diff --git a/examples/foundational/08-bots-arguing.py b/examples/foundational/08-bots-arguing.py deleted file mode 100644 index b84e945c3..000000000 --- a/examples/foundational/08-bots-arguing.py +++ /dev/null @@ -1,147 +0,0 @@ -import asyncio -import logging -import os -from typing import Tuple - -import aiohttp -from dotenv import load_dotenv - -from pipecat.frames.frames import AudioFrame, EndFrame, ImageFrame, LLMContextFrame, TextFrame -from pipecat.pipeline.pipeline import Pipeline -from pipecat.processors.aggregators import SentenceAggregator -from pipecat.processors.aggregators.llm_context import LLMContext -from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair -from pipecat.runner.daily import configure -from pipecat.services.azure import AzureLLMService, AzureTTSService -from pipecat.services.elevenlabs import ElevenLabsTTSService -from pipecat.services.fal import FalImageGenService -from pipecat.transports.daily.transport import DailyTransport - -load_dotenv(override=True) - -logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s") -logger = logging.getLogger("pipecat") -logger.setLevel(logging.DEBUG) - - -async def main(): - async with aiohttp.ClientSession() as session: - (room_url, _) = await configure(session) - - transport = DailyTransport( - room_url, - None, - "Respond bot", - duration_minutes=10, - mic_enabled=True, - mic_sample_rate=16000, - camera_enabled=True, - camera_width=1024, - camera_height=1024, - ) - - llm = AzureLLMService( - api_key=os.getenv("AZURE_CHATGPT_API_KEY"), - endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"), - model=os.getenv("AZURE_CHATGPT_MODEL"), - ) - tts1 = AzureTTSService( - api_key=os.getenv("AZURE_SPEECH_API_KEY"), - region=os.getenv("AZURE_SPEECH_REGION"), - ) - tts2 = ElevenLabsTTSService( - api_key=os.getenv("ELEVENLABS_API_KEY"), - voice_id="jBpfuIE2acCO8z3wKNLl", - ) - dalle = FalImageGenService( - params=FalImageGenService.InputParams(image_size="1024x1024"), - aiohttp_session=session, - key=os.getenv("FAL_KEY"), - ) - - bot1_messages = [ - { - "role": "system", - "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.", - }, - ] - bot2_messages = [ - { - "role": "system", - "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.", - }, - ] - - async def get_text_and_audio(messages) -> Tuple[str, bytearray]: - """This function streams text from the LLM and uses the TTS service to convert - that text to speech as it's received. - """ - source_queue = asyncio.Queue() - sink_queue = asyncio.Queue() - sentence_aggregator = SentenceAggregator() - pipeline = Pipeline([llm, sentence_aggregator, tts1], source_queue, sink_queue) - - await source_queue.put(LLMContextFrame(LLMContext(messages))) - await source_queue.put(EndFrame()) - await pipeline.run_pipeline() - - message = "" - all_audio = bytearray() - while sink_queue.qsize(): - frame = sink_queue.get_nowait() - if isinstance(frame, TextFrame): - message += frame.text - elif isinstance(frame, AudioFrame): - all_audio.extend(frame.audio) - - return (message, all_audio) - - async def get_bot1_statement(): - message, audio = await get_text_and_audio(bot1_messages) - - bot1_messages.append({"role": "assistant", "content": message}) - bot2_messages.append({"role": "user", "content": message}) - - return audio - - async def get_bot2_statement(): - message, audio = await get_text_and_audio(bot2_messages) - - bot2_messages.append({"role": "assistant", "content": message}) - bot1_messages.append({"role": "user", "content": message}) - - return audio - - async def argue(): - for i in range(100): - print(f"In iteration {i}") - - bot1_description = "A woman conservatively dressed as a librarian in a library surrounded by books, cartoon, serious, highly detailed" - - (audio1, image_data1) = await asyncio.gather( - get_bot1_statement(), dalle.run_image_gen(bot1_description) - ) - await transport.send_queue.put( - [ - ImageFrame(image_data1[1], image_data1[2]), - AudioFrame(audio1), - ] - ) - - bot2_description = "A cat dressed in a hot dog costume, cartoon, bright colors, funny, highly detailed" - - (audio2, image_data2) = await asyncio.gather( - get_bot2_statement(), dalle.run_image_gen(bot2_description) - ) - await transport.send_queue.put( - [ - ImageFrame(image_data2[1], image_data2[2]), - AudioFrame(audio2), - ] - ) - - await asyncio.gather(transport.run(), argue()) - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/examples/foundational/08-custom-frame-processor.py b/examples/foundational/08-custom-frame-processor.py new file mode 100644 index 000000000..20da4f876 --- /dev/null +++ b/examples/foundational/08-custom-frame-processor.py @@ -0,0 +1,170 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +import io +import os +import re + +from dotenv import load_dotenv +from loguru import logger + +from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams +from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3 +from pipecat.audio.vad.silero import SileroVADAnalyzer +from pipecat.audio.vad.vad_analyzer import VADParams +from pipecat.frames.frames import ( + Frame, + LLMRunFrame, + MetricsFrame, +) +from pipecat.pipeline.pipeline import Pipeline +from pipecat.pipeline.runner import PipelineRunner +from pipecat.pipeline.task import PipelineParams, PipelineTask +from pipecat.processors.aggregators.llm_context import LLMContext +from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair +from pipecat.processors.frame_processor import FrameDirection, FrameProcessor +from pipecat.runner.types import RunnerArguments +from pipecat.runner.utils import create_transport +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 BaseTransport, TransportParams +from pipecat.transports.daily.transport import DailyParams + +load_dotenv(override=True) + + +def format_metrics(metrics, indent=0): + lines = [] + tab = "\t" * indent + + for metric in metrics: + lines.append(tab + type(metric).__name__) + for field, value in vars(metric).items(): + if hasattr(value, "__dict__") and not isinstance( + value, (str, int, float, bool, type(None)) + ): + lines.append(f"{tab}\t{field}={type(value).__name__}") + for k, v in vars(value).items(): + lines.append(f"{tab}\t\t{k}={repr(v)}") + else: + lines.append(f"{tab}\t{field}={repr(value)}") + + return "\n".join(lines) + + +class MetricsFrameLogger(FrameProcessor): + """MetricsFrameLogger formats and logs all MetericsFrames""" + + def __init__(self, **kwargs): + super().__init__(**kwargs) + + async def process_frame(self, frame: Frame, direction: FrameDirection): + await super().process_frame(frame, direction) + + if isinstance(frame, MetricsFrame): + logger.info(f"{frame.name}\n {format_metrics(frame.data)}") + await self.push_frame(frame, direction) + + # ALWAYS push all frames + else: + # SUPER IMPORTANT: always push every frame! + await self.push_frame(frame, direction) + + +# We store functions so objects (e.g. SileroVADAnalyzer) don't get +# instantiated. The function will be called when the desired transport gets +# selected. +transport_params = { + "daily": lambda: DailyParams( + audio_in_enabled=True, + audio_out_enabled=True, + vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)), + turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()), + ), + "webrtc": lambda: TransportParams( + audio_in_enabled=True, + audio_out_enabled=True, + video_out_enabled=True, + vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)), + turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()), + ), +} + + +async def run_bot(transport: BaseTransport, runner_args: RunnerArguments): + logger.info(f"Starting bot") + + 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 = LLMContext(messages) + context_aggregator = LLMContextAggregatorPair(context) + + metrics_frame_processor = MetricsFrameLogger() + + pipeline = Pipeline( + [ + transport.input(), + stt, + context_aggregator.user(), + llm, + tts, + transport.output(), + context_aggregator.assistant(), + metrics_frame_processor, # pretty print metrics frames + ] + ) + + task = PipelineTask( + pipeline, + params=PipelineParams( + enable_metrics=True, + enable_usage_metrics=True, + ), + idle_timeout_secs=runner_args.pipeline_idle_timeout_secs, + ) + + @transport.event_handler("on_client_connected") + async def on_client_connected(transport, client): + logger.info(f"Client connected: {client}") + # Kick off the conversation. + messages.append({"role": "system", "content": "Please introduce yourself to the user."}) + await task.queue_frames([LLMRunFrame()]) + + @transport.event_handler("on_client_disconnected") + async def on_client_disconnected(transport, client): + logger.info(f"Client disconnected") + await task.cancel() + + runner = PipelineRunner(handle_sigint=runner_args.handle_sigint) + + await runner.run(task) + + +async def bot(runner_args: RunnerArguments): + """Main bot entry point compatible with Pipecat Cloud.""" + transport = await create_transport(runner_args, transport_params) + await run_bot(transport, runner_args) + + +if __name__ == "__main__": + from pipecat.runner.run import main + + main()