Project Summary

Role: Lead Developer & Product Architect

Timeline: 4 months

Github: https://github.com/ariana02880/video-to-notes-scribe?tab=readme-ov-file

Core Technologies: Multi-Model AI Orchestration, Speech-to-Text (STT) Engines, API Abstraction Layer, Cloud AI Services

This project focused on designing and delivering a flexible, production-ready speech-to-text platform that allows users to dynamically select different AI models for voice transcription based on accuracy, latency, language support, and cost considerations.

Rather than locking users into a single vendor, the system was architected as a model-agnostic STT orchestration layer, enabling seamless switching between best-in-class speech recognition engines without changing the user experience.

Project Objectives (SMART)

• Model Flexibility: Support multiple leading speech-to-text engines within a unified product interface, allowing real-time model selection by the user.
• Transcription Accuracy: Achieve high transcription fidelity across diverse accents, audio qualities, and speaking styles by leveraging specialized models.
• Latency Control: Deliver transcription results within acceptable real-time or near–real-time thresholds depending on the selected model.
• Scalability: Build a modular architecture that allows rapid onboarding of new STT providers without refactoring core application logic.

Product Overview

The platform enables users to upload or record audio and choose their preferred AI transcription engine before processing. Each engine offers different trade-offs in accuracy, speed, language coverage, and pricing, empowering users to select the most appropriate model for their specific use case.

Supported model categories include:

• General-purpose large-scale speech models
• Enterprise-grade cloud speech services
• Real-time transcription engines optimized for meetings and calls