pipecat/examples/foundational/43a-heygen-video-service.py

#
# Copyright (c) 2024–2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#

import os

import aiohttp
from dotenv import load_dotenv
from loguru import logger

from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.audio.vad.vad_analyzer import VADParams
from pipecat.frames.frames import LLMRunFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_context import LLMContext
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
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.google.llm import GoogleLLMService
from pipecat.services.heygen.api import AvatarQuality, NewSessionRequest
from pipecat.services.heygen.video import HeyGenVideoService
from pipecat.transports.base_transport import BaseTransport, TransportParams
from pipecat.transports.daily.transport import DailyParams, DailyTransport

load_dotenv(override=True)

# We store functions so objects (e.g. SileroVADAnalyzer) don't get
# instantiated. The function will be called when the desired transport gets
# selected.
transport_params = {
    "daily": lambda: DailyParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
        video_out_enabled=True,
        video_out_is_live=True,
        video_out_width=1280,
        video_out_height=720,
        video_out_bitrate=2_000_000,  # 2MBps
        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
    ),
    "webrtc": lambda: TransportParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
        video_out_enabled=True,
        video_out_is_live=True,
        video_out_width=1280,
        video_out_height=720,
        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
    ),
}


async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
    logger.info(f"Starting bot")
    async with aiohttp.ClientSession() as session:
        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
            voice_id="00967b2f-88a6-4a31-8153-110a92134b9f",
        )

        llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"))

        heyGen = HeyGenVideoService(
            api_key=os.getenv("HEYGEN_API_KEY"),
            session=session,
            session_request=NewSessionRequest(
                avatar_id="Shawn_Therapist_public", version="v2", quality=AvatarQuality.high
            ),
        )

        messages = [
            {
                "role": "system",
                "content": "You are a helpful assistant. Your output will be converted to audio so don't include special characters in your answers. Be succinct and respond to what the user said in a creative and helpful way.",
            },
        ]

        context = LLMContext(messages)
        context_aggregator = LLMContextAggregatorPair(context)

        pipeline = Pipeline(
            [
                transport.input(),  # Transport user input
                stt,  # STT
                context_aggregator.user(),  # User responses
                llm,  # LLM
                tts,  # TTS
                heyGen,  # Avatar
                transport.output(),  # Transport bot output
                context_aggregator.assistant(),  # Assistant spoken responses
            ]
        )

        task = PipelineTask(
            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")
            # Updating publishing settings to enable adaptive bitrate
            if isinstance(transport, DailyTransport):
                await transport.update_publishing(
                    publishing_settings={
                        "camera": {
                            "sendSettings": {
                                "allowAdaptiveLayers": True,
                            }
                        }
                    }
                )

            # Kick off the conversation.
            messages.append(
                {
                    "role": "system",
                    "content": "Start by saying 'Hello' and then a short greeting.",
                }
            )
            await task.queue_frames([LLMRunFrame()])

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

        runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)

        await runner.run(task)


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()