POC demo in progress
This commit is contained in:
Mark Backman
265
examples/foundational/voicemail_test.py
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265
examples/foundational/voicemail_test.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 asyncio
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import os
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from typing import Optional
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from dotenv import load_dotenv
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from loguru import logger
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import (
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BotInterruptionFrame,
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CancelFrame,
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EndFrame,
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Frame,
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LLMTextFrame,
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StartFrame,
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TTSSpeakFrame,
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)
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from pipecat.pipeline.parallel_pipeline import ParallelPipeline
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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.openai_llm_context import OpenAILLMContext
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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.llm_service import LLMService
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from pipecat.services.openai.llm import OpenAILLMService
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from pipecat.sync.base_notifier import BaseNotifier
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from pipecat.sync.event_notifier import EventNotifier
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
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from pipecat.transports.services.daily import DailyParams
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load_dotenv(override=True)
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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(),
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),
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"twilio": lambda: FastAPIWebsocketParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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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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vad_analyzer=SileroVADAnalyzer(),
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),
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}
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class VoicemailDetector(ParallelPipeline):
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def __init__(self, llm: LLMService):
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# Initialize LLM
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self._classifier_llm = llm
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self._messages = [
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{
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"role": "system",
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"content": """You are a voicemail detection classifier. Your job is to determine if the caller is leaving a voicemail message or trying to have a live conversation.
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VOICEMAIL INDICATORS (respond "YES"):
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- One-way communication (caller talks without expecting immediate responses)
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- Messages like "Hi, this is [name], please call me back"
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- "I'm calling about..." followed by details without pausing for response
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- "Leave me a message" or "call me when you get this"
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- Monologue-style speech patterns
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- Mentions of time/date when they're calling
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- Business-like messages with contact information
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CONVERSATION INDICATORS (respond "NO"):
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- Interactive speech ("Hello?", "Are you there?", "Can you hear me?")
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- Questions directed at the recipient expecting immediate answers
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- Responses to prompts or questions
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- Back-and-forth dialogue patterns
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- Greetings expecting responses ("Hi, how are you?")
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- Real-time problem solving or discussion
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Respond with ONLY "YES" if it's a voicemail, or "NO" if it's a conversation attempt. Do not explain your reasoning.""",
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},
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]
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self._context = OpenAILLMContext(self._messages)
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self._context_aggregator = llm.create_context_aggregator(self._context)
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self._conversation_notifier = EventNotifier()
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self._classifier_gate = self.ClassifierGate(self._conversation_notifier)
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self._voicemail_processor = self.VoicemailProcessor(self._conversation_notifier)
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self._passthrough_processor = self.PassThroughProcessor()
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super().__init__(
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# Conversation branch
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[self._passthrough_processor],
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# Classifer branch
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[
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self._classifier_gate,
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self._context_aggregator.user(),
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self._classifier_llm,
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self._voicemail_processor,
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self._context_aggregator.assistant(),
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],
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)
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class ClassifierGate(FrameProcessor):
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def __init__(self, notifier: BaseNotifier):
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super().__init__()
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self._notifier = notifier
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self._gate_opened = True
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self._gate_task: Optional[asyncio.Task] = None
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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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if isinstance(frame, StartFrame):
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# Start the task immediately, don't wait for other conditions
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self._gate_task = self.create_task(self._wait_for_notification())
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logger.info(f"{self}: Gate task started, waiting for notification")
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elif isinstance(frame, (EndFrame, CancelFrame)):
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if self._gate_task:
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await self.cancel_task(self._gate_task)
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self._gate_task = None
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if self._gate_opened:
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await self.push_frame(frame, direction)
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elif not self._gate_opened and isinstance(frame, BotInterruptionFrame):
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await self.push_frame(frame, direction)
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async def _wait_for_notification(self):
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try:
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logger.info(f"{self}: Waiting for notification...")
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await self._notifier.wait()
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logger.info(f"{self}: Received notification!")
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if self._gate_opened:
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self._gate_opened = False
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logger.info(f"{self}: Gate closed")
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except asyncio.CancelledError:
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logger.debug(f"{self}: Gate task was cancelled")
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raise
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except Exception as e:
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logger.exception(f"{self}: Error in gate task: {e}")
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raise
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class VoicemailProcessor(FrameProcessor):
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def __init__(self, notifier: BaseNotifier):
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super().__init__()
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self._notifier = notifier
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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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if isinstance(frame, LLMTextFrame):
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# Check if the frame is a NO response, notify the notifier
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response = frame.text.strip().upper()
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print(f"Response from LLM: {response}")
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if "NO" in response:
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logger.info(f"{self}: User conversation, notifying to close gate")
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await self._notifier.notify()
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elif "YES" in response:
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logger.info(f"{self}: User is leaving a voicemail, push BotInterruptionFrame")
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# If the user is leaving a voicemail, we push a BotInterruptionFrame
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await self._notifier.notify()
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await self.push_frame(BotInterruptionFrame(), FrameDirection.UPSTREAM)
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# How do we know when to send this?!
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await asyncio.sleep(3)
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await self.push_frame(TTSSpeakFrame("This is Mark. Call me back later."))
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else:
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# Push the frame
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await self.push_frame(frame, direction)
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class PassThroughProcessor(FrameProcessor):
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def __init__(self):
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super().__init__()
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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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await self.push_frame(frame, direction)
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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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voicemail_detector_llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
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voicemail_detector = VoicemailDetector(voicemail_detector_llm)
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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 = OpenAILLMContext(messages)
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context_aggregator = llm.create_context_aggregator(context)
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pipeline = Pipeline(
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[
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transport.input(), # Transport user input
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stt,
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voicemail_detector,
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context_aggregator.user(), # User responses
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llm, # LLM
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tts, # TTS
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transport.output(), # Transport bot output
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context_aggregator.assistant(), # Assistant spoken responses
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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")
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# # Kick off the conversation.
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# messages.append({"role": "system", "content": "Please introduce yourself to the user."})
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# await task.queue_frames([context_aggregator.user().get_context_frame()])
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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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