Kensub Video Content Analysis and Clipping Service

An API service that automatically processes videos to create engaging clips with subtitles. It uses Whisper for transcription and the Grok API for intelligent content analysis.

Features

• Automatic video transcription using OpenAI's Whisper
• Intelligent content segmentation using Grok API
• Automatic subtitle generation and overlay
• REST API interface for video processing
• Progress tracking and job management
• Multi-user support
• Clip and transcript downloads

Prerequisites

• Docker and Docker Compose
• Python 3.10+
• NVIDIA GPU (optional, for faster processing)

GPU Support (Optional)

If you want to use GPU acceleration for faster transcription:

For Debian:

# Add NVIDIA repository & GPG key
curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg

curl -s -L https://nvidia.github.io/libnvidia-container/debian11/libnvidia-container.list | \
    sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
    sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list

# Install the toolkit
sudo apt-get update
sudo apt-get install -y nvidia-container-toolkit
sudo systemctl restart docker

Installation

1. Clone the repository:

git clone <repository_url>
cd video-content-service

2. Create a .env file:

GROK_API_KEY=your_grok_api_key
DATABASE_URL=postgresql://user:password@postgres/videodb
REDIS_URL=redis://redis:6379/0

3. Start the services:

docker-compose up --build

API Endpoints

Upload Video

POST /videos/

Upload a video file for processing. Returns a job ID.

Check Job Status

GET /videos/{job_id}

Get the current status of a processing job.

List Clips

GET /videos/{job_id}/clips

Get a list of generated clips for a job.

Download Clip

GET /videos/{job_id}/clips/{clip_filename}

Download a specific clip.

Get Full Transcript

GET /videos/{job_id}/transcript

Get the complete transcript of the video.

Get Clip Transcript

GET /videos/{job_id}/clips/{clip_filename}/transcript

Get the transcript for a specific clip.

Usage Example

import requests

# Set your API key
headers = {'X-API-Key': 'your_api_key'}

# Upload a video
with open('video.mp4', 'rb') as f:
    response = requests.post(
        'http://localhost:8000/videos/', 
        files={'file': f},
        headers=headers
    )
job_id = response.json()['id']

# Check job status
status = requests.get(
    f'http://localhost:8000/videos/{job_id}', 
    headers=headers
).json()

# Get full transcript
transcript = requests.get(
    f'http://localhost:8000/videos/{job_id}/transcript', 
    headers=headers
).json()

# List clips
clips = requests.get(
    f'http://localhost:8000/videos/{job_id}/clips', 
    headers=headers
).json()

# Download clip
clip_filename = clips[0]['filename']
clip = requests.get(
    f'http://localhost:8000/videos/{job_id}/clips/{clip_filename}', 
    headers=headers
).content

# Get clip transcript
transcript = requests.get(
    f'http://localhost:8000/videos/{job_id}/clips/{clip_filename}/transcript', 
    headers=headers
).text

Project Structure

app/
├── __init__.py
├── main.py           # FastAPI endpoints
├── config.py         # Configuration settings
├── models.py         # Database models
├── database.py       # Database connection
├── schemas.py        # Pydantic models
├── tasks.py         # Celery tasks
└── core/
    └── video_processor.py  # Core processing logic

Configuration

The service can be configured through environment variables:

• GROK_API_KEY: Your Grok API key
• MAX_CONCURRENT_JOBS: Maximum number of concurrent processing jobs (default: 3)
• MAX_UPLOAD_SIZE: Maximum video file size in bytes (default: 1GB)
• WHISPER_MODEL: Whisper model to use (default: "medium")

Development

1. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install dependencies:

pip install -r requirements.txt

3. Run the development server:

uvicorn app.main:app --reload