132 lines
4.7 KiB
Python
132 lines
4.7 KiB
Python
import aiohttp
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import asyncio
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import logging
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import os
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import wave
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from dailyai.services.daily_transport_service import DailyTransportService
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from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
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from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
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from dailyai.queue_aggregators import LLMContextAggregator, LLMUserContextAggregator, LLMAssistantContextAggregator
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from dailyai.services.ai_services import AIService, FrameLogger
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from dailyai.queue_frame import QueueFrame, AudioQueueFrame, LLMResponseEndQueueFrame, LLMMessagesQueueFrame
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from typing import AsyncGenerator
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from examples.foundational.support.runner import configure
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logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s") # or whatever
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logger = logging.getLogger("dailyai")
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logger.setLevel(logging.DEBUG)
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sounds = {}
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sound_files = [
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'ding1.wav',
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'ding2.wav'
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]
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script_dir = os.path.dirname(__file__)
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for file in sound_files:
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# Build the full path to the image file
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full_path = os.path.join(script_dir, "assets", file)
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# Get the filename without the extension to use as the dictionary key
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filename = os.path.splitext(os.path.basename(full_path))[0]
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# Open the image and convert it to bytes
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with wave.open(full_path) as audio_file:
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sounds[file] = audio_file.readframes(-1)
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class OutboundSoundEffectWrapper(AIService):
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def __init__(self):
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pass
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async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
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if isinstance(frame, LLMResponseEndQueueFrame):
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yield AudioQueueFrame(sounds["ding1.wav"])
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# In case anything else up the stack needs it
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yield frame
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else:
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yield frame
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class InboundSoundEffectWrapper(AIService):
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def __init__(self):
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pass
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async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
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if isinstance(frame, LLMMessagesQueueFrame):
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yield AudioQueueFrame(sounds["ding2.wav"])
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# In case anything else up the stack needs it
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yield frame
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else:
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yield 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 = DailyTransportService(
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room_url,
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token,
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"Respond bot",
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duration_minutes=5,
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mic_enabled=True,
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mic_sample_rate=16000,
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camera_enabled=False
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)
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llm = AzureLLMService(
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api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
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endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
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model=os.getenv("AZURE_CHATGPT_MODEL"))
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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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voice_id="ErXwobaYiN019PkySvjV")
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@transport.event_handler("on_first_other_participant_joined")
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async def on_first_other_participant_joined(transport):
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await tts.say("Hi, I'm listening!", transport.send_queue)
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await transport.send_queue.put(AudioQueueFrame(sounds["ding1.wav"]))
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async def handle_transcriptions():
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messages = [
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{"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. Respond to what the user said in a creative and helpful way."},
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]
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tma_in = LLMUserContextAggregator(
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messages, transport._my_participant_id
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)
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tma_out = LLMAssistantContextAggregator(
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messages, transport._my_participant_id
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)
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out_sound = OutboundSoundEffectWrapper()
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in_sound = InboundSoundEffectWrapper()
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fl = FrameLogger("LLM Out")
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fl2 = FrameLogger("Transcription In")
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await out_sound.run_to_queue(
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transport.send_queue,
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tts.run(
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fl.run(
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tma_out.run(
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llm.run(
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fl2.run(
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in_sound.run(
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tma_in.run(
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transport.get_receive_frames()
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)
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)
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)
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)
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)
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)
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)
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)
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transport.transcription_settings["extra"]["punctuate"] = True
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await asyncio.gather(transport.run(), handle_transcriptions())
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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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