From 'Speaker 0' to 'Alex Hormozi', a guess.
So, your data pipeline is humming along, transcribing podcasts perfectly. But there's a catch: my database is full of anonymous labels like Speaker 0 and Speaker 1. I encountered this exact obstacle while trying to identify Alex Hormozi in his podcast. To make the data truly useful, I needed to know who was speaking.
My first plan involved using a dedicated "voice fingerprinting" service. I researched Microsoft Azure's Speaker Recognition API, which seemed ideal. The idea was to enroll Alex's voice to create a unique "voiceprint" and then match new audio against it. It was a solid plan, until we came across a critical detail in the documentation.
The Plot Twist and the Pivot
The Azure service I intended to use is being retired on September 30, 2025. Building a core feature on a deprecated API is a non-starter. This forced me to pivot and ask a better question: "Can I solve this with the tools I already have?" The answer was a resounding yes.
My pipeline was already using OpenAI to create vector embeddings for text. I realized I could apply the same method for voice. My new challenge wasn't a complex multi-speaker issue but a simple binary question: "Is this speaker Alex Hormozi, or not?" This is a perfect use case for vector similarity.
A Smarter, More Integrated Approach
My new architecture is surprisingly simple and integrates seamlessly into our existing workflow.
First, create a "Vector Voiceprint."
I'll take high-quality audio clips of Alex Hormozi and use the OpenAI embeddings model to generate a set of reference vectors that numerically represent his voice.
Then, identify using Cosine Similarity
In the pipeline, I'll generate embeddings for each anonymous speaker and calculate the cosine similarity between their vectors and Alex's reference vectors. If the score is high, I identify our host.
I think this pivot will be a huge win. My new solution feels more future-proof, incredibly cost-effective, and streamlined, as it simply extends the capabilities of a tool already in my stack.
It was a powerful lesson: sometimes, the best solution comes from rethinking the problem with the tools ou already have.
– Benoit Meunier