# # Copyright (c) 2024–2025, Daily # # SPDX-License-Identifier: BSD 2-Clause License # import argparse import asyncio import functools import os import sys from call_connection_manager import CallConfigManager, SessionManager from dotenv import load_dotenv from loguru import logger from pipecat.audio.vad.silero import SileroVADAnalyzer from pipecat.frames.frames import ( EndFrame, EndTaskFrame, InputAudioRawFrame, StopTaskFrame, TranscriptionFrame, UserStartedSpeakingFrame, UserStoppedSpeakingFrame, ) from pipecat.pipeline.pipeline import Pipeline from pipecat.pipeline.runner import PipelineRunner from pipecat.pipeline.task import PipelineParams, PipelineTask from pipecat.processors.frame_processor import FrameDirection, FrameProcessor from pipecat.services.cartesia.tts import CartesiaTTSService from pipecat.services.deepgram.stt import DeepgramSTTService from pipecat.services.google.google import GoogleLLMContext from pipecat.services.google.llm import GoogleLLMService from pipecat.services.llm_service import FunctionCallParams from pipecat.transports.services.daily import ( DailyParams, DailyTransport, ) load_dotenv(override=True) logger.remove(0) logger.add(sys.stderr, level="DEBUG") daily_api_key = os.getenv("DAILY_API_KEY", "") daily_api_url = os.getenv("DAILY_API_URL", "https://api.daily.co/v1") # ------------ HELPER CLASSES ------------ class UserAudioCollector(FrameProcessor): """Collects audio frames in a buffer, then adds them to the LLM context when the user stops speaking.""" def __init__(self, context, user_context_aggregator): super().__init__() self._context = context self._user_context_aggregator = user_context_aggregator self._audio_frames = [] self._start_secs = 0.2 # this should match VAD start_secs (hardcoding for now) self._user_speaking = False async def process_frame(self, frame, direction): await super().process_frame(frame, direction) if isinstance(frame, TranscriptionFrame): # Skip transcription frames - we're handling audio directly return elif isinstance(frame, UserStartedSpeakingFrame): self._user_speaking = True elif isinstance(frame, UserStoppedSpeakingFrame): self._user_speaking = False self._context.add_audio_frames_message(audio_frames=self._audio_frames) await self._user_context_aggregator.push_frame( self._user_context_aggregator.get_context_frame() ) elif isinstance(frame, InputAudioRawFrame): if self._user_speaking: # When speaking, collect frames self._audio_frames.append(frame) else: # Maintain a rolling buffer of recent audio (for start of speech) self._audio_frames.append(frame) frame_duration = len(frame.audio) / 16 * frame.num_channels / frame.sample_rate buffer_duration = frame_duration * len(self._audio_frames) while buffer_duration > self._start_secs: self._audio_frames.pop(0) buffer_duration -= frame_duration await self.push_frame(frame, direction) class FunctionHandlers: """Handlers for the voicemail detection bot functions.""" def __init__(self, session_manager): self.session_manager = session_manager self.prompt = None # Can be set externally async def voicemail_response(self, params: FunctionCallParams): """Function the bot can call to leave a voicemail message.""" message = """You are Chatbot leaving a voicemail message. Say EXACTLY this message and then terminate the call: 'Hello, this is a message for Pipecat example user. This is Chatbot. Please call back on 123-456-7891. Thank you.'""" await params.result_callback(message) async def human_conversation(self, params: FunctionCallParams): """Function called when bot detects it's talking to a human.""" # Update state to indicate human was detected self.session_manager.call_flow_state.set_human_detected() await params.llm.push_frame(StopTaskFrame(), FrameDirection.UPSTREAM) # ------------ MAIN FUNCTION ------------ async def main( room_url: str, token: str, body: dict, ): # ------------ CONFIGURATION AND SETUP ------------ # Create a configuration manager from the provided body call_config_manager = CallConfigManager.from_json_string(body) if body else CallConfigManager() # Get important configuration values dialout_settings = call_config_manager.get_dialout_settings() test_mode = call_config_manager.is_test_mode() # Get caller info (might be None for dialout scenarios) caller_info = call_config_manager.get_caller_info() logger.info(f"Caller info: {caller_info}") # Initialize the session manager session_manager = SessionManager() # ------------ TRANSPORT AND SERVICES SETUP ------------ # Initialize transport transport = DailyTransport( room_url, token, "Voicemail Detection Bot", DailyParams( api_url=daily_api_url, api_key=daily_api_key, audio_in_enabled=True, audio_out_enabled=True, video_out_enabled=False, vad_analyzer=SileroVADAnalyzer(), ), ) # Initialize TTS tts = CartesiaTTSService( api_key=os.getenv("CARTESIA_API_KEY", ""), voice_id="b7d50908-b17c-442d-ad8d-810c63997ed9", # Use Helpful Woman voice by default ) # Initialize speech-to-text service (for human conversation phase) stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY")) # ------------ FUNCTION DEFINITIONS ------------ async def terminate_call( params: FunctionCallParams, session_manager=None, ): """Function the bot can call to terminate the call.""" if session_manager: # Set call terminated flag in the session manager session_manager.call_flow_state.set_call_terminated() await params.llm.queue_frame(EndTaskFrame(), FrameDirection.UPSTREAM) # ------------ VOICEMAIL DETECTION PHASE SETUP ------------ # Define tools for both LLMs tools = [ { "function_declarations": [ { "name": "switch_to_voicemail_response", "description": "Call this function when you detect this is a voicemail system.", }, { "name": "switch_to_human_conversation", "description": "Call this function when you detect this is a human.", }, { "name": "terminate_call", "description": "Call this function to terminate the call.", }, ] } ] # Get voicemail detection prompt voicemail_detection_prompt = call_config_manager.get_prompt("voicemail_detection_prompt") if voicemail_detection_prompt: system_instruction = voicemail_detection_prompt else: system_instruction = """You are Chatbot trying to determine if this is a voicemail system or a human. If you hear any of these phrases (or very similar ones): - "Please leave a message after the beep" - "No one is available to take your call" - "Record your message after the tone" - "You have reached voicemail for..." - "You have reached [phone number]" - "[phone number] is unavailable" - "The person you are trying to reach..." - "The number you have dialed..." - "Your call has been forwarded to an automated voice messaging system" Then call the function switch_to_voicemail_response. If it sounds like a human (saying hello, asking questions, etc.), call the function switch_to_human_conversation. DO NOT say anything until you've determined if this is a voicemail or human. If you are asked to terminate the call, **IMMEDIATELY** call the `terminate_call` function. **FAILURE TO CALL `terminate_call` IMMEDIATELY IS A MISTAKE.**""" # Initialize voicemail detection LLM voicemail_detection_llm = GoogleLLMService( model="models/gemini-2.0-flash-lite", # Lighter model for faster detection api_key=os.getenv("GOOGLE_API_KEY"), system_instruction=system_instruction, tools=tools, ) # Initialize context and context aggregator voicemail_detection_context = GoogleLLMContext() voicemail_detection_context_aggregator = voicemail_detection_llm.create_context_aggregator( voicemail_detection_context ) # Get custom voicemail prompt if available voicemail_prompt = call_config_manager.get_prompt("voicemail_prompt") # Set up function handlers handlers = FunctionHandlers(session_manager) handlers.prompt = voicemail_prompt # Set custom prompt if available # Register functions with the voicemail detection LLM voicemail_detection_llm.register_function( "switch_to_voicemail_response", handlers.voicemail_response, ) voicemail_detection_llm.register_function( "switch_to_human_conversation", handlers.human_conversation ) voicemail_detection_llm.register_function( "terminate_call", lambda params: terminate_call(params, session_manager) ) # Set up audio collector for handling audio input voicemail_detection_audio_collector = UserAudioCollector( voicemail_detection_context, voicemail_detection_context_aggregator.user() ) # Build voicemail detection pipeline voicemail_detection_pipeline = Pipeline( [ transport.input(), # Transport user input voicemail_detection_audio_collector, # Collect audio frames voicemail_detection_context_aggregator.user(), # User context voicemail_detection_llm, # LLM tts, # TTS transport.output(), # Transport bot output voicemail_detection_context_aggregator.assistant(), # Assistant context ] ) # Create pipeline task voicemail_detection_pipeline_task = PipelineTask( voicemail_detection_pipeline, params=PipelineParams(allow_interruptions=True), ) # ------------ EVENT HANDLERS ------------ @transport.event_handler("on_joined") async def on_joined(transport, data): # Start dialout if needed if not test_mode and dialout_settings: logger.debug("Dialout settings detected; starting dialout") await call_config_manager.start_dialout(transport, dialout_settings) @transport.event_handler("on_dialout_connected") async def on_dialout_connected(transport, data): logger.debug(f"Dial-out connected: {data}") @transport.event_handler("on_dialout_answered") async def on_dialout_answered(transport, data): logger.debug(f"Dial-out answered: {data}") # Start capturing transcription await transport.capture_participant_transcription(data["sessionId"]) @transport.event_handler("on_first_participant_joined") async def on_first_participant_joined(transport, participant): logger.debug(f"First participant joined: {participant['id']}") if test_mode: await transport.capture_participant_transcription(participant["id"]) @transport.event_handler("on_participant_left") async def on_participant_left(transport, participant, reason): # Mark that a participant left early session_manager.call_flow_state.set_participant_left_early() await voicemail_detection_pipeline_task.queue_frame(EndFrame()) # ------------ RUN VOICEMAIL DETECTION PIPELINE ------------ if test_mode: logger.debug("Detect voicemail example. You can test this in Daily Prebuilt") runner = PipelineRunner() print("!!! starting voicemail detection pipeline") try: await runner.run(voicemail_detection_pipeline_task) except Exception as e: logger.error(f"Error in voicemail detection pipeline: {e}") import traceback logger.error(traceback.format_exc()) print("!!! Done with voicemail detection pipeline") # Check if we should exit early if ( session_manager.call_flow_state.participant_left_early or session_manager.call_flow_state.call_terminated ): if session_manager.call_flow_state.participant_left_early: print("!!! Participant left early; terminating call") elif session_manager.call_flow_state.call_terminated: print("!!! Bot terminated call; not proceeding to human conversation") return # ------------ HUMAN CONVERSATION PHASE SETUP ------------ # Get human conversation prompt human_conversation_prompt = call_config_manager.get_prompt("human_conversation_prompt") if human_conversation_prompt: human_conversation_system_instruction = human_conversation_prompt else: human_conversation_system_instruction = """You are Chatbot talking to a human. Be friendly and helpful. Start with: "Hello! I'm a friendly chatbot. How can I help you today?" Keep your responses brief and to the point. Listen to what the person says. When the person indicates they're done with the conversation by saying something like: - "Goodbye" - "That's all" - "I'm done" - "Thank you, that's all I needed" THEN say: "Thank you for chatting. Goodbye!" and call the terminate_call function.""" # Initialize human conversation LLM human_conversation_llm = GoogleLLMService( model="models/gemini-2.0-flash-001", # Full model for better conversation api_key=os.getenv("GOOGLE_API_KEY"), system_instruction=human_conversation_system_instruction, tools=tools, ) # Initialize context and context aggregator human_conversation_context = GoogleLLMContext() human_conversation_context_aggregator = human_conversation_llm.create_context_aggregator( human_conversation_context ) # Register terminate function with the human conversation LLM human_conversation_llm.register_function( "terminate_call", functools.partial(terminate_call, session_manager=session_manager) ) # Build human conversation pipeline human_conversation_pipeline = Pipeline( [ transport.input(), # Transport user input stt, # Speech-to-text human_conversation_context_aggregator.user(), # User context human_conversation_llm, # LLM tts, # TTS transport.output(), # Transport bot output human_conversation_context_aggregator.assistant(), # Assistant context ] ) # Create pipeline task human_conversation_pipeline_task = PipelineTask( human_conversation_pipeline, params=PipelineParams(allow_interruptions=True), ) # Update participant left handler for human conversation phase @transport.event_handler("on_participant_left") async def on_participant_left(transport, participant, reason): await voicemail_detection_pipeline_task.queue_frame(EndFrame()) await human_conversation_pipeline_task.queue_frame(EndFrame()) # ------------ RUN HUMAN CONVERSATION PIPELINE ------------ print("!!! starting human conversation pipeline") # Initialize the context with system message human_conversation_context_aggregator.user().set_messages( [call_config_manager.create_system_message(human_conversation_system_instruction)] ) # Queue the context frame to start the conversation await human_conversation_pipeline_task.queue_frames( [human_conversation_context_aggregator.user().get_context_frame()] ) # Run the human conversation pipeline try: await runner.run(human_conversation_pipeline_task) except Exception as e: logger.error(f"Error in voicemail detection pipeline: {e}") import traceback logger.error(traceback.format_exc()) print("!!! Done with human conversation pipeline") # ------------ SCRIPT ENTRY POINT ------------ if __name__ == "__main__": parser = argparse.ArgumentParser(description="Pipecat Voicemail Detection Bot") parser.add_argument("-u", "--url", type=str, help="Room URL") parser.add_argument("-t", "--token", type=str, help="Room Token") parser.add_argument("-b", "--body", type=str, help="JSON configuration string") args = parser.parse_args() # Log the arguments for debugging logger.info(f"Room URL: {args.url}") logger.info(f"Token: {args.token}") logger.info(f"Body provided: {bool(args.body)}") asyncio.run(main(args.url, args.token, args.body))