pipecat/examples/voice/voice-google-gemini-tts.py

#
# Copyright (c) 2024-2026, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#


import os

from dotenv import load_dotenv
from loguru import logger

from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import LLMRunFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.worker import PipelineParams, PipelineWorker
from pipecat.processors.aggregators.llm_context import LLMContext
from pipecat.processors.aggregators.llm_response_universal import (
    LLMContextAggregatorPair,
    LLMUserAggregatorParams,
)
from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import create_transport
from pipecat.services.google.llm import GoogleLLMService
from pipecat.services.google.stt import GoogleSTTService
from pipecat.services.google.tts import GeminiTTSService
from pipecat.transcriptions.language import Language
from pipecat.transports.base_transport import BaseTransport, TransportParams
from pipecat.transports.daily.transport import DailyParams
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams

load_dotenv(override=True)

# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
transport_params = {
    "daily": lambda: DailyParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
    ),
    "twilio": lambda: FastAPIWebsocketParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
    ),
    "webrtc": lambda: TransportParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
    ),
}


async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
    logger.info(f"Starting bot with Gemini TTS")

    stt = GoogleSTTService(
        settings=GoogleSTTService.Settings(
            languages=[Language.EN_US],
        ),
        credentials=os.environ["GOOGLE_TEST_CREDENTIALS"],
    )

    tts = GeminiTTSService(
        credentials=os.environ["GOOGLE_TEST_CREDENTIALS"],
        settings=GeminiTTSService.Settings(
            model="gemini-2.5-flash-tts",
            voice="Charon",
            language=Language.EN_US,
            prompt="You are a helpful AI assistant. Speak in a natural, conversational tone.",
        ),
    )

    llm = GoogleLLMService(
        api_key=os.environ["GOOGLE_API_KEY"],
        model="gemini-2.5-flash",
        settings=GoogleLLMService.Settings(
            system_instruction="""You are a helpful assistant in a voice conversation.

            IMPORTANT: You're using Gemini TTS which supports expressive markup tags. You can use these tags in your responses:
            - [sigh] - Insert a sigh sound
            - [laughing] - Insert a laugh
            - [uhm] - Insert a hesitation sound
            - [whispering] - Speak the next part in a whisper
            - [shouting] - Speak the next part louder
            - [extremely fast] - Speak the next part very quickly
            - [short pause], [medium pause], [long pause] - Add pauses for dramatic effect

            Examples:
            - "Well [sigh] that's a tricky question."
            - "[laughing] That's a great joke!"
            - "[whispering] Let me tell you a secret."
            - "The answer is... [long pause] ...42!"

            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.""",
        ),
    )

    context = LLMContext()
    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
        context,
        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
    )

    pipeline = Pipeline(
        [
            transport.input(),  # Transport user input
            stt,  # STT
            user_aggregator,  # User responses
            llm,  # LLM
            tts,  # Gemini TTS
            transport.output(),  # Transport bot output
            assistant_aggregator,  # Assistant spoken responses
        ]
    )

    worker = PipelineWorker(
        pipeline,
        params=PipelineParams(
            enable_metrics=True,
            enable_usage_metrics=True,
        ),
        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
    )

    @transport.event_handler("on_client_connected")
    async def on_client_connected(transport, client):
        logger.info(f"Client connected")
        # Kick off the conversation
        context.add_message(
            {
                "role": "developer",
                "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.",
            }
        )
        await worker.queue_frames([LLMRunFrame()])

    @transport.event_handler("on_client_disconnected")
    async def on_client_disconnected(transport, client):
        logger.info(f"Client disconnected")
        await worker.cancel()

    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)

    await runner.add_workers(worker)
    await runner.run()


async def bot(runner_args: RunnerArguments):
    """Main bot entry point compatible with Pipecat Cloud."""
    transport = await create_transport(runner_args, transport_params)
    await run_bot(transport, runner_args)


if __name__ == "__main__":
    from pipecat.runner.run import main

    main()