voiceagent 0.0.3

A package to make it easy to interact with LLM's using voice

Voice Agent

A python package that makes it easy to interact with LLM's using voice

Setup

Install Port Audio on you OS

brew install portaudio #(MacOS)
apt install portaudio19-dev #(Debian/Ubuntu)

Then, setup your project, and install the python package

pip install voiceagent

Description

Voice Agent currently uses AssemblyAI for Speech to Text and ElevenLabs for Text to Speech

You can configure it to work with any LLM of your choice, for example OpenAI, or set it up to work with LangChain also. You are free to make the LLM setup as complex as you like, e.g adding Agents, RAG, Memory, or any other features you like

Voice Agent aims to make it easy to setup your LLM application the way you like, while also making it easy to get setup with voice

The current flow of requests looks like:

1. Microphone -> Speech To Text (using AssemblyAI)
2. LLM -> Can setup any code you like here to work with the text from the user query
3. Speaker -> Text To Speech (using ElevenLabs)

Getting setup should look as easy as:

from os import getenv
from voiceagent.voice_agent import VoiceAgent

voice_agent = VoiceAgent(
    assemblyai_api_key=getenv('ASSEMBLYAI_API_KEY'),
    elevenlabs_api_key=getenv('ELEVENLABS_API_KEY')
)

def on_message_callback(message):
    print(f"Your message from the microphone: {message}", end="\r\n")
    # add any application code you want here to handle the user request
    # e.g. send the message to the OpenAI Chat API
    return "{response from the LLM}"

voice_agent.on_message(on_message_callback)

print("------------------------------------")
print("Voice Agent started. Start chatting!")
print("------------------------------------")
voice_agent.start()

And that is the message returned from on_message_callback gets sent to the speakers. That means you can focus only on writing your application code, and not worry about speech-to-text and text-to-speech conversions from the microphone and speakers

Example

The below example shows how to setup OpenAI to work with Voice Agent, so that you can talk to ChatGPT from your microphone, and hear back it's response from the speakers.

For convenience, the example script also streams the chat and responses to the command line

from os import getenv
from voiceagent.voice_agent import VoiceAgent
from openai import OpenAI

voice_agent = VoiceAgent(
    assemblyai_api_key=getenv('ASSEMBLYAI_API_KEY'),
    elevenlabs_api_key=getenv('ELEVENLABS_API_KEY')
)

openai_client = OpenAI(
    api_key=getenv('OPENAI_API_KEY'),
)

green = "\033[0;32m"
white = "\033[0;39m"


def on_partial_message_callback(message):
    print(f"{green}You: {message}", end="\r")


def on_message_callback(message):
    print(f"{green}You: {message}", end="\r\n")
    chat_completion = openai_client.chat.completions.create(
        messages=[
            {
                "role": "user",
                "content": f"{message}. Keep responses no longer than 20 words",
            }
        ],
        model="gpt-3.5-turbo",
        stream=True
    )
    return chat_completion


def on_response_callback(response):
    print(f"{white}Assistant: {response}", end="\r\n")


voice_agent.on_partial_message(on_partial_message_callback)
voice_agent.on_message(on_message_callback)
voice_agent.on_response(on_response_callback)

print("------------------------------------")
print("Voice Agent started. Start chatting!")
print("------------------------------------")
voice_agent.start()

Event hooks

There are a few event hook you can use from when the user speaks into the microphone until the audio goes out on the speakers.

In order of the request flow, these event hooks are:

on_partial_message - This is called when the user is speaking into the microphone, and the speech-to-text conversion is still ongoing. For example the user is still speaking into the microphone, so the speech-to-text conversion is streaming in real time, but still not finalised. Once there is a pause in the user's speech, the message is then considered complete and the on_message event is called

on_message - This is called when the user has finished speaking into the microphone (user has paused speaking for more than 1.8 seconds), and the speech-to-text conversion is complete, and now ready to be sent to an LLM for processing

on_response - This is called when the LLM has processed the user's message. We can use this to access the response returned from the LLM

Future Work

Support more Speech-to-Text and Text-to-Speech libraries

Currently the setup is hardcoded to work with AssemblyAI and ElevenLabs. Would be nice to make it easy to swap these out for other libraries

Web application examples

Would be nice to setup some web application example that show how to use Voice Agent via a browser based application

Current Issues

Echo Issues

When I tested with certain headsets, or even used the computer speaker and microphones, the microphone would pick up the speaker's audio, resulting in ongoing loop of the LLM essentially chatting with itself. Using a headset where the microphone was a good distance away from the speaker resolved this

Latency Issues

Latency could definitely be better for a smoother conversation experience.