More updates—added new voicemail module
This commit is contained in:
Mark Backman
@@ -6,38 +6,27 @@
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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.frames.frames import EndFrame, EndTaskFrame, TTSSpeakFrame
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from pipecat.observers.loggers.debug_log_observer import DebugLogObserver
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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.processors.frame_processor import FrameDirection
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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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from pipecat.utils.voicemail.voicemail_detector import VoicemailDetector
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load_dotenv(override=True)
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@@ -63,130 +52,23 @@ transport_params = {
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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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async def handle_voicemail(processor):
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"""Called when a voicemail is detected.
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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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Args:
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processor: The VoicemailProcessor instance. processor.push_frame() is
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available to push frames.
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"""
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logger.info("Voicemail detected! Playing greeting...")
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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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# Wait a moment for interruption to clear
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await asyncio.sleep(1)
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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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# Push frames using standard Pipecat pattern
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await processor.push_frame(
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TTSSpeakFrame("This is Mattie. Call me back when you can!"),
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)
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await processor.push_frame(EndTaskFrame(), FrameDirection.UPSTREAM)
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async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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@@ -202,7 +84,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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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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voicemail_detector = VoicemailDetector(
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llm=voicemail_detector_llm, on_voicemail_detected=handle_voicemail
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)
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messages = [
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{
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@@ -234,6 +118,14 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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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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observers=[
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DebugLogObserver(
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frame_types={
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EndFrame: None,
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EndTaskFrame: None,
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}
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),
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],
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)
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@transport.event_handler("on_client_connected")
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0
src/pipecat/utils/voicemail/__init__.py
Normal file
0
src/pipecat/utils/voicemail/__init__.py
Normal file
224
src/pipecat/utils/voicemail/voicemail_detector.py
Normal file
224
src/pipecat/utils/voicemail/voicemail_detector.py
Normal file
@@ -0,0 +1,224 @@
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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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"""Voicemail detection module for Pipecat.
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This module provides voicemail detection capabilities using parallel pipeline
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processing to classify incoming calls as either voicemail messages or live
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conversations.
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"""
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import asyncio
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from typing import Awaitable, Callable, Optional
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from loguru import logger
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from pipecat.frames.frames import (
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BotInterruptionFrame,
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CancelFrame,
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CancelTaskFrame,
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EndFrame,
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EndTaskFrame,
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Frame,
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LLMTextFrame,
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StartFrame,
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)
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from pipecat.pipeline.parallel_pipeline import ParallelPipeline
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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.services.llm_service import LLMService
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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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class ClassifierGate(FrameProcessor):
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"""Gate processor that controls frame flow based on classification decisions.
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The gate starts open and closes permanently once a classification decision
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is made (YES or NO). This ensures the classifier only runs until a definitive
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decision is reached.
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"""
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def __init__(self, notifier: BaseNotifier):
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"""Initialize the classifier gate.
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Args:
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notifier: Notifier that signals when to close the gate.
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"""
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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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"""Process frames and control gate state.
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Args:
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frame: The frame to process.
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direction: The direction of frame flow.
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"""
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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(
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frame, (BotInterruptionFrame, EndTaskFrame, EndFrame, CancelTaskFrame, CancelFrame)
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):
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await self.push_frame(frame, direction)
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async def _wait_for_notification(self):
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"""Wait for classification decision notification."""
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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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"""Processor that handles LLM classification responses and triggers callbacks.
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Processes LLM text responses to determine if the call is a voicemail (YES)
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or conversation (NO), then triggers appropriate actions including developer
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callbacks for voicemail detection.
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"""
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def __init__(
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self,
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notifier: BaseNotifier,
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on_voicemail_detected: Optional[Callable[["VoicemailProcessor"], Awaitable[None]]] = None,
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):
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"""Initialize the voicemail processor.
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Args:
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notifier: Notifier to signal classification decisions.
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on_voicemail_detected: Callback function called when voicemail is detected.
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"""
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super().__init__()
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self._notifier = notifier
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self._on_voicemail_detected = on_voicemail_detected
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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"""Process frames and handle LLM classification responses.
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Args:
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frame: The frame to process.
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direction: The direction of frame flow.
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"""
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await super().process_frame(frame, direction)
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if isinstance(frame, LLMTextFrame):
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response = frame.text.strip().upper()
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if "NO" in response:
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logger.info(f"{self}: CONVERSATION detected - 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}: VOICEMAIL detected - triggering callback")
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# Notify gate to close (decision is final)
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await self._notifier.notify()
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# Push BotInterruptionFrame to clear the pipeline
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await self.push_frame(BotInterruptionFrame(), FrameDirection.UPSTREAM)
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# Call developer callback if provided
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if self._on_voicemail_detected:
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try:
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await self._on_voicemail_detected(self)
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except Exception as e:
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logger.exception(f"{self}: Error in voicemail callback: {e}")
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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 VoicemailDetector(ParallelPipeline):
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"""Parallel pipeline for detecting voicemail vs. live conversation.
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Uses a parallel pipeline architecture with two branches:
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1. Conversation branch: Normal frame flow for live conversations
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2. Classification branch: LLM-based classification that can interrupt for voicemail
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The classifier runs in parallel and makes a one-time decision to either:
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- Continue normal conversation flow (NO response)
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- Interrupt and trigger voicemail handling (YES response)
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"""
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def __init__(
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self,
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*,
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llm: LLMService,
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on_voicemail_detected: Optional[Callable[[], Awaitable[None]]] = None,
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):
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"""Initialize the voicemail detector.
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Args:
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llm: LLM service for classification.
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on_voicemail_detected: Callback function called when voicemail is detected.
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"""
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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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|
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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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|
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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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|
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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 = ClassifierGate(self._conversation_notifier)
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self._voicemail_processor = VoicemailProcessor(
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self._conversation_notifier, on_voicemail_detected
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)
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super().__init__(
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# Conversation branch
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[],
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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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Block a user