143 lines
4.6 KiB
Python
143 lines
4.6 KiB
Python
#
|
|
# Copyright (c) 2024, Daily
|
|
#
|
|
# SPDX-License-Identifier: BSD 2-Clause License
|
|
#
|
|
|
|
import asyncio
|
|
import aiohttp
|
|
import os
|
|
import sys
|
|
|
|
from PIL import Image
|
|
|
|
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
|
from pipecat.frames.frames import Frame, OutputImageRawFrame, SystemFrame, TextFrame
|
|
from pipecat.pipeline.pipeline import Pipeline
|
|
from pipecat.pipeline.runner import PipelineRunner
|
|
from pipecat.pipeline.task import PipelineTask
|
|
from pipecat.processors.aggregators.llm_response import (
|
|
LLMAssistantResponseAggregator,
|
|
LLMUserResponseAggregator,
|
|
)
|
|
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
|
from pipecat.services.cartesia import CartesiaHttpTTSService
|
|
from pipecat.services.openai import OpenAILLMService
|
|
from pipecat.transports.services.daily import DailyTransport
|
|
|
|
from pipecat.transports.services.daily import DailyParams
|
|
from runner import configure
|
|
|
|
from loguru import logger
|
|
|
|
from dotenv import load_dotenv
|
|
|
|
load_dotenv(override=True)
|
|
|
|
logger.remove(0)
|
|
logger.add(sys.stderr, level="DEBUG")
|
|
|
|
|
|
class ImageSyncAggregator(FrameProcessor):
|
|
def __init__(self, speaking_path: str, waiting_path: str):
|
|
super().__init__()
|
|
self._speaking_image = Image.open(speaking_path)
|
|
self._speaking_image_format = self._speaking_image.format
|
|
self._speaking_image_bytes = self._speaking_image.tobytes()
|
|
|
|
self._waiting_image = Image.open(waiting_path)
|
|
self._waiting_image_format = self._waiting_image.format
|
|
self._waiting_image_bytes = self._waiting_image.tobytes()
|
|
|
|
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
|
await super().process_frame(frame, direction)
|
|
|
|
if not isinstance(frame, SystemFrame) and direction == FrameDirection.DOWNSTREAM:
|
|
await self.push_frame(
|
|
OutputImageRawFrame(
|
|
image=self._speaking_image_bytes,
|
|
size=(1024, 1024),
|
|
format=self._speaking_image_format,
|
|
)
|
|
)
|
|
await self.push_frame(frame)
|
|
await self.push_frame(
|
|
OutputImageRawFrame(
|
|
image=self._waiting_image_bytes,
|
|
size=(1024, 1024),
|
|
format=self._waiting_image_format,
|
|
)
|
|
)
|
|
else:
|
|
await self.push_frame(frame)
|
|
|
|
|
|
async def main():
|
|
async with aiohttp.ClientSession() as session:
|
|
(room_url, token) = await configure(session)
|
|
|
|
transport = DailyTransport(
|
|
room_url,
|
|
token,
|
|
"Respond bot",
|
|
DailyParams(
|
|
audio_out_enabled=True,
|
|
camera_out_enabled=True,
|
|
camera_out_width=1024,
|
|
camera_out_height=1024,
|
|
transcription_enabled=True,
|
|
vad_enabled=True,
|
|
vad_analyzer=SileroVADAnalyzer(),
|
|
),
|
|
)
|
|
|
|
tts = CartesiaHttpTTSService(
|
|
api_key=os.getenv("CARTESIA_API_KEY"),
|
|
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
|
)
|
|
|
|
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
|
|
|
|
messages = [
|
|
{
|
|
"role": "system",
|
|
"content": "You are a helpful LLM in a WebRTC call. 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.",
|
|
},
|
|
]
|
|
|
|
tma_in = LLMUserResponseAggregator(messages)
|
|
tma_out = LLMAssistantResponseAggregator(messages)
|
|
|
|
image_sync_aggregator = ImageSyncAggregator(
|
|
os.path.join(os.path.dirname(__file__), "assets", "speaking.png"),
|
|
os.path.join(os.path.dirname(__file__), "assets", "waiting.png"),
|
|
)
|
|
|
|
pipeline = Pipeline(
|
|
[
|
|
transport.input(),
|
|
image_sync_aggregator,
|
|
tma_in,
|
|
llm,
|
|
tts,
|
|
transport.output(),
|
|
tma_out,
|
|
]
|
|
)
|
|
|
|
task = PipelineTask(pipeline)
|
|
|
|
@transport.event_handler("on_first_participant_joined")
|
|
async def on_first_participant_joined(transport, participant):
|
|
participant_name = participant["info"]["userName"] or ""
|
|
transport.capture_participant_transcription(participant["id"])
|
|
await task.queue_frames([TextFrame(f"Hi there {participant_name}!")])
|
|
|
|
runner = PipelineRunner()
|
|
|
|
await runner.run(task)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
asyncio.run(main())
|