await transport.capture_participant_transcription(participant["id"])
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await task.queue_frames([context_aggregator.user().get_context_frame()])
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@transport.event_handler("on_participant_left")
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async def on_participant_left(transport, participant, reason):
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print(f"Participant left: {participant}")
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await task.cancel()
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@transport.event_handler("on_call_state_updated")
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async def on_call_state_updated(transport, state):
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if state == "left":
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# Here we don't want to cancel, we just want to finish sending
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# whatever is queued, so we use an EndFrame().
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await task.queue_frame(EndFrame())
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runner = PipelineRunner()
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await runner.run(task)
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if __name__ == "__main__":
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asyncio.run(main())
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