add 47-custom-frame-processor.py to foundational examples
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vipyne
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examples/foundational/47-custom-frame-processor.py
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223
examples/foundational/47-custom-frame-processor.py
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#
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# Copyright (c) 2024–2025, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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import io
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import os
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import re
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from dotenv import load_dotenv
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from loguru import logger
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from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
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from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.audio.vad.vad_analyzer import VADParams
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from pipecat.frames.frames import (
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BotStartedSpeakingFrame,
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BotStoppedSpeakingFrame,
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CancelFrame,
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EndFrame,
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Frame,
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FunctionCallResultFrame,
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InputAudioRawFrame,
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InterruptionFrame,
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LLMRunFrame,
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LLMTextFrame,
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StartFrame,
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UserStartedSpeakingFrame,
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UserStoppedSpeakingFrame,
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VADUserStartedSpeakingFrame,
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)
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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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.processors.frame_processor import FrameDirection, FrameProcessor
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from pipecat.runner.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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from pipecat.services.cartesia.tts import CartesiaTTSService
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from pipecat.services.deepgram.stt import DeepgramSTTService
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from pipecat.services.openai.llm import OpenAILLMService
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.daily.transport import DailyParams
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load_dotenv(override=True)
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class CustomFrameProcessor(FrameProcessor):
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"""CustomFrameProcessor does 3 things:
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1. keeps count of `InputAudioRawFrame` frames and logs count
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when a `UserStoppedSpeakingFrame` is emitted.
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2. Filters `LLMTextFrame` frames and replaces "the" with "the pumpkin".
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3. Logs the following frames:
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BotStartedSpeakingFrame
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BotStoppedSpeakingFrame
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CancelFrame
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EndFrame
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InterruptionFrame
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StartFrame
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UserStartedSpeakingFrame
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VADUserStartedSpeakingFrame
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4. 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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self._raw_audio_input_frame_count = 0
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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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#### 1.
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# InputAudioRawFrames are noisy- probably don't want to log every instance
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# keep a count and only log it when we see `UserStoppedSpeakingFrame`
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if isinstance(frame, InputAudioRawFrame):
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self._raw_audio_input_frame_count = self._raw_audio_input_frame_count + 1
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await self.push_frame(frame, direction)
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elif isinstance(frame, UserStoppedSpeakingFrame):
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logger.info(
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f"* * frame: {frame}; number of `InputAudioRawFrame` frames so far: {self._raw_audio_input_frame_count}"
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)
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await self.push_frame(frame, direction)
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#### 2.
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# everytime the LLM's response includes "the", replace it with "the pumpkin"
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elif isinstance(frame, LLMTextFrame):
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if "the" in frame.text:
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text = re.sub(r" the\b", " the pumpkin", frame.text)
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frame.text = text
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await self.push_frame(frame, direction)
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#### 3.
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# frames types to log
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elif isinstance(
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frame,
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(
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BotStartedSpeakingFrame,
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BotStoppedSpeakingFrame,
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CancelFrame,
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EndFrame,
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InterruptionFrame,
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StartFrame,
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UserStartedSpeakingFrame,
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VADUserStartedSpeakingFrame,
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),
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):
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logger.info(f"* * frame: {frame}")
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await self.push_frame(frame, direction)
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#### 4.
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# ALWAYS push all other frames
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else:
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# SUPER IMPORTANT: always push every frame!
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await self.push_frame(frame, direction)
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# We store functions so objects (e.g. SileroVADAnalyzer) don't get
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# instantiated. The function will be called when the desired transport gets
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# selected.
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transport_params = {
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"daily": lambda: DailyParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
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turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
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),
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"webrtc": lambda: TransportParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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video_out_enabled=True,
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vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
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turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
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),
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}
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async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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logger.info(f"Starting bot")
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stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
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)
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
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custom_frame_processor = CustomFrameProcessor()
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messages = [
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{
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"role": "system",
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"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.",
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},
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]
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context = LLMContext(messages)
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context_aggregator = LLMContextAggregatorPair(context)
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pipeline = Pipeline(
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[
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transport.input(),
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stt,
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context_aggregator.user(),
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llm,
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custom_frame_processor, # filter and log frames
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tts,
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transport.output(),
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context_aggregator.assistant(),
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]
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)
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task = PipelineTask(
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pipeline,
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params=PipelineParams(
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enable_metrics=True,
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enable_usage_metrics=True,
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),
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idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
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)
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@transport.event_handler("on_client_connected")
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async def on_client_connected(transport, client):
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logger.info(f"Client connected: {client}")
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# Kick off the conversation.
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messages.append(
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{
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"role": "system",
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"content": "Please introduce yourself to the user and inform them that your responses illustrate use of a Custom Frame Processor.",
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}
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)
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await task.queue_frames([LLMRunFrame()])
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@transport.event_handler("on_client_disconnected")
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async def on_client_disconnected(transport, client):
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logger.info(f"Client disconnected")
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await task.cancel()
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runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
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await runner.run(task)
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async def bot(runner_args: RunnerArguments):
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"""Main bot entry point compatible with Pipecat Cloud."""
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transport = await create_transport(runner_args, transport_params)
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await run_bot(transport, runner_args)
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if __name__ == "__main__":
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from pipecat.runner.run import main
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main()
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