
Building a Fire Detection System with Next.js and YOLOv11
ludgi
Learn how to build a real-time fire detection system using Next.js, YOLOv11, and FastAPI.
초급
Next.js, yolo, Python
This is a practical project course on running Whisper locally and quickly developing an AI speech recognition app using FastAPI and Next.js. You'll implement real-time speech recognition and complete a project that can be used as a simple portfolio piece in a short amount of time.
Next.js
whisper
FastAPI
Vibe Coding
In this course, you will learn how to run Whisper locally and develop an AI speech recognition web app using FastAPI and Next.js.
This technology can be used in a variety of fields, including voice memo apps, real-time meeting recording systems, automatic caption generation, and voice-based chatbots .
✅ How to run Faster-Whisper locally
Learn how to run Whisper models locally without API calls.
✅ Run Whisper in a CPU environment
Normally, Whisper runs on a GPU, but this tutorial will cover how to run it on a CPU-only setup . You will learn how to optimize Whisper so that you can use it without having to configure CUDA.
✅ Development of voice conversion API using FastAPI
Learn how to develop an API that converts voice to text using FastAPI, and integrate it with Next.js to build a voice memo web app that works like a real service .
In this course, you will learn how to run the Whisper model locally and configure it to run efficiently on the CPU . 🚀
✅ Implementing Next.js with Vibe Coding
In this lecture, we will implement the entire Next.js frontend using the vibe coding method . It is a method of implementing it through cursor ai with short theories and short explanations . In other words, it will proceed in a way that you can complete the project right from the lecture.
✅ Integration with FastAPI and Next.js
We will walk through the process of integrating the faster-Whisper speech conversion API implemented with FastAPI with the Next.js frontend . Through this, we will be able to complete the function of actually uploading speech and outputting the converted text to the UI .
✅ Quickly complete your portfolio draft project
By focusing on implementing functionality rather than theory , you will have a simple speech recognition web app at the end of the course. You will gain experience in a short period of time and can use it to develop your own portfolio project.
CPU: Intel Core i7-12700K or equivalent recommended
RAM: Minimum 8GB (recommended 16GB or more)
Disk space: At least 5 GB required for downloading and caching Whisper models
Link to GitHub repository (source code and project files provided)
Text documentation and code samples
If you have experience using Python's basic grammar and FastAPI, you will understand it quickly.
Front-end integration is easy if you have basic knowledge of JavaScript and Next.js.
Familiarity with REST API and WebSocket concepts would be helpful
Who is this course right for?
For those who want to run the Whisper model locally.
Anyone who wants to develop AI-based projects using FastAPI and Next.js
A beginner developer who wants to implement a real-time voice recognition feature.
For those who want to create a draft of an AI speech recognition project to use as a personal portfolio.
For those who want to complete projects in a short amount of time
Need to know before starting?
Basic Python Syntax (for FastAPI Utilization)
JavaScript and React basic concepts (for using Next.js)
REST API and WebSocket Concepts (for Backend-Frontend Integration)
596
Learners
29
Reviews
8
Answers
3.9
Rating
9
Courses
안녕하세요, 주식회사 럿지의 대표입니다.
저는 스타트업, 금융권, 공공기관 등 다양한 분야에서 프로젝트를 진행하며,
개발뿐만 아니라 서비스를 직접 운영하는 경험을 쌓아왔습니다.
이 과정에서 팀원 및 프리랜서들과 협업하며 문제를 해결하고 프로젝트를 완성하는 능력을 길렀습니다.
특히, 단순히 개발자로서의 역할을 넘어서 자신의 서비스를 운영하고자 하는 꿈을 가진 분들께 더 많은 도움을 드릴 수 있다고 생각합니다.
완성의 즐거움과 성취감을 함께 경험하며 성장해 나가길 기대합니다. 감사합니다.
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11 lectures ∙ (1hr 13min)
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$4.40
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