EVI Python Quickstart

In this guide, you’ll learn how to use Hume’s Python SDK to integrate with EVI, for applications like command-line or desktop applications.

1. Environment setup: Download package and system dependencies to run EVI.
2. Import statements and helpers: Import needed symbols and define helper functions.
3. Authentication: Use your API credentials to authenticate your EVI application.
4. Connection: Set up a secure WebSocket connection to interact with EVI.
5. Handling incoming messages: Process incoming chat events.
6. Audio input: Capture audio data from an input device and send to EVI.

Looking for sample code?

See the complete implementation of this guide on GitHub

Python SDK

Explore or contribute to Hume’s Python SDK on GitHub

Environment setup

This guide uses the Hume SDK Python package hume with the [microphone] package extra. You can install this with uv (recommended), poetry, or pip. It also uses the python-dotenv package for loading environment variables from an .env file.

uv init
uv add hume[microphone] python-dotenv

System dependencies

The Hume Python SDK uses the sounddevice library for audio recording and playback, which relies on the PortAudio C Library to be installed on your system. On macOS and Windows, PortAudio is typically included with the sounddevice package, so no additional installation is required. However, on Linux, you will need to manually install PortAudio correctly for your distribution.

sudo apt-get --yes update
sudo apt-get --yes install libasound2-dev libportaudio2

Import statements and helpers

First, we import needed symbols from the Python standard library and the Hume SDK, and define some helpers that are useful for printing readable output to the terminal.

import asyncio
import datetime
import os
from dotenv import load_dotenv
from hume import MicrophoneInterface, Stream
from hume.client import AsyncHumeClient
from hume.empathic_voice.types import SubscribeEvent
def extract_top_n_emotions(emotion_scores: dict, n: int) -> dict:
    sorted_emotions = sorted(emotion_scores.items(), key=lambda item: item[1], reverse=True)
    top_n_emotions = {emotion: score for emotion, score in sorted_emotions[:n]}
    return top_n_emotions
def print_emotions(emotion_scores: dict) -> None:
    print(' | '.join([f"{emotion} ({score:.2f})" for emotion, score in emotion_scores.items()]))
def log(text: str) -> None:
    now = datetime.datetime.now(tz=datetime.timezone.utc).strftime("%H:%M:%S")
    print(f"[{now}] {text}")

Authentication

Log into your Hume AI Account and obtain an API key. Store it as HUME_API_KEY inside your project’s .env file.

Read HUME_API_KEY and use it to instantiate the AsyncHumeClient class. This is the main entry point provided by the Hume Python SDK.

You can specify EVI’s voice and behavior for a chat by Creating a Configuration through the API or the Hume platform web interface. Set HUME_CONFIG_ID in .env or as an environment variable and read it.

async def main() -> None:
    load_dotenv()
    HUME_API_KEY = os.getenv("HUME_API_KEY")
    HUME_CONFIG_ID = os.getenv("HUME_CONFIG_ID")
    client = AsyncHumeClient(api_key=HUME_API_KEY)
    ...

Connection

To connect to an EVI chat, use client.empathic_voice.chat.connect(...) with connection arguments passed directly, such as config_id=HUME_CONFIG_ID. EVI chats are event-based, so iterate over incoming events from the chat socket and handle each message in your loop (or delegate to a helper function such as on_message).

async def main() -> None:
    ...
    async def on_message(message: SubscribeEvent):
        # (Completed in later steps)
        ...
    async with client.empathic_voice.chat.connect(
        config_id=HUME_CONFIG_ID
    ) as socket:
        print("WebSocket connection opened.")
        async for message in socket:
            await on_message(message)

Handling incoming messages

After you successfully connect to an EVI chat, iterate over messages from the socket and pass each one to your on_message handler. These are described by the Hume SDK’s SubscribeEvent type.

In this quickstart, the message handler logs chat_metadata, logs final user_message content and top emotions, and raises an exception if an error event is received. Assistant output and other event types are ignored in this handler.

async def main() -> None:
    ...
    async def on_message(message: SubscribeEvent):
        if message.type == "chat_metadata":
            log(
                f"<{message.type}> Chat ID: {message.chat_id}, Chat Group ID: {message.chat_group_id}"
            )
        elif message.type == "user_message":
            if getattr(message, "interim", False):
                return
            log(f"{message.message.role}: {message.message.content}")
            print_emotions(
                extract_top_n_emotions(dict(message.models.prosody and message.models.prosody.scores or {}), 3)
            )
        elif message.type == "error":
            raise RuntimeError(
                f"Received error message from Hume websocket ({message.code}): {message.message}"
            )
        else:
            # Ignore assistant output and any other message types.
            return
    ...

Audio input

The Hume SDK provides MicrophoneInterface.start(...), which handles microphone capture and streaming recorded audio through the WebSocket to EVI. Stream.new() is initialized and passed as byte_stream to MicrophoneInterface.start(...). This quickstart focuses on mic input and message handling; it does not include manual audio_output playback queueing.

Pass the chat socket provided by connect(...) in order to use MicrophoneInterface.start:

async def main() -> None:
    ...
    stream = Stream.new()
    ...
    async with client.empathic_voice.chat.connect(
        config_id=HUME_CONFIG_ID
    ) as socket:
        await MicrophoneInterface.start(
            socket,
            allow_user_interrupt=False,
            byte_stream=stream
        )

Specify a microphone device

MicrophoneInterface.start will attempt to use the system’s default audio input device. To specify a specific audio input device, you can pass it via the optional device parameter in MicrophoneInterface.start.

To view a list of available audio devices, run the following command:

python -c "import sounddevice; print(sounddevice.query_devices())"
# outputs something like
  0 DELL U2720QM, Core Audio (0 in, 2 out)
  1 I, Phone 15 Pro Max Microphone, Core Audio (1 in, 0 out)
>  2 Studio Display Microphone, Core Audio (1 in, 0 out)
  3 Studio Display Speakers, Core Audio (0 in, 8 out)
  4 MacBook Pro Microphone, Core Audio (1 in, 0 out)
<  5 MacBook Pro Speakers, Core Audio (0 in, 2 out)
  6 Pro Tools Audio Bridge 16, Core Audio (16 in, 16 out)
  7 Pro Tools Audio Bridge 2-A, Core Audio (2 in, 2 out)
  8 Pro Tools Audio Bridge 2-B, Core Audio (2 in, 2 out)
  9 Pro Tools Audio Bridge 32, Core Audio (32 in, 32 out)
  10 Pro Tools Audio Bridge 64, Core Audio (64 in, 64 out)
  11 Pro Tools Audio Bridge 6, Core Audio (6 in, 6 out)
  12 Apowersoft Audio Device, Core Audio (2 in, 2 out)
  13 ZoomAudioDevice, Core Audio (2 in, 2 out)

If the MacBook Pro Microphone is the desired device, specify device 4 in the Microphone context. For example:

# Specify device 4 in MicrophoneInterface 
await MicrophoneInterface.start(
  socket,
  device=4,
  allow_user_interrupt=False,
  byte_stream=stream
)

For troubleshooting faulty device detection - particularly with systems using ALSA, the Advanced Linux Sound Architecture, the device may also be directly specified using the sounddevice library:

# Directly import the sounddevice library
import sounddevice as sd
# Set the default device prior to scheduling audio input task
sd.default.device = 4

Interruption

The allow_user_interrupt parameter on MicrophoneInterface.start(...) controls whether the user can send a message while the assistant is speaking:

# Specify allowing interruption
await MicrophoneInterface.start(
  socket,
  allow_user_interrupt=True,
  byte_stream=stream
)

• allow_user_interrupt=True: Allows the user to send microphone input even when the assistant is speaking. This enables more fluid, overlapping conversation.
• allow_user_interrupt=False: Prevents the user from sending microphone input while the assistant is speaking, ensuring that the user does not interrupt the assistant. This is useful in scenarios where clear, uninterrupted communication is important.

Put it all together

Finally, add the following code at the end of your script to run the main function:

if __name__ == "__main__":
    asyncio.run(main())

View the complete quickstart.py code on GitHub

Next steps

Congratulations! You’ve successfully implemented a real-time conversational application using Hume’s Empathic Voice Interface (EVI).

Next, consider exploring these areas to enhance your EVI application: