[Microgrant] Realtime Voice Translator

Nobody has claimed this yet.

Application by : __bhuvanesh__
## Project Description
LiveTranslate is a real-time voice translation system that enables seamless communication across language barriers in streaming video. It solves accessibility issues in live content by providing instant, high-quality S2ST (Speech to Speech Translation) while preserving the speaker's voice characteristics.

## Link to public GitHub repo (if applicable)

## Link to demo website (if applicable)

## How is Livepeer used for this project?
Livepeer will serve as the video streaming infrastructure where our ComfyUI node with Facebook's Seamless Communication models will be tested. The integration with comfystream will demonstrate how real-time voice translation can enhance Livepeer's streaming capabilities.

## How will you improve your project with this grant? What steps will you take to meet this objective?
The grant will fund:
- Development of a basic ComfyUI node implementing Seamless Communication models
- Research and confirm Seamless Communication as SOTA for S2ST
- Testing of different buffer sizes to optimize latency vs. accuracy
- Integration with comfystream for proof-of-concept demonstrations
- Creation of documentation and demonstration materials

## Was this project started at a hackathon or another web3 event? Which one?
No, this is an original project conceived specifically to enhance the Livepeer ecosystem.

## Please describe (in words) your team's relevant experience, and why you think you are the right team to build this project. You can cite your team's prior experience in similar domains, doing similar dev work, individual team members' backgrounds, etc.
I bring extensive expertise in ML optimization and deployment at scale. As an ML Product Engineer at Sprinklr, I have:

Led optimization efforts for serving LLMs at scale, reducing server costs by 4.5x and latency by 3x

Implemented PagedAttention and extended vLLM and TensorRT-LLM libraries

Developed agentic systems using LLMs for extracting insights from data

My technical skill set directly applicable to this project includes:

Proficiency in Python, PyTorch, TensorFlow

Experience with speech recognition systems (during my Sprinklr internship)

Docker and Kubernetes for deployment

I've made open-source contributions to Nvidia TensorRT-LLM including:

Bugfix for using custom dataset for calibration in Post Training Quantization

Added support for distil-whisper to TensorRT-LLM

## What is the project’s expected deliverable at the conclusion of the grant time period?
- Working prototype ComfyUI node implementing Seamless Communication model
- Demonstration video showcasing capabilities with Livepeer streaming
- Technical blog post explaining implementation details
- Basic documentation for developers to test and extend the solution

## What is the one thing (the core mechanic) you want someone to do when using your deliverable?
Enable voice translation for a pre-recorded video or simple live stream without much hassle, allowing testing of the technology's potential for breaking language barriers in content creation.

## How will this deliverable benefit the Livepeer ecosystem?
This technology will benefit Livepeer by:
- Demonstrating AI-enhanced video capabilities
- Creating foundation for multilingual streaming solutions
- Expanding potential creator base to global audiences
- Positioning Livepeer at the forefront of accessible content creation

## How did you learn about the Livepeer Grants Program?
From an former colleague and Livepeer Discord.

No repository, files, tests, or concrete entry point is provided. Begin by reviewing the proposed ComfyUI node, Facebook Seamless Communication models, and comfystream integration, including the planned buffer-size experiments. Done would be a working prototype, a Livepeer streaming demonstration, a technical blog post, and developer documentation.

Written by the indexing model from the issue text.