Why I’m building Speech To Text Tools

The problem that started it

I began this project while building a tool to gather knowledge from YouTube videos. I assumed modern speech-to-text would be effectively solved — but early experiments showed otherwise. In practice, many systems struggle with names, brands, and other domain-specific terms; even well‑known proper nouns can be transcribed incorrectly. That makes reliable knowledge extraction difficult without systematic verification.

Comparing providers turned out to be more work than expected. Vendor demos rarely expose the full set of API parameters, and meaningful testing typically requires creating accounts and enabling billing. That friction makes trying two or three providers a time-consuming and demotivating process for product and research teams.

What I built first

To solve that problem I started by building an internal gateway to simplify evaluation. It evolved into Speech To Text Tools — a standalone product that reduces friction for teams who need to experiment and validate transcription quality. The platform exposes provider API options consistently, so you can reproduce and compare results without juggling multiple accounts or hidden settings.

Where it’s going

Beyond ease of use, the goal is to be an independent, third‑party authority: we publicly evaluate providers, track performance metrics (including WER) on curated datasets, and monitor changes over time. That helps engineering and product teams make data‑driven decisions, verify vendor claims, and reduce the operational overhead of choosing a transcription solution.