replace foundational example 08-bots-arguing.py with 08-custom-frame-processor.py
This commit is contained in:
vipyne
@@ -1,147 +0,0 @@
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import asyncio
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import logging
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import os
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from typing import Tuple
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import aiohttp
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from dotenv import load_dotenv
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from pipecat.frames.frames import AudioFrame, EndFrame, ImageFrame, LLMContextFrame, TextFrame
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.processors.aggregators import SentenceAggregator
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from pipecat.processors.aggregators.llm_context import LLMContext
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from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
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from pipecat.runner.daily import configure
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from pipecat.services.azure import AzureLLMService, AzureTTSService
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from pipecat.services.elevenlabs import ElevenLabsTTSService
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from pipecat.services.fal import FalImageGenService
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from pipecat.transports.daily.transport import DailyTransport
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load_dotenv(override=True)
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logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
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logger = logging.getLogger("pipecat")
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logger.setLevel(logging.DEBUG)
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async def main():
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async with aiohttp.ClientSession() as session:
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(room_url, _) = await configure(session)
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transport = DailyTransport(
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room_url,
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None,
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"Respond bot",
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duration_minutes=10,
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mic_enabled=True,
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mic_sample_rate=16000,
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camera_enabled=True,
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camera_width=1024,
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camera_height=1024,
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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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)
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tts1 = AzureTTSService(
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api_key=os.getenv("AZURE_SPEECH_API_KEY"),
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region=os.getenv("AZURE_SPEECH_REGION"),
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)
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tts2 = ElevenLabsTTSService(
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api_key=os.getenv("ELEVENLABS_API_KEY"),
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voice_id="jBpfuIE2acCO8z3wKNLl",
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)
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dalle = FalImageGenService(
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params=FalImageGenService.InputParams(image_size="1024x1024"),
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aiohttp_session=session,
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key=os.getenv("FAL_KEY"),
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)
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bot1_messages = [
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{
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"role": "system",
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"content": "You are a stern librarian. You strongly believe that a hot dog is a sandwich. Start by stating this fact in a few sentences, then be prepared to debate this with the user. You shouldn't ever compromise on the fundamental truth that a hot dog is a sandwich. Your responses should only be a few sentences long.",
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},
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]
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bot2_messages = [
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{
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"role": "system",
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"content": "You are a silly cat, and you strongly believe that a hot dog is not a sandwich. Debate this with the user, only responding with a few sentences. Don't ever accept that a hot dog is a sandwich.",
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},
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]
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async def get_text_and_audio(messages) -> Tuple[str, bytearray]:
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"""This function streams text from the LLM and uses the TTS service to convert
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that text to speech as it's received.
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"""
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source_queue = asyncio.Queue()
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sink_queue = asyncio.Queue()
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sentence_aggregator = SentenceAggregator()
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pipeline = Pipeline([llm, sentence_aggregator, tts1], source_queue, sink_queue)
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await source_queue.put(LLMContextFrame(LLMContext(messages)))
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await source_queue.put(EndFrame())
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await pipeline.run_pipeline()
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message = ""
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all_audio = bytearray()
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while sink_queue.qsize():
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frame = sink_queue.get_nowait()
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if isinstance(frame, TextFrame):
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message += frame.text
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elif isinstance(frame, AudioFrame):
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all_audio.extend(frame.audio)
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return (message, all_audio)
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async def get_bot1_statement():
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message, audio = await get_text_and_audio(bot1_messages)
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bot1_messages.append({"role": "assistant", "content": message})
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bot2_messages.append({"role": "user", "content": message})
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return audio
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async def get_bot2_statement():
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message, audio = await get_text_and_audio(bot2_messages)
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bot2_messages.append({"role": "assistant", "content": message})
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bot1_messages.append({"role": "user", "content": message})
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return audio
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async def argue():
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for i in range(100):
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print(f"In iteration {i}")
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bot1_description = "A woman conservatively dressed as a librarian in a library surrounded by books, cartoon, serious, highly detailed"
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(audio1, image_data1) = await asyncio.gather(
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get_bot1_statement(), dalle.run_image_gen(bot1_description)
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)
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await transport.send_queue.put(
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[
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ImageFrame(image_data1[1], image_data1[2]),
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AudioFrame(audio1),
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]
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)
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bot2_description = "A cat dressed in a hot dog costume, cartoon, bright colors, funny, highly detailed"
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(audio2, image_data2) = await asyncio.gather(
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get_bot2_statement(), dalle.run_image_gen(bot2_description)
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)
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await transport.send_queue.put(
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[
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ImageFrame(image_data2[1], image_data2[2]),
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AudioFrame(audio2),
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]
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)
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await asyncio.gather(transport.run(), argue())
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if __name__ == "__main__":
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asyncio.run(main())
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@@ -57,13 +57,10 @@ def format_metrics(metrics, indent=0):
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class MetricsFrameLogger(FrameProcessor):
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"""MetricsFrameLogger logs all MetericsFrames.
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"""MetricsFrameLogger formats and logs all MetericsFrames"""
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AND it Always pushes all frames.
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"""
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def __init__(self):
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super().__init__()
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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await super().process_frame(frame, direction)
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@@ -110,8 +107,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
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metrics_frame_processor = MetricsFrameLogger()
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messages = [
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{
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"role": "system",
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@@ -122,6 +117,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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context = LLMContext(messages)
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context_aggregator = LLMContextAggregatorPair(context)
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metrics_frame_processor = MetricsFrameLogger()
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pipeline = Pipeline(
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[
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transport.input(),
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