161 lines
5.1 KiB
Python
161 lines
5.1 KiB
Python
import asyncio
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import aiohttp
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import os
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import sys
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from PIL import Image
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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 PipelineTask
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from pipecat.processors.aggregators.llm_response import LLMUserResponseAggregator
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from pipecat.frames.frames import (
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AudioRawFrame,
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ImageRawFrame,
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SpriteFrame,
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Frame,
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LLMMessagesFrame,
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TTSStoppedFrame
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)
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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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, DailyTranscriptionSettings, DailyTransport
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from pipecat.vad.silero import SileroVAD
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from runner import configure
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from loguru import logger
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from dotenv import load_dotenv
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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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sprites = []
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script_dir = os.path.dirname(__file__)
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for i in range(1, 26):
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# Build the full path to the image file
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full_path = os.path.join(script_dir, f"assets/robot0{i}.png")
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# Get the filename without the extension to use as the dictionary key
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# Open the image and convert it to bytes
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with Image.open(full_path) as img:
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sprites.append(ImageRawFrame(image=img.tobytes(), size=img.size, format=img.format))
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flipped = sprites[::-1]
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sprites.extend(flipped)
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# When the bot isn't talking, show a static image of the cat listening
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quiet_frame = sprites[0]
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talking_frame = SpriteFrame(images=sprites)
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class TalkingAnimation(FrameProcessor):
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"""
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This class starts a talking animation when it receives an first AudioFrame,
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and then returns to a "quiet" sprite when it sees a LLMResponseEndFrame.
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"""
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def __init__(self):
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super().__init__()
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self._is_talking = False
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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if isinstance(frame, AudioRawFrame):
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if not self._is_talking:
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await self.push_frame(talking_frame)
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self._is_talking = True
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elif isinstance(frame, TTSStoppedFrame):
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await self.push_frame(quiet_frame)
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self._is_talking = False
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await self.push_frame(frame)
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async def main(room_url: str, token):
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async with aiohttp.ClientSession() as 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_in_enabled=True,
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audio_out_enabled=True,
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camera_out_enabled=True,
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camera_out_width=1024,
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camera_out_height=576,
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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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vad = SileroVAD()
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tts = ElevenLabsTTSService(
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aiohttp_session=session,
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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="pNInz6obpgDQGcFmaJgB",
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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(
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api_key=os.getenv("OPENAI_API_KEY"),
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model="gpt-4-turbo-preview")
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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 never use 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.",
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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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user_response = LLMUserResponseAggregator()
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ta = TalkingAnimation()
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pipeline = Pipeline([transport.input(), vad, user_response,
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llm, tts, ta, transport.output()])
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task = PipelineTask(pipeline)
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await task.queue_frame(quiet_frame)
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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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transport.capture_participant_transcription(participant["id"])
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await task.queue_frames([LLMMessagesFrame(messages)])
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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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(url, token) = configure()
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asyncio.run(main(url, token))
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