Build a Speaker-Diarization Pipeline for a Legal-Tech Startup

FreeVerified credential2 weeksAdvanced

Overview

What this challenge is about.

Build a Speaker-Diarization Pipeline for a Legal-Tech Startup. Advanced challenge in code. Writing production code that solves real engineering problems, ear...

CredentialBlockchain-anchored

ShareableLinkedIn-ready

LanguageEnglish

PaceSelf-paced

The Brief

What you'll do, and what you'll demonstrate.

Build a speaker-diarization pipeline that brings DER under 12 percent on legal-hearing audio.

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

Build a modern speaker-diarization pipeline
Evaluate diarization with DER and sliced analysis
Tune diarization for a known-cardinality speaker setup
Document integration with an existing ASR stack

Program Fit

Where this fits in your program.

Sharpens the same skills your degree expects you to demonstrate.

Speech Recognition and Spoken Language Processing

Master · Nlp

Strong alignment

This challenge maps to Speech Recognition and Spoken Language Processing 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.

Careers

Career paths this challenge builds toward

Completing this challenge demonstrates skills that transfer directly to these roles:

Career paths this builds toward

Canonical roles

NLP Engineer
Language is messy. People misspell, contradict themselves, ask the same thing five different ways, and expect a machine to understand. NLP engineers build the systems that try. The role spans classical text processing in spaCy, modern retrieval-augmented architectures stitched together with LangChain, and the constant judgment calls about when to fine-tune, when to prompt, and when to fall back to rules. It rewards people who love both linguistics and systems thinking. Students grow into it through small projects — a question-answering bot over their notes, a classifier for their inbox — that surface the real failure modes of language models. Good NLP engineers obsess over evaluation as much as architecture.
AI Engineering

NLP Engineer

Owning the diarization layer on top of ASR is the day-to-day work of NLP/speech engineers at any voice-transcription startup.

This challenge sharpens

speaker-diarization
speech-recognition
pyannote

Machine Learning Engineer

Integrating two ML components (ASR + diarization) into a shippable pipeline is core MLE craft.

This challenge sharpens

pyannote
evaluation
pytorch

Applied AI Scientist

Sliced evaluation on the hard cases (witness-vs-defense) and a written integration memo are bread-and-butter applied-AI work.

This challenge sharpens

speaker-diarization
audio-processing
evaluation

One more thing

You can put a credential on your CV by Friday.

Start this challenge