139 lines
4.4 KiB
Python
139 lines
4.4 KiB
Python
#
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# Copyright (c) 2024–2026, 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 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.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
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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 LLMRunFrame
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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.llm_context import LLMContext
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from pipecat.processors.aggregators.llm_response_universal import (
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LLMContextAggregatorPair,
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LLMUserAggregatorParams,
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)
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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.camb.tts import CambTTSService
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from pipecat.services.deepgram.stt import DeepgramSTTService
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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.daily.transport import DailyParams
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from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
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from pipecat.turns.user_stop import TurnAnalyzerUserTurnStopStrategy
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from pipecat.turns.user_turn_strategies import UserTurnStrategies
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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(params=VADParams(stop_secs=0.2)),
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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=VADParams(stop_secs=0.2)),
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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=VADParams(stop_secs=0.2)),
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),
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}
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async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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logger.info("Starting Camb AI TTS bot")
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stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
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tts = CambTTSService(
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api_key=os.getenv("CAMB_API_KEY"),
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model="mars-flash",
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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 voice assistant powered by Camb AI text-to-speech. "
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"Keep your responses concise and conversational since they will be spoken aloud. "
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"Avoid special characters, emojis, or bullet points.",
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},
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]
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context = LLMContext(messages)
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user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
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context,
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user_params=LLMUserAggregatorParams(
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user_turn_strategies=UserTurnStrategies(
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stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
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),
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),
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)
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pipeline = Pipeline(
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[
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transport.input(),
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stt,
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user_aggregator,
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llm,
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tts,
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transport.output(),
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assistant_aggregator,
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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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audio_out_sample_rate=22050,
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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("Client connected")
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messages.append({"role": "system", "content": "Please introduce yourself to the user."})
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await task.queue_frames([LLMRunFrame()])
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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("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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