Real-time Voice Agent

[cerebrium.deployment]
name = "llama-llm"
python_version = "3.11"
docker_base_image_url = "debian:bookworm-slim"
include = ["./*", "main.py", "cerebrium.toml"]
exclude = [".*"]

[cerebrium.hardware]
cpu = 4
memory = 12.0
compute = "ADA_L40"

[cerebrium.scaling]
min_replicas = 1
max_replicas = 5
cooldown = 60

[cerebrium.dependencies.pip]
vllm = "latest"
pydantic = "latest"

import os
import time
import json

from huggingface_hub import login
from pydantic import BaseModel
from vllm import SamplingParams, AsyncLLMEngine
from vllm.engine.arg_utils import AsyncEngineArgs

login(token=os.environ.get("HF_TOKEN"))

engine_args = AsyncEngineArgs(
    model="RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
    gpu_memory_utilization=0.9,
    max_model_len=8192,
)
engine = AsyncLLMEngine.from_engine_args(engine_args)


class Message(BaseModel):
    role: str
    content: str


def format_chat_prompt(messages: list) -> str:
    formatted_messages = []
    for msg in messages:
        msg_obj = Message(**msg)
        formatted_messages.append(
            f"<|start_header_id|>{msg_obj.role}<|end_header_id|>\n{msg_obj.content}<|eot_id|>"
        )
    return "<|begin_of_text|>" + "".join(formatted_messages) + "<|start_header_id|>assistant<|end_header_id|>"


async def run(
    messages: list,
    model: str,
    run_id: str,
    stream: bool = True,
    temperature: float = 0.8,
    top_p: float = 0.95,
    max_tokens: int = 256,
    stream_options: dict = None,
):
    # Format your prompt for llama-friendly usage:
    prompt = format_chat_prompt(messages)

    sampling_params = SamplingParams(temperature=temperature, top_p=top_p, max_tokens=max_tokens)
    results_generator = engine.generate(prompt, sampling_params, run_id)

    previous_text = ""
    first_chunk = True

    async for output in results_generator:
        prompt_output = output.outputs
        new_text = prompt_output[0].text[len(previous_text) :]
        previous_text = prompt_output[0].text

        # Construct OpenAI-compatible chunk
        chunk = {
            "id": run_id,
            "object": "chat.completion.chunk",
            "created": int(time.time()),
            "model": model,
            "choices": [
                {
                    "index": 0,
                    "delta": {},
                    "finish_reason": None,
                }
            ],
        }

        # Include the role in the first chunk
        if first_chunk:
            chunk["choices"][0]["delta"]["role"] = "assistant"
            first_chunk = False

        # Add new text to the delta if any
        if new_text:
            chunk["choices"][0]["delta"]["content"] = new_text

        # Capture a finish reason if it's provided
        finish_reason = prompt_output[0].finish_reason or None
        if finish_reason and finish_reason != "none":
            chunk["choices"][0]["finish_reason"] = finish_reason

        yield f"data: {json.dumps(chunk)}\n\n"

    # Send the final [DONE] message
    yield "data: [DONE]\n\n"

[cerebrium.deployment]
# This file was automatically generated by Cerebrium as a starting point for your project.
# You can edit it as you wish.
# If you would like to learn more about your Cerebrium config, please visit https://docs.cerebrium.ai/environments/config-files#config-file-example

[cerebrium.deployment]
name = "pipecat-agent"
python_version = "3.11"
include = ["./*", "main.py", "cerebrium.toml"]
exclude = ["./example_exclude"]

[cerebrium.hardware]
compute = "CPU"
cpu = 6
memory = 12.0

[cerebrium.scaling]
min_replicas = 1 # Note: This incurs a constant cost since at least one instance is always running.
max_replicas = 2
cooldown = 180

[cerebrium.dependencies.pip]
torch = ">=2.0.0"
"pipecat-ai[silero, daily, openai, deepgram, cartesia]" = "==0.0.67"
aiohttp = ">=3.9.4"
torchaudio = ">=2.3.0"
channels = ">=4.0.0"
requests = "==2.32.2"
dotenv = "latest"

import asyncio
import os
import subprocess
import sys
import time
from multiprocessing import Process

import aiohttp
import requests
from loguru import logger
from pipecat.frames.frames import LLMMessagesFrame, EndFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner

from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_response import (
    LLMAssistantResponseAggregator,
    LLMUserResponseAggregator,
)
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.cartesia.tts import CartesiaTTSService
from deepgram import LiveOptions
from pipecat.services.rime.tts import RimeHttpTTSService, RimeTTSService, Language
from pipecat.services.openai import OpenAILLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.audio.vad.vad_analyzer import VADParams

from helpers import (
    CustomDeepgramSTTService,
)

from dotenv import load_dotenv

load_dotenv()

logger.remove(0)
logger.add(sys.stderr, level="DEBUG")

deepgram_voice: str = "aura-asteria-en"

async def main(room_url: str, token: str):
    async with aiohttp.ClientSession() as session:
        transport = DailyTransport(
            room_url,
            token,
            "Respond bot",
            DailyParams(
                audio_out_enabled=True,
                audio_in_enabled=True,
                transcription_enabled=False,
                vad_enabled=True,
                vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.15)),
                vad_audio_passthrough=True,
            ),
        )

        stt = CustomDeepgramSTTService(
            api_key=os.environ.get("DEEPGRAM_API_KEY"),
            websocket_url="ws://api.aws/v4/p-xxxxxxxx/deepgram/v1/listen",
            live_options=LiveOptions(
                model="nova-2-general",
                language="en-US",
                smart_format=True,
                vad_events=True
            )
        )

        tts = CartesiaTTSService(
            api_key=os.environ.get("CARTESIA_API_KEY"),
            voice_id='97f4b8fb-f2fe-444b-bb9a-c109783a857a',

        )

        llm = OpenAILLMService(
            name="LLM",
            model="RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
            base_url="http://api.aws/v4/p-xxxxxxxx/llama-llm/run"
        )

        messages = [
            {
                "role": "system",
                "content": "You are a fast, low-latency chatbot. Your goal is to demonstrate voice-driven AI capabilities at human-like speeds. The technology powering you is Daily for transport, Cerebrium for serverless infrastructure, Llama 3 (8-B version) LLM, and Deepgram for speech-to-text and text-to-speech. You are hosted on the east coast of the United States. Respond to what the user said in a creative and helpful way, but keep responses short and legible. Ensure responses contain only words. Check again that you have not included special characters other than '?' or '!'.",
            },
        ]

        tma_in = LLMUserResponseAggregator(messages)
        tma_out = LLMAssistantResponseAggregator(messages)

        pipeline = Pipeline(
            [
                transport.input(),  # Transport user input
                stt,  # Speech-to-text
                tma_in,  # User responses
                llm,  # LLM
                tts,  # TTS
                transport.output(),  # Transport bot output
                tma_out,  # Assistant spoken responses
            ]
        )

        task = PipelineTask(
            pipeline,
            params=PipelineParams(
                allow_interruptions=True,
                enable_metrics=True
            ),
        )

        # When the first participant joins, the bot should introduce itself.
        @transport.event_handler("on_first_participant_joined")
        async def on_first_participant_joined(transport, participant):
            # Kick off the conversation.
            time.sleep(1.5)
            messages.append(
                {
                    "role": "system",
                    "content": "Introduce yourself by saying 'hello, I'm FastBot, how can I help you today?'",
                }
            )
            await task.queue_frame(LLMMessagesFrame(messages))

        # When the participant leaves, we exit the bot.
        @transport.event_handler("on_participant_left")
        async def on_participant_left(transport, participant, reason):
            await task.queue_frame(EndFrame())

        # If the call is ended make sure we quit as well.
        @transport.event_handler("on_call_state_updated")
        async def on_call_state_updated(transport, state):
            if state == "left":
                await task.queue_frame(EndFrame())

        runner = PipelineRunner()

        await runner.run(task)
        await session.close()


async def start_bot(room_url: str, token: str = None):

    try:
        await main(room_url, token)
    except Exception as e:
        logger.error(f"Exception in main: {e}")
        sys.exit(1)  # Exit with a non-zero status code

    return {"message": "session finished"}


def create_room():
    url = "https://api.daily.co/v1/rooms/"
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {os.environ.get('DAILY_TOKEN')}",
    }
    data = {
        "properties": {
            "exp": int(time.time()) + 60 * 5,  ##5 mins
            "eject_at_room_exp": True,
        }
    }

    response = requests.post(url, headers=headers, json=data)
    if response.status_code == 200:
        room_info = response.json()
        token = create_token(room_info["name"])
        if token and "token" in token:
            room_info["token"] = token["token"]
        else:
            logger.error("Failed to create token")
            return {
                "message": "There was an error creating your room",
                "status_code": 500,
            }
        return room_info
    else:
        data = response.json()
        if data.get("error") == "invalid-request-error" and "rooms reached" in data.get(
            "info", ""
        ):
            logger.error(
                "We are currently at capacity for this demo. Please try again later."
            )
            return {
                "message": "We are currently at capacity for this demo. Please try again later.",
                "status_code": 429,
            }
        logger.error(f"Failed to create room: {response.status_code}")
        return {"message": "There was an error creating your room", "status_code": 500}


def create_token(room_name: str):
    url = "https://api.daily.co/v1/meeting-tokens"
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {os.environ.get('DAILY_TOKEN')}",
    }
    data = {"properties": {"room_name": room_name, "is_owner": True}}

    response = requests.post(url, headers=headers, json=data)
    if response.status_code == 200:
        token_info = response.json()
        return token_info
    else:
        logger.error(f"Failed to create token: {response.status_code}")
        return None


# if __name__ == "__main__":
#     room = create_room()
#     if room and "token" in room:
#         asyncio.run(main(room["url"], room["token"]))
#     else:
#         logger.error("Failed to create room")

DAILY_TOKEN=
HF_TOKEN=
DEEPGRAM_API_KEY=
CARTESIA_API_KEY=

VITE_SERVER_URL=https://api.aws.us-east-1.cerebrium.ai/v4/p-xxxxx/<APP_NAME> #This is the base url of your pipecat-agent. Do not include the function names
VITE_SERVER_AUTH= #This is the JWT token you can get from the API Keys section of your Cerebrium Dashboard.