Clipnetic AI

AI Podcast Clipper SaaS

Clipnetic-AI is an end-to-end SaaS platform that transforms long-form podcast videos into viral short-form clips optimized for platforms like YouTube Shorts and TikTok. Powered by cutting-edge AI models, the app automatically transcribes content, detects high-engagement segments, and intelligently crops the clip to focus on the active speaker.

Basic Workflow of the Application

---

Tech Stack

• Frontend: Next.js 15, ShadCN UI, Tailwind CSS
• AI/Backend: Python, Gemini AI model, Modal (serverless GPU)
• Queue System: Inngest for background processing
• Auth: NextAuth.js with JWT & credential providers
• Cloud: AWS S3, ECR, EKS with ALB Ingress Controller
• Payments: Razorpay integration
• Database: Prisma ORM with Neon PostgreSQL

---

Features

• 🎬 Auto-detection of viral moments in podcasts (stories, questions, etc.)
• 🔊 Automatically added subtitles on clips
• 📝 Transcription with m-bain/whisperX
• 🎯 Active speaker detection for video cropping with Junhua-Liao/LR-ASD
• 📱 Clips optimized for vertical platforms (TikTok, YouTube Shorts)
• 🎞️ GPU-accelerated video rendering with FFMPEGCV
• 🧠 LLM-powered viral moment identification with Gemini 2.5 Pro
• 📊 Queue system with Inngest for handling user load
• 💳 Credit-based system
• 💰 Razorpay integration for credit pack purchases
• 👤 User authentication system
• 📱 Responsive Next.js web interface
• 🎛️ Dashboard to upload podcasts and see clips
• ⏱️ Inngest for handling long-running processes
• ⚡ Serverless GPU processing with Modal
• 🌐 FastAPI endpoint for podcast processing
• 🎨 Modern UI with Tailwind CSS & Shadcn UI

---

FFmpeg – media converter

To crop a video, use the following command:

ffmpeg -ss 00:14:00 -to 00:20:00 -i podcast.mp4 -c copy podcast6min.mp4

---

AWS Setup

Setup S3 Bucket

• Create a new S3 bucket from AWS Console

• CORS configuration for S3 bucket

  [
      {
          "AllowedHeaders": [
              "Content-Type",
              "Content-Length",
              "Authorization"
          ],
          "AllowedMethods": [
              "PUT"
          ],
          "AllowedOrigins": [
              "*"
          ],
          "ExposeHeaders": [
              "ETag"
          ],
          "MaxAgeSeconds": 3600
      }
  ]

• You can create a new folder (/test1) in that bucket and upload an example video file.

Setup IAM User

• Create a new User from IAM Dashboard

• Provide User name and then Next

• Attach policies directly > Create policy > Specify permissions in JSON

• IAM user policy to upload, download and list bucket items:

  {
      "Version": "2012-10-17",
      "Statement": [
          {
              "Effect": "Allow",
              "Action": [
                  "s3:ListBucket"
              ],
              "Resource": "[S3 ARN here]"
          },
          {
              "Effect": "Allow",
              "Action": [
                  "s3:GetObject",
                  "s3:PutObject"
              ],
              "Resource": "[S3 ARN here]/*"
          }
      ]
  }

• Refresh and search it, add that policy to the new User

Create Access Key

• Select that newly created IAM User > Security and Credentials
• Create access key
• Select "Application running outside AWS"
• Copy the "Access key" and "Secret access key"
• Set those two as new secret in Modal
  • AWS_ACCESS_KEY_ID
  • AWS_SECRET_ACCESS_KEY

---

Setup the Project Locally

Follow these steps to install and set up the project.

Clone the Repository

git clone --recurse-submodules https://github.com/SuperSection/Clipnetic-AI.git

Install Python

Download and install Python if not already installed. Refer to the Official Python Download page for guidance on installation.

Create a virtual environment with Python 3.12. You can use pyenv or pyenv-win to set specific Python version while creating a .venv

---

Backend Setup

1. Navigate to backend folder:

   cd clipnetic-ai-backend

2. Install dependencies:

   pip install -r requirements.txt

3. Clone the LR-ASD repo into the backend folder, and rename the folder as asd:

   git clone https://github.com/Junhua-Liao/LR-ASD.git asd

4. Modal setup:

   modal setup

5. Run on Modal:

   modal run main.py

6. Deploy backend:

   modal deploy main.py

---

Frontend Setup

Install dependencies:

cd clipnetic-ai-frontend
npm i

Run:

npm run dev

---

Inngest Queue

Run the local queue development server with Inngest:

cd clipnetic-ai-frontend
npm run inngest-dev

Access Inngest Development UI on: http://localhost:8288

Inngest Cloud

Refer to inngest cloud and follow ther steps to setup function and sync app.

---

Razorpay – Payment Gateway

Refer to Razorpay Node.js Integration Docs

---

CI/CD Pipeline Automation

Overview of what I implemented:

• GitHub Actions with self-hosted runners
• Multi-stage pipeline: Lint → Test → Security → Build → Deploy
• SonarQube for code quality analysis
• Trivy for vulnerability scanning
• Docker containerization with multi-stage builds
• ECR for container registry
• EKS deployment with Kubernetes manifests
• AWS Load Balancer Controller for ingress
• Automated secret management in K8s
• Slack notifications for deployment status

---

Author

• Soumo Sarkar

Reference

• Python3 library for downloading YouTube Videos
• FFmpeg
• LR-ASD
• WhisperX
• ffmpegcv
• inngest – Next.js Quick Start
• shadcn/ui
• Favicon Generator
• React Hook Form
• shadcn-dropzone
• @aws-sdk/client-s3
• @aws-sdk/s3-request-presigner
• Razorpay Node.js Integration Steps
• SonarQube cloud