208 lines
7.0 KiB
Python
208 lines
7.0 KiB
Python
#
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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 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.audio.vad.vad_analyzer import VADParams
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from pipecat.frames.frames import (
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BotStartedSpeakingFrame,
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Frame,
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LLMFullResponseStartFrame,
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LLMTextFrame,
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TranscriptionFrame,
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TTSSpeakFrame,
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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.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.deepgram.stt import DeepgramSTTService
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from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
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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.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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# Create VAD parameters optimized for quiet speakers
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quiet_speaker_vad_params = VADParams(
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confidence=0.4, # Lower confidence threshold (default: 0.7)
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min_volume=0.3, # Lower volume threshold (default: 0.6)
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start_secs=0.1, # Faster response to speech start (default: 0.2)
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stop_secs=1.0, # Longer wait before stopping (default: 0.8)
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)
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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=quiet_speaker_vad_params),
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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(params=quiet_speaker_vad_params),
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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(params=quiet_speaker_vad_params),
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),
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}
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class TranscriptionLogger(FrameProcessor):
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"""Custom processor that logs transcription frames."""
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async def process_frame(self, frame, direction):
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await super().process_frame(frame, direction)
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# Only log TranscriptionFrame objects
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if isinstance(frame, TranscriptionFrame):
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logger.info(f"[TRANSCRIPTION]: {frame.text}")
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# Always pass the frame through to maintain pipeline flow
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await self.push_frame(frame, direction)
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class InterventionProcessor(FrameProcessor):
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"""Custom processor that logs LLM response frames."""
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def __init__(self):
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super().__init__()
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self._timer_task = None
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async def process_frame(self, frame, direction):
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await super().process_frame(frame, direction)
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# Log LLM response start frames
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if isinstance(frame, LLMFullResponseStartFrame):
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logger.info(f"[LLM_START]: Starting LLM response")
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# Cancel any existing timer
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if self._timer_task and not self._timer_task.done():
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self._timer_task.cancel()
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# Start a new 500ms timer
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self._timer_task = asyncio.create_task(self._log_after_delay())
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# Cancel timer if bot started speaking before 500ms
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elif isinstance(frame, BotStartedSpeakingFrame):
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logger.info(f"[BOT_SPEAKING]: Bot started speaking, canceling intervention timer")
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if self._timer_task and not self._timer_task.done():
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self._timer_task.cancel()
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# Log LLM text frames
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elif isinstance(frame, LLMTextFrame):
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logger.info(f"[LLM_TEXT]: {frame.text}")
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# Always pass the frame through to maintain pipeline flow
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await self.push_frame(frame, direction)
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async def _log_after_delay(self):
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"""Log a message after 500ms delay."""
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try:
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await asyncio.sleep(0.5) # 500ms
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logger.info(f"500ms passed since LLMFullResponseStartFrame")
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await self.queue_frame(TTSSpeakFrame("um..."))
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except asyncio.CancelledError:
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# Timer was cancelled, which is fine
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pass
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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 = ElevenLabsTTSService(
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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(api_key=os.getenv("OPENAI_API_KEY"))
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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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# Create transcription logger instance
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transcription_logger = TranscriptionLogger()
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# Create LLM logger instance
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intervention = InterventionProcessor()
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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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transcription_logger, # Log transcription frames
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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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intervention, # Log LLM response frames
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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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