157 lines
5.1 KiB
Python
157 lines
5.1 KiB
Python
#
|
||
# Copyright (c) 2024–2025, Daily
|
||
#
|
||
# SPDX-License-Identifier: BSD 2-Clause License
|
||
#
|
||
|
||
import os
|
||
|
||
from dotenv import load_dotenv
|
||
from loguru import logger
|
||
from PIL import Image
|
||
|
||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||
from pipecat.frames.frames import (
|
||
BotStartedSpeakingFrame,
|
||
BotStoppedSpeakingFrame,
|
||
Frame,
|
||
OutputImageRawFrame,
|
||
)
|
||
from pipecat.pipeline.pipeline import Pipeline
|
||
from pipecat.pipeline.runner import PipelineRunner
|
||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
|
||
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||
from pipecat.services.openai.llm import OpenAILLMService
|
||
from pipecat.transports.base_transport import TransportParams
|
||
from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
|
||
from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
|
||
|
||
load_dotenv(override=True)
|
||
|
||
|
||
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 isinstance(frame, BotStartedSpeakingFrame):
|
||
await self.push_frame(
|
||
OutputImageRawFrame(
|
||
image=self._speaking_image_bytes,
|
||
size=(1024, 1024),
|
||
format=self._speaking_image_format,
|
||
)
|
||
)
|
||
|
||
elif isinstance(frame, BotStoppedSpeakingFrame):
|
||
await self.push_frame(
|
||
OutputImageRawFrame(
|
||
image=self._waiting_image_bytes,
|
||
size=(1024, 1024),
|
||
format=self._waiting_image_format,
|
||
)
|
||
)
|
||
|
||
await self.push_frame(frame)
|
||
|
||
|
||
async def run_bot(webrtc_connection: SmallWebRTCConnection):
|
||
logger.info(f"Starting bot")
|
||
|
||
transport = SmallWebRTCTransport(
|
||
webrtc_connection=webrtc_connection,
|
||
params=TransportParams(
|
||
audio_in_enabled=True,
|
||
audio_out_enabled=True,
|
||
camera_out_enabled=True,
|
||
camera_out_width=1024,
|
||
camera_out_height=1024,
|
||
vad_enabled=True,
|
||
vad_analyzer=SileroVADAnalyzer(),
|
||
vad_audio_passthrough=True,
|
||
),
|
||
)
|
||
|
||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||
|
||
tts = CartesiaTTSService(
|
||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||
)
|
||
|
||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||
|
||
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.",
|
||
},
|
||
]
|
||
|
||
context = OpenAILLMContext(messages)
|
||
context_aggregator = llm.create_context_aggregator(context)
|
||
|
||
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(),
|
||
stt,
|
||
context_aggregator.user(),
|
||
llm,
|
||
tts,
|
||
image_sync_aggregator,
|
||
transport.output(),
|
||
context_aggregator.assistant(),
|
||
]
|
||
)
|
||
|
||
task = PipelineTask(
|
||
pipeline,
|
||
params=PipelineParams(
|
||
allow_interruptions=True,
|
||
enable_metrics=True,
|
||
enable_usage_metrics=True,
|
||
report_only_initial_ttfb=True,
|
||
),
|
||
)
|
||
|
||
@transport.event_handler("on_client_connected")
|
||
async def on_client_connected(transport, client):
|
||
logger.info(f"Client connected")
|
||
# Kick off the conversation.
|
||
await task.queue_frames([context_aggregator.user().get_context_frame()])
|
||
|
||
@transport.event_handler("on_client_disconnected")
|
||
async def on_client_disconnected(transport, client):
|
||
logger.info(f"Client disconnected")
|
||
|
||
@transport.event_handler("on_client_closed")
|
||
async def on_client_closed(transport, client):
|
||
logger.info(f"Client closed connection")
|
||
await task.cancel()
|
||
|
||
runner = PipelineRunner(handle_sigint=False)
|
||
await runner.run(task)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
from run import main
|
||
|
||
main()
|