pipecat/examples/voice/voice-nvidia-sagemaker.py

#
# Copyright (c) 2024-2026, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
# For a full example of how to deploy to SageMaker, see:
# https://github.com/pipecat-ai/pipecat-examples/tree/main/nvidia_sagemaker_example/deployment/aws-sagemaker-nvidia


import datetime
import io
import os
import wave

import aiofiles
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.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_context import LLMContext
from pipecat.processors.aggregators.llm_response_universal import (
    LLMContextAggregatorPair,
    LLMUserAggregatorParams,
)
from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor
from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import create_transport
from pipecat.services.nvidia.llm import NvidiaLLMService
from pipecat.services.nvidia.sagemaker.stt import NvidiaSageMakerSTTService
from pipecat.services.nvidia.sagemaker.tts import NvidiaSageMakerTTSService
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)


async def save_audio_file(audio: bytes, filename: str, sample_rate: int, num_channels: int):
    """Save audio data to a WAV file."""
    if len(audio) > 0:
        with io.BytesIO() as buffer:
            with wave.open(buffer, "wb") as wf:
                wf.setsampwidth(2)
                wf.setnchannels(num_channels)
                wf.setframerate(sample_rate)
                wf.writeframes(audio)
            async with aiofiles.open(filename, "wb") as file:
                await file.write(buffer.getvalue())
        logger.info(f"Audio saved to {filename}")


# 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 = NvidiaSageMakerSTTService(
        endpoint_name=os.environ["SAGEMAKER_ASR_ENDPOINT_NAME"],
        region=os.getenv("AWS_REGION", "us-west-2"),
    )

    llm = NvidiaLLMService(
        api_key=os.environ["NVIDIA_API_KEY"],
        settings=NvidiaLLMService.Settings(
            model="meta/llama-3.3-70b-instruct",
            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.",
        ),
    )

    tts = NvidiaSageMakerTTSService(
        endpoint_name=os.environ["SAGEMAKER_MAGPIE_ENDPOINT_NAME"],
        region=os.getenv("AWS_REGION", "us-west-2"),
    )
    audiobuffer = AudioBufferProcessor()

    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,  # TTS
            transport.output(),  # Transport bot output
            audiobuffer,  # Audio buffer for recording
            assistant_aggregator,  # 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")
        # Start recording audio
        await audiobuffer.start_recording()
        # Kick off the conversation.
        context.add_message(
            {"role": "developer", "content": "Please introduce yourself to the user."}
        )
        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()

    # Handler for merged audio
    @audiobuffer.event_handler("on_audio_data")
    async def on_audio_data(buffer, audio, sample_rate, num_channels):
        timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
        filename = f"recordings/merged_{timestamp}.wav"
        os.makedirs("recordings", exist_ok=True)
        await save_audio_file(audio, filename, sample_rate, num_channels)

    # Handler for separate tracks
    @audiobuffer.event_handler("on_track_audio_data")
    async def on_track_audio_data(buffer, user_audio, bot_audio, sample_rate, num_channels):
        timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
        os.makedirs("recordings", exist_ok=True)

        user_filename = f"recordings/user_{timestamp}.wav"
        await save_audio_file(user_audio, user_filename, sample_rate, 1)

        bot_filename = f"recordings/bot_{timestamp}.wav"
        await save_audio_file(bot_audio, bot_filename, sample_rate, 1)

    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()