We're living in an era where AI has lowered the barriers to building sophisticated applications. What once required weeks of development can now be accomplished in minutes. We recently came across an interesting project in Shubhamsaboo's repos, awesome-llm-apps. The GitHub repo collects a lot of creative LLM applications that showcase practical AI implementations. Thanks to Shubham and the open-source community for making these valuable resources freely available. In this article we will show you how we created a working demo in just 10 minutes.
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We're living in an era where AI has lowered the barriers to building sophisticated applications. What once required weeks of development can now be accomplished in minutes. We recently came across an interesting project in Shubhamsaboo's repos, awesome-llm-apps. The GitHub repo collects a lot of creative LLM applications that showcase practical AI implementations.
Thanks to Shubham and the open-source community for making these valuable resources freely available. In this article we will show you how we created a working demo in just 10 minutes.
Many standout projects are worth exploring, like
Each project includes detailed documentation and setup instructions. Experimenting with cutting-edge AI technologies across various domains is made easy.
Among the many fascinating demos, one caught our attention, an AI-powered audio tour guide that generates personalized narratives for any location. So, we decided to build our own version. This tutorial walks you through how we created a working demo in just 10 minutes.
The demo is, as they said, “A conversational voice agent system that generates immersive, self-guided audio tours based on the user’s location, areas of interest, and tour duration. Built on a multi-agent architecture using OpenAI Agents SDK, real-time information retrieval, and expressive TTS for natural speech output.”
The pipeline of this project consists of two components designed to generate natural speech from simple user input.
The first is a multi-agent system, where multiple specialized AI agents work in parallel to produce different aspects of the tour guide content. Afterwards, it connects to live web search for timely info like operating hours and events, keeping content fresh unlike static guides.
The second converts the generated text into speech using a Text-to-Speech (TTS) model. Once generation completes, the system delivers high-quality MP3 files that users can download immediately and use on any device.
This seems to be a complex project, but the deployment process was actually remarkably simple. Even if you have no software development experience, don't worry. We'll walk you through every command step by step. All the commands below should be executed in your computer's terminal (Command Prompt on Windows, Terminal on Mac/Linux). Follow this deployment checklist, you can get a working demo running locally in just 10 minutes.
Before we begin, ensure you have the following installed on your system.
First, grab the project from the awesome-llm-apps repository.
cd /path-to-your-project-folder
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
cd awesome-llm-apps/voice_ai_agents/ai_audio_tour_agent
Tip: The awesome-llm-apps collection contains dozens of other AI projects you can explore later.
Set up your Python environment and install the required packages.
# Create a Python 3.8+ virtual environment to avoid conflicts
python3.10 -m venv audio_tour_env
source audio_tour_env/bin/activate
# On Windows: audio_tour_env\Scripts\activate
pip install -r requirements.txtThis installs the multi-agent framework, text-to-speech libraries, and all necessary dependencies for the tour guide system.
Tip: If you want to better isolate this project from other Python environments and avoid version conflicts, we strongly recommend first creating a Python 3.8+ virtual environment using the first command, though not necessary for the project to run.
The application uses various AI models for content generation and speech synthesis. You'll need to obtain API keys for two types of models, including an LLM model API key for text generation and a TTS model API key for text-to-speech conversion. While the original project was designed for multiple API providers, you can streamline this process by using a unified API service that provides access to all required models through a single endpoint.
Quick API key setup steps (e.g., OpenAI, DeepSeek, NetMind):
Tip: Many API providers offer free trial credits or free tiers, perfect for testing this demo without any upfront costs.
Great, now we can start the Streamlit interface to see how it goes,
streamlit run ai_audio_tour_agent.py
Your browser will automatically open to localhost:8501 with the tour guide interface ready to go.
Tip: If port 8501 is already in use, Streamlit will automatically try the next available port (8502, 8503, etc.) and show you the correct URL in the terminal output.
In the sidebar, enter your API credentials. Then simply:
Within moments, you'll have a personalized audio tour ready for download as an MP3 file that works on any device, which is perfect for offline exploration during your travels.
Tip: Start with famous landmarks like "Times Square, New York" or "Colosseum, Rome" for the best results, as these locations have rich historical and cultural content that the AI can draw from.
The AI Audio Tour Guide here operates on a three-tier architecture that seamlessly integrates multiple components.
While building this demo, we found that several different AI models needed to work together. In practice, you can have access to these models through various APIs, but we chose to use NetMind’s API service directly since it has covered everything we needed, from Chat Inference with mainstream LLMs to Audio generation with tools like Chatterbox. Now, you only need one API key to have access to all these amazing models at incredibly affordable rates. Here's the cost breakdown to run 20 test generations:
This application provides ultimate convenience for users. Just type in the location, your interests, and adjust other factors to your will, then your personal guide will be generated. Once generation completes, the system delivers high-quality MP3 files that users can download immediately and use on any device, smartphones, tablets, laptops, or dedicated audio players. Users can prepare tours in advance and access them anywhere, anytime throughout their journey.
Now that you understand how the demo works, you can find some amazing projects of your own interests in awesome-llm-apps. You are also welcome to visit this project integrated with NetMind's API service on GitHub repository.
We are so thrilled to see open-source projects like these continue to drive innovation forward, enabling the community to build upon each other's work. Now, let's simply pull the code and deploy our own instance to start experimenting with the technology stack!