Build a Voice Assistant with Agents Platform on a Raspberry Pi

Build a voice assistant with Agents Platform on a Raspberry Pi.

Tutorial · Assumes you have completed the ElevenAgents quickstart and have a Raspberry Pi with Python installed.

Introduction

In this tutorial you will learn how to build a voice assistant with Agents Platform running on a Raspberry Pi. Just like conventional home assistants like Alexa on Amazon Echo, Google Home, or Siri on Apple devices, your Eleven Voice assistant will listen to a hotword, in our case “Hey Eleven”, and then initiate an ElevenLabs Agents session to assist the user.

Requirements

  • A Raspberry Pi 5 or similar device.
  • A microphone and speaker.
  • Python 3.9 or higher installed on your machine.
  • An ElevenLabs account with an API key.

Setup

Install dependencies

On Debian-based systems you can install the dependencies with:

sudo apt-get update
sudo apt-get install libportaudio2 libportaudiocpp0 portaudio19-dev libasound-dev libsndfile1-dev -y

Create the project

On your Raspberry Pi, open the terminal and create a new directory for your project.

mkdir eleven-voice-assistant
cd eleven-voice-assistant

Create a new virtual environment and install the dependencies:

python -m venv .venv # Only required the first time you set up the project
source .venv/bin/activate

Install the dependencies:

pip install tflite-runtime
pip install librosa
pip install EfficientWord-Net
pip install elevenlabs
pip install "elevenlabs[pyaudio]"

Now create a new python file called hotword.py and add the following code:

hotword.py
import os
import signal
import time
from eff_word_net.streams import SimpleMicStream
from eff_word_net.engine import HotwordDetector
from eff_word_net.audio_processing import Resnet50_Arc_loss
# from eff_word_net import samples_loc
from elevenlabs.client import ElevenLabs
from elevenlabs.conversational_ai.conversation import Conversation, ConversationInitiationData
from elevenlabs.conversational_ai.default_audio_interface import DefaultAudioInterface
convai_active = False
elevenlabs = ElevenLabs()
agent_id = os.getenv("ELEVENLABS_AGENT_ID")
api_key = os.getenv("ELEVENLABS_API_KEY")
dynamic_vars = {
'user_name': 'Thor',
'greeting': 'Hey'
}
config = ConversationInitiationData(
dynamic_variables=dynamic_vars
)
base_model = Resnet50_Arc_loss()
eleven_hw = HotwordDetector(
hotword="hey_eleven",
model = base_model,
reference_file=os.path.join("hotword_refs", "hey_eleven_ref.json"),
threshold=0.7,
relaxation_time=2
)
def create_conversation():
"""Create a new conversation instance"""
return Conversation(
# API client and agent ID.
elevenlabs,
agent_id,
config=config,
# Assume auth is required when API_KEY is set.
requires_auth=bool(api_key),
# Use the default audio interface.
audio_interface=DefaultAudioInterface(),
# Simple callbacks that print the conversation to the console.
callback_agent_response=lambda response: print(f"Agent: {response}"),
callback_agent_response_correction=lambda original, corrected: print(f"Agent: {original} -> {corrected}"),
callback_user_transcript=lambda transcript: print(f"User: {transcript}"),
# Uncomment if you want to see latency measurements.
# callback_latency_measurement=lambda latency: print(f"Latency: {latency}ms"),
)
def start_mic_stream():
"""Start or restart the microphone stream"""
global mic_stream
try:
# Always create a new stream instance
mic_stream = SimpleMicStream(
window_length_secs=1.5,
sliding_window_secs=0.75,
)
mic_stream.start_stream()
print("Microphone stream started")
except Exception as e:
print(f"Error starting microphone stream: {e}")
mic_stream = None
time.sleep(1) # Wait a bit before retrying
def stop_mic_stream():
"""Stop the microphone stream safely"""
global mic_stream
try:
if mic_stream:
# SimpleMicStream doesn't have a stop_stream method
# We'll just set it to None and recreate it next time
mic_stream = None
print("Microphone stream stopped")
except Exception as e:
print(f"Error stopping microphone stream: {e}")
# Initialize microphone stream
mic_stream = None
start_mic_stream()
print("Say Hey Eleven ")
while True:
if not convai_active:
try:
# Make sure we have a valid mic stream
if mic_stream is None:
start_mic_stream()
continue
frame = mic_stream.getFrame()
result = eleven_hw.scoreFrame(frame)
if result == None:
#no voice activity
continue
if result["match"]:
print("Wakeword uttered", result["confidence"])
# Stop the microphone stream to avoid conflicts
stop_mic_stream()
# Start ConvAI Session
print("Start ConvAI Session")
convai_active = True
try:
# Create a new conversation instance
conversation = create_conversation()
# Start the session
conversation.start_session()
# Set up signal handler for graceful shutdown
def signal_handler(sig, frame):
print("Received interrupt signal, ending session...")
try:
conversation.end_session()
except Exception as e:
print(f"Error ending session: {e}")
signal.signal(signal.SIGINT, signal_handler)
# Wait for session to end
conversation_id = conversation.wait_for_session_end()
print(f"Conversation ID: {conversation_id}")
except Exception as e:
print(f"Error during conversation: {e}")
finally:
# Cleanup
convai_active = False
print("Conversation ended, cleaning up...")
# Give some time for cleanup
time.sleep(1)
# Restart microphone stream
start_mic_stream()
print("Ready for next wake word...")
except Exception as e:
print(f"Error in wake word detection: {e}")
# Try to restart microphone stream if there's an error
mic_stream = None
time.sleep(1)
start_mic_stream()

Agent configuration

1

Sign in to ElevenLabs

Go to elevenlabs.io and sign in to your account.

2

Create a new agent

Navigate to Agents Platform > Agents and create a new agent from the blank template.

3

Set the first message

Set the first message and specify the dynamic variable for the platform.

{{greeting}} {{user_name}}, Eleven here, what's up?
4

Set the system prompt

Set the system prompt. You can find our best practises docs here.

You are a helpful Agents Platform assistant with access to a weather tool. When users ask about
weather conditions, use the get_weather tool to fetch accurate, real-time data. The tool requires
a latitude and longitude - use your geographic knowledge to convert location names to coordinates
accurately.
Never ask users for coordinates - you must determine these yourself. Always report weather
information conversationally, referring to locations by name only. For weather requests:
1. Extract the location from the user's message
2. Convert the location to coordinates and call get_weather
3. Present the information naturally and helpfully
For non-weather queries, provide friendly assistance within your knowledge boundaries. Always be
concise, accurate, and helpful.
5

Set up a webhook tool

We’ll set up a simple webhook tool that will fetch the weather data for us. Follow the setup steps here to set up the tool.

Run the app

To run the app, first set the required environment variables:

export ELEVENLABS_API_KEY=YOUR_API_KEY
export ELEVENLABS_AGENT_ID=YOUR_AGENT_ID

Then simply run the following command:

python hotword.py

Now say “Hey Eleven” to start the conversation. Happy chattin’!

[Optional] Train your custom hotword

Generate training audio

To generate the hotword embeddings, you can use ElevenLabs to generate four training samples. Simply navigate to Text To Speech within your ElevenLabs app, and type in your hotword, e.g. “Hey Eleven”. Select a voice and click on the “Generate” button.

After the audio has been generated, download the audio file and save them into a folder called hotword_training_audio at the root of your project. Repeat this process three more times with different voices.

Train the hotword

In your terminal, with your virtual environment activated, run the following command to train the hotword:

python -m eff_word_net.generate_reference --input-dir hotword_training_audio --output-dir hotword_refs --wakeword hey_eleven --model-type resnet_50_arc

This will generate the hey_eleven_ref.json file in the hotword_refs folder. Now you simply need to update the reference_file parameter in the HotwordDetector class in hotword.py to point to the new reference file and you’re good to go!

Next steps