From abf015026177d4c9f5d60485af89ce30a3d7b61b Mon Sep 17 00:00:00 2001 From: vipyne Date: Wed, 15 Oct 2025 10:45:36 -0500 Subject: [PATCH] add 47-custom-frame-processor.py to foundational examples --- .../foundational/47-custom-frame-processor.py | 223 ++++++++++++++++++ 1 file changed, 223 insertions(+) create mode 100644 examples/foundational/47-custom-frame-processor.py diff --git a/examples/foundational/47-custom-frame-processor.py b/examples/foundational/47-custom-frame-processor.py new file mode 100644 index 000000000..7de01ab17 --- /dev/null +++ b/examples/foundational/47-custom-frame-processor.py @@ -0,0 +1,223 @@ +# +# 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 ( + BotStartedSpeakingFrame, + BotStoppedSpeakingFrame, + CancelFrame, + EndFrame, + Frame, + FunctionCallResultFrame, + InputAudioRawFrame, + InterruptionFrame, + LLMRunFrame, + LLMTextFrame, + StartFrame, + UserStartedSpeakingFrame, + UserStoppedSpeakingFrame, + VADUserStartedSpeakingFrame, +) +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) + + +class CustomFrameProcessor(FrameProcessor): + """CustomFrameProcessor does 3 things: + + 1. keeps count of `InputAudioRawFrame` frames and logs count + when a `UserStoppedSpeakingFrame` is emitted. + + 2. Filters `LLMTextFrame` frames and replaces "the" with "the pumpkin". + + 3. Logs the following frames: + BotStartedSpeakingFrame + BotStoppedSpeakingFrame + CancelFrame + EndFrame + InterruptionFrame + StartFrame + UserStartedSpeakingFrame + VADUserStartedSpeakingFrame + + 4. Always pushes all frames + + """ + + def __init__(self): + super().__init__() + self._raw_audio_input_frame_count = 0 + + async def process_frame(self, frame: Frame, direction: FrameDirection): + await super().process_frame(frame, direction) + + #### 1. + # InputAudioRawFrames are noisy- probably don't want to log every instance + # keep a count and only log it when we see `UserStoppedSpeakingFrame` + if isinstance(frame, InputAudioRawFrame): + self._raw_audio_input_frame_count = self._raw_audio_input_frame_count + 1 + await self.push_frame(frame, direction) + + elif isinstance(frame, UserStoppedSpeakingFrame): + logger.info( + f"* * frame: {frame}; number of `InputAudioRawFrame` frames so far: {self._raw_audio_input_frame_count}" + ) + await self.push_frame(frame, direction) + + #### 2. + # everytime the LLM's response includes "the", replace it with "the pumpkin" + elif isinstance(frame, LLMTextFrame): + if "the" in frame.text: + text = re.sub(r" the\b", " the pumpkin", frame.text) + frame.text = text + await self.push_frame(frame, direction) + + #### 3. + # frames types to log + elif isinstance( + frame, + ( + BotStartedSpeakingFrame, + BotStoppedSpeakingFrame, + CancelFrame, + EndFrame, + InterruptionFrame, + StartFrame, + UserStartedSpeakingFrame, + VADUserStartedSpeakingFrame, + ), + ): + logger.info(f"* * frame: {frame}") + await self.push_frame(frame, direction) + + #### 4. + # ALWAYS push all other 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")) + + custom_frame_processor = CustomFrameProcessor() + + 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) + + pipeline = Pipeline( + [ + transport.input(), + stt, + context_aggregator.user(), + llm, + custom_frame_processor, # filter and log frames + tts, + transport.output(), + context_aggregator.assistant(), + ] + ) + + 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 and inform them that your responses illustrate use of a Custom Frame Processor.", + } + ) + 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()