improved wake word filter
This commit is contained in:
Kwindla Hultman Kramer
committed by
Aleix Conchillo Flaqué
Aleix Conchillo Flaqué
parent
d7bb0bc5cb
commit
2c32cc2f27
172
examples/foundational/14-wake-phrase.py
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172
examples/foundational/14-wake-phrase.py
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#
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# Copyright (c) 2024, 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 aiohttp
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import os
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import sys
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import re
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import time
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from enum import Enum
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from pipecat.frames.frames import (
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Frame, SystemFrame, TranscriptionFrame, TextFrame
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)
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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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 PipelineTask
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from pipecat.processors.aggregators.llm_response import (
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LLMAssistantResponseAggregator, LLMUserResponseAggregator)
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from pipecat.services.elevenlabs import ElevenLabsTTSService
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from pipecat.services.openai import OpenAILLMService
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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from pipecat.vad.silero import SileroVADAnalyzer
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from runner import configure
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from loguru import logger
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from dotenv import load_dotenv
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load_dotenv(override=True)
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logger.remove(0)
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logger.add(sys.stderr, level="DEBUG")
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class WakeCheckFilter(FrameProcessor):
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"""
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This filter looks for wake phrases in the transcription frames and only passes through frames
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after a wake phrase has been detected. It also has a keepalive timeout to allow for a brief
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period of continued conversation after a wake phrase has been detected.
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"""
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class WakeState(Enum):
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IDLE = 1
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AWAKE = 2
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class ParticipantState:
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def __init__(self, participant_id: str):
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self.participant_id = participant_id
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self.state = WakeCheckFilter.WakeState.IDLE
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self.wake_timer = 0
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self.accumulator = ""
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def __init__(self, wake_phrases: list[str], keepalive_timeout: float = 2):
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super().__init__()
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self._participant_states = {}
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self._keepalive_timeout = keepalive_timeout
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self._wake_patterns = []
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for name in wake_phrases:
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pattern = re.compile(r'\b' + r'\s*'.join(re.escape(word)
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for word in name.split()) + r'\b', re.IGNORECASE)
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self._wake_patterns.append(pattern)
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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try:
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if isinstance(frame, TranscriptionFrame):
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p = self._participant_states.get(frame.user_id)
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if p is None:
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p = WakeCheckFilter.ParticipantState(frame.user_id)
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self._participant_states[frame.user_id] = p
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# If we have been AWAKE within the last keepalive_timeout seconds, pass
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# the frame through
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if p.state == WakeCheckFilter.WakeState.AWAKE:
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if time.time() - p.wake_timer < self._keepalive_timeout:
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logger.debug(
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f"Wake phrase keepalive timeout has not expired. Passing frame through.")
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p.wake_timer = time.time()
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await self.push_frame(frame)
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return
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else:
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p.state = WakeCheckFilter.WakeState.IDLE
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p.accumulator += frame.text
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for pattern in self._wake_patterns:
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match = pattern.search(p.accumulator)
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if match:
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logger.debug(f"Wake phrase triggered: {match.group()}")
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# Found the wake word. Discard from the accumulator up to the start of the match
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# and modify the frame in place.
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p.state = WakeCheckFilter.WakeState.AWAKE
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p.wake_timer = time.time()
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frame.text = p.accumulator[match.start():]
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p.accumulator = ""
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await self.push_frame(frame)
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else:
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pass
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else:
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await self.push_frame(frame, direction)
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except Exception as e:
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logger.error(f"Error in wake word filter: {e}")
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async def main(room_url: str, token):
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async with aiohttp.ClientSession() as session:
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transport = DailyTransport(
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room_url,
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token,
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"Robot",
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DailyParams(
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audio_out_enabled=True,
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transcription_enabled=True,
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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer()
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)
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)
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tts = ElevenLabsTTSService(
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aiohttp_session=session,
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api_key=os.getenv("ELEVENLABS_API_KEY"),
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voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
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)
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llm = OpenAILLMService(
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api_key=os.getenv("OPENAI_API_KEY"),
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model="gpt-4o")
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messages = [
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{
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"role": "system",
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"content": "You are a helpful assistant. Respond to what the user said in a creative and helpful way. Keep your responses brief.",
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},
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]
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hey_robot_filter = WakeCheckFilter(["hey robot", "hey, robot"])
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tma_in = LLMUserResponseAggregator(messages)
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tma_out = LLMAssistantResponseAggregator(messages)
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pipeline = Pipeline([
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transport.input(), # Transport user input
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hey_robot_filter, # Filter out speech not directed at the robot
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tma_in, # 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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tma_out # Assistant spoken responses
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])
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task = PipelineTask(pipeline, allow_interruptions=True)
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@ transport.event_handler("on_first_participant_joined")
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async def on_first_participant_joined(transport, participant):
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transport.capture_participant_transcription(participant["id"])
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await tts.say("Hi! If you want to talk to me, just say 'Hey Robot'.")
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# Kick off the conversation.
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# messages.append(
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# {"role": "system", "content": "Please introduce yourself to the user."})
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# await task.queue_frames([LLMMessagesFrame(messages)])
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runner = PipelineRunner()
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await runner.run(task)
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if __name__ == "__main__":
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(url, token) = configure()
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asyncio.run(main(url, token))
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