Aqib Ali Ghumro
> BACK_TO_SYSTEMS
AI[SYS_ID: CHANNEL-IQ]

Channel-IQ

AI video-processing platform that turns long-form YouTube videos into short clips and publishing assets.

01.System Interface & Screenshots

Full-Stack / Applied AI Engineer
Channel-IQ Interface Screenshot
[SCREENSHOT_01_ACTIVE]

02. Project architecture visualization

Interactive visualization of the implemented video-processing pipeline

A pipeline that downloads video, transcribes audio, selects highlights, renders clips, and prepares upload metadata. [This visualization reflects the implemented workflow from the working prototype and does not imply live monitoring.]

Speed:
React Client
Django API
YouTube Downloader
FFmpeg Audio Extraction
OpenAI Whisper Transcription
Timestamped Transcript Processing
OpenAI Highlight Selection
YOLO Face Detection
OpenCV Framing and Crop Logic
FFmpeg Clip Rendering
Caption and Enhancement Processing
Three Generated Clips
OAuth / YouTube Data API
Upload or Scheduled Publishing
Active Pipeline Stage
Launch simulation to view stage details.
Live Stage Data Payload
Data telemetry will load here.
Console Logs
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>Ready to trace workflow pipelines...

03. The Problem

Long-form videos take significant manual effort to review, trim, caption, and prepare for short-form publishing.

04. The Solution

Built a video-processing workflow that downloads source footage, transcribes audio, identifies highlights, applies face-aware framing, renders vertical clips, and prepares SEO metadata for upload and publishing.

05. Key Features

  • >YouTube video input and download
  • >FFmpeg audio extraction
  • >Whisper transcription with timestamp handling
  • >OpenAI highlight selection
  • >Three generated short-form clips
  • >YOLO face detection and face-aware framing
  • >16:9 to 9:16 conversion
  • >Caption, title, description, and tag generation
  • >OAuth authentication and YouTube upload
  • >Scheduled publishing

AI & Automation Workflows

-OpenAI highlight selection from timestamped transcript segments
-YOLO face detection and OpenCV crop logic
-Face-aware framing for active-speaker and multi-face clips

08. Engineering Challenges

Keeping transcript timestamps aligned with rendered clips

Combined Whisper output with timestamp-aware processing before clip rendering.

Preserving the active speaker while converting from widescreen to vertical video

Applied face-aware framing before FFmpeg rendering.

[SYSTEM_SPECIFICATIONS]
ROLE:
Full-Stack / Applied AI Engineer
CATEGORY:
AI
TECHNOLOGY STACK:
PythonDjangoOpenAIWhisperYOLOOpenCVFFmpeg
BUSINESS IMPACT:
Reduced turnaround for a 1-hour video from roughly 3-4 hours of manual editing to under 25 minutes on a GPU VM.