Overview
What this challenge is about.
Build an Audio-Visual Speaker Diarization Pipeline. Advanced challenge in code. Writing production code that solves real engineering problems, earn a blockch...
The Brief
What you'll do, and what you'll demonstrate.
Cut Diarization Error Rate on tutoring sessions by fusing audio + video and prove the win on a 50-session held-out test.
This is not a coding exercise. It is the work a software engineer does between a Jira ticket and a merged PR. That distinction matters to every hiring manager who has seen candidates solve LeetCode problems and none who have shipped production code under real constraints.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
Earning criteria — what you'll demonstrate
- Combine audio and video modalities at the right granularity
- Apply active-speaker detection to disambiguate similar voices
- Evaluate diarization with standard metrics (DER, JER)
- Hand off a multimodal pipeline to a non-ML platform team
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Multimodal Machine Learning
Master · Machine Learning
Strong alignment
This challenge maps to Multimodal Machine Learning at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Audio Visual Fusion
Apply audio visual fusion to solve real industry problems and demonstrate production-level capability.
- Speaker Diarization
Apply speaker diarization to solve real industry problems and demonstrate production-level capability.
- Active Speaker Detection
Apply active speaker detection to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch to solve real industry problems and demonstrate production-level capability.
- Pyannote
Apply pyannote to solve real industry problems and demonstrate production-level capability.
- Evaluation
Apply evaluation to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
ML Researcher
Fusing audio + video for diarization with honest DER evaluation is the applied multimodal research that edtech and conferencing AI teams hire for.
This challenge sharpens
- audio-visual-fusion
- speaker-diarization
- evaluation
Applied AI Scientist
Translating open research models into a working multimodal pipeline with a demo and handoff is core applied-AI-scientist work at AI-first startups.
This challenge sharpens
- audio-visual-fusion
- active-speaker-detection
- pytorch
Machine Learning Engineer
Shipping a production-shape AV pipeline that the platform team adopts is exactly the MLE work that edtech AI teams need on roadmap.
This challenge sharpens
- pytorch
- pyannote
- speaker-diarization