If you’re interested in developing a real-time voice chat application that utilizes large language models (LLMs), you’re in the right place! In this article, we’ll take a look at QuiLLMan, a versatile chat app that transcribes audio, communicates with language models, and converts responses into natural-sounding speech.

What is QuiLLMan?

QuiLLMan is a complete chat application that employs OpenAI’s Whisper to transcribe audio, taps into the Llama 3.1 Instruct model to generate responses, and uses Coqui’s XTTS to bring those responses to life in a voice-to-voice conversation. Think of it as a sophisticated virtual chat partner that listens, understands, and speaks back to you!

File Structure Overview

• React frontend ([src/frontend](.src/frontend))
• FastAPI server ([src/app.py](.src/app.py))
• Whisper transcription module ([src/whisper.py](.src/whisper.py))
• XTTS text-to-speech module ([src/xtts.py](.src/xtts.py))
• LLaMA 3.1 text generation module ([src/llama.py](.src/llama.py))

Setting Up Your Development Environment

Before jumping into coding, ensure you have everything ready!

Requirements

• Install Modal in your current Python virtual environment:

pip install modal

• Create a Modal account.
• Set up your Modal token in your environment:

modal token new

Testing the Inference Modules

Each component like Whisper, XTTS, and Llama can be tested individually to ensure functionality before integrating everything together.

For instance, to test the Whisper transcription module, simply execute the following command:

modal run -q src.whisper

Setting Up the HTTP Server and Frontend

The FastAPI application located in src/app.py orchestrates the different inference modules into a seamless pipeline. You can initiate a development server with:

modal serve src.app

After running the command, you’ll receive a URL in your terminal output to access your application. As you develop, updates to files will be automatically deployed, making it easy to see your progress. To stop the app, simply press Ctrl+C. However, you might want to clear your browser cache to see frontend changes.

Deploying Your Application to Modal

Once you’re satisfied with your application’s functionality, it’s time to deploy it. Execute the following command:

modal deploy src.app

Don’t worry about costs since Modal apps are serverless and scale to 0 when not in use!

Troubleshooting

Like any project, you may encounter issues while developing. Here are some common troubleshooting tips:

• Ensure that all Python dependencies are correctly installed.
• Check that your **[Whisper V3](https://huggingface.co/openai/whisper-large-v3)** and **[Llama 3.1 8B Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)** models are compatible with your setup.
• If getting errors, try restarting your terminal and re-running the commands.
• For frontend-related issues, clearing the browser cache is a good first step.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

With QuiLLMan, you’ve got a robust starting point for building your voice-chatting applications. By transcribing audio, generating responses, and synthesizing speech, you can create engaging and interactive experiences.