pipecat/examples/features/features-switch-languages.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.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import Frame, LLMRunFrame
from pipecat.pipeline.parallel_pipeline import ParallelPipeline
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.processors.filters.function_filter import FunctionFilter
from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import create_transport
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.llm_service import FunctionCallParams
from pipecat.services.openai.llm import OpenAILLMService
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)


class SwitchLanguage(ParallelPipeline):
    def __init__(self):
        self._current_language = "English"

        english_tts = CartesiaTTSService(
            api_key=os.environ["CARTESIA_API_KEY"],
            settings=CartesiaTTSService.Settings(
                voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
            ),
        )

        spanish_tts = CartesiaTTSService(
            api_key=os.environ["CARTESIA_API_KEY"],
            settings=CartesiaTTSService.Settings(
                voice="d4db5fb9-f44b-4bd1-85fa-192e0f0d75f9",  # Spanish-speaking Lady
            ),
        )

        super().__init__(
            # English
            [FunctionFilter(self.english_filter), english_tts],
            # Spanish
            [FunctionFilter(self.spanish_filter), spanish_tts],
        )

    @property
    def current_language(self):
        return self._current_language

    async def switch_language(self, params: FunctionCallParams):
        self._current_language = params.arguments["language"]
        await params.result_callback(
            {"voice": f"Your answers from now on should be in {self.current_language}."}
        )

    async def english_filter(self, _: Frame) -> bool:
        return self.current_language == "English"

    async def spanish_filter(self, _: Frame) -> bool:
        return self.current_language == "Spanish"


# 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")

    stt = DeepgramSTTService(
        api_key=os.environ["DEEPGRAM_API_KEY"],
        settings=DeepgramSTTService.Settings(
            language="multi",
        ),
    )

    tts = SwitchLanguage()

    llm = OpenAILLMService(
        api_key=os.environ["OPENAI_API_KEY"],
        settings=OpenAILLMService.Settings(
            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way. You can speak the following languages: 'English' and 'Spanish'.",
        ),
    )
    llm.register_function("switch_language", tts.switch_language)

    switch_language_function = FunctionSchema(
        name="switch_language",
        description="Switch to another language when the user asks you to",
        properties={
            "language": {
                "type": "string",
                "description": "The language the user wants you to speak",
            },
        },
        required=["language"],
    )
    tools = ToolsSchema(standard_tools=[switch_language_function])
    context = LLMContext(tools=tools)
    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,  # TTS (bot will speak the chosen language)
            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": f"Please introduce yourself to the user and let them know the languages you speak. Your initial responses should be in {tts.current_language}.",
            }
        )
        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()