157 lines
5.3 KiB
Python
157 lines
5.3 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.vad.silero import SileroVADAnalyzer
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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.google.llm import GoogleLLMService
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from pipecat.services.google.stt import GoogleSTTService
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from pipecat.services.google.tts import GeminiTTSService
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from pipecat.transcriptions.language import Language
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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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load_dotenv(override=True)
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# We use lambdas to defer transport parameter creation until the transport
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# type is selected at runtime.
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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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),
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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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),
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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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),
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}
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async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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logger.info(f"Starting bot with Gemini TTS")
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stt = GoogleSTTService(
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settings=GoogleSTTService.Settings(
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languages=[Language.EN_US],
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),
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credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
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)
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tts = GeminiTTSService(
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credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
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settings=GeminiTTSService.Settings(
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model="gemini-2.5-flash-tts",
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voice="Charon",
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language=Language.EN_US,
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prompt="You are a helpful AI assistant. Speak in a natural, conversational tone.",
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),
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)
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llm = GoogleLLMService(
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api_key=os.getenv("GOOGLE_API_KEY"),
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model="gemini-2.5-flash",
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settings=GoogleLLMService.Settings(
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system_instruction="""You are a helpful assistant in a voice conversation.
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IMPORTANT: You're using Gemini TTS which supports expressive markup tags. You can use these tags in your responses:
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- [sigh] - Insert a sigh sound
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- [laughing] - Insert a laugh
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- [uhm] - Insert a hesitation sound
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- [whispering] - Speak the next part in a whisper
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- [shouting] - Speak the next part louder
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- [extremely fast] - Speak the next part very quickly
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- [short pause], [medium pause], [long pause] - Add pauses for dramatic effect
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Examples:
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- "Well [sigh] that's a tricky question."
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- "[laughing] That's a great joke!"
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- "[whispering] Let me tell you a secret."
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- "The answer is... [long pause] ...42!"
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Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Keep responses concise. 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 = LLMContext()
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user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
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context,
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user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
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)
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pipeline = Pipeline(
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[
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transport.input(), # Transport user input
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stt, # STT
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user_aggregator, # User responses
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llm, # LLM
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tts, # Gemini TTS
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transport.output(), # Transport bot output
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assistant_aggregator, # 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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context.add_message(
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{
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"role": "developer",
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"content": "You are an AI assistant. You can help with a variety of tasks. Introduce yourself and ask the user what they would like to know.",
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}
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)
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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(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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