ClearCap
"Breaking sound barriers."
The problem ClearCap solves
First Time Builder
š§© Problem
Millions of Deaf and hard-of-hearing individuals in India struggle to access online contentāespecially live or fast-paced media like YouTube videos, lectures, livestreams, and educational content.
Existing caption systems fail because:
Auto-generated captions are often inaccurate, especially for Indian languages.
Many captions are too complex, making them difficult to read quickly.
Most platforms do not support Indian Sign Language (ISL).
Live captions lag or break, creating confusion and exclusion.
As a result, a significant portion of online content remains inaccessible.
š” Our Solution: ClearCap
ClearCap bridges this accessibility gap with a fully AI-powered, real-time captioning system designed specifically for India.
ā 1. Real-Time Multilingual Captions
ClearCap processes audio from YouTube (and soon any media source) and generates real-time captions in 10+ major Indian languages.
It enables instant comprehension, even for fast-paced speech.
ā 2. Simplified, Deaf-Friendly Caption Mode
Many Deaf users prefer simplified captions instead of direct transcriptions.
ClearCap automatically produces:
Original captions
Simplified captions
Both displayed side-by-side for accessibility
This ensures clarity, readability, and understanding.
ā 3. Perfectly Synced to Video Playback
Unlike traditional caption dumps, ClearCap uses a custom timestamp engine that syncs captions precisely with the video timelineā
even if users pause, skip, or scrub.
ā 4. Low Latency Through AI Workers
A separate Python worker system handles:
audio extraction
chunking
transcription
translation
streaming captions
This architecture ensures sub-second latency and scalable performance.
ā 5. Built for Indian Languages First
ClearCap handles regional accents, code-switching (HindiāEnglish mix), and Indian pronunciation patterns much better than generic caption engines.
š Coming Soon: Indian Sign Language (ISL) Support
One of ClearCapās key upcoming features is AI-generated ISL representation.
We aim to:
Translate caption text to ISL gloss
Animate it using 3D avatar signing models
Provide dual-mode captions: Text + ISL
Make digital content accessible to users who prefer sign language over text
This will be a first-of-its-kind step towards inclusive, full-spectrum accessibility for the Deaf community in India.
šÆ Impact
ClearCap makes online content inclusive, understandable, and accessible, unlocking equal access for:
Deaf & hard-of-hearing individuals
Students learning in regional languages
Older adults with hearing loss
Anyone watching videos without sound
By bridging audio, text, and soon sign language, ClearCap moves us closer to a world where everyone can consume content equally, regardless of hearing ability.
Challenges we ran into
š§± Challenges I Ran Into
1ļøā£ Integrating AWS Into a Real-Time YouTube Captioning Pipeline
One of the biggest challenges was making AWS work smoothly with live YouTube audio, because YouTube does not offer direct streaming access to raw audio. Instead, I had to:
Implement a Python worker that downloads the video audio in parallel while the user is watching.
Break the audio into small chunks and upload them to S3 continuously.
Trigger AWS Transcribe or Whisper processing on each chunk without creating delay.
Handle the fact that AWS services, especially S3 and Transcribe, are not naturally optimized for sub-second latency.
Maintain stable and secure environment variables across multiple components ā backend, worker, and local dev ā without pushing them to GitHub, which caused initial push protection errors.
Synchronizing all parts ā the worker downloading audio, AWS jobs running asynchronously, captions being generated, and the frontend syncing everything to the video ā required building a custom low-latency, event-driven pipeline using Socket.io.
Achieving smooth playback + accurate caption timing was significantly harder than expected and required careful orchestration of multiple asynchronous systems working together.
2ļøā£ Designing an Indian Sign Language (ISL) Framework for Future Integration
Another major challenge was planning how to integrate ISL (Indian Sign Language) into ClearCap.
Unlike English or Hindi text translation, ISL does not have:
A widely available open dataset for training
A standardized glossing format
A simple text-to-sign mapping
ISL translation involves complex grammar transformation, facial expressions, and motion.
To implement this realistically in the future, I had to understand:
How to convert speech ā text ā sign language gloss
How 3D avatar models or animation frameworks represent ISL gestures
How to design the system so that ISL can plug in later without rewriting the entire pipeline
Also, since real-time sign language generation must stay low latency, I needed to architect the backend in a modular way so that ISL output can be streamed just like text captions.
While ISL isnāt fully implemented yet, laying the groundwork ā from gloss mapping to avatar rendering ā was one of the most complex conceptual challenges in the project.