Joshua Oluwatigbemileke Agboola

# Nigerian Accented Speech-to-Text Model A fine-tuned version of OpenAI's Whisper model, trained to better understand Nigerian-accented English speech. ## What It Does Most speech-to-text (STT) systems struggle with Nigerian accents because they're mainly trained on American or British English. This project fine-tunes Whisper on Nigerian-accented voice data to improve transcription accuracy for Nigerian speakers. ## How It Was Built - **Base model:** `openai/whisper-small` - **Training data:** Nigerian-accent subset of the Mozilla Common Voice dataset (`benjaminogbonna/nigerian_common_voice_dataset`) - **Language:** Python - **Core libraries:** - `torch` (PyTorch) — model training, tensor operations, GPU handling - `transformers` (Hugging Face) — Whisper model, tokenizer, and feature extractor - `datasets` (Hugging Face) — loading and splitting the training data - `huggingface_hub` — authenticating and downloading models/datasets - **Evaluation:** - `evaluate` — scoring model outputs - `jiwer` — calculating Word Error Rate (WER) and Character Error Rate (CER) - `rapidfuzz` — string alignment used internally by `jiwer` ## Why It Matters Accurate Nigerian-accented STT can support: - Medical documentation (transcribing doctor-patient conversations) - Customer service and voice assistants - Call analysis for sales/support quality - Video captioning and searchable media - Voice dictation in apps and internal tools ## What's Next The model currently only covers Nigerian-accented **English**. Future work will expand it to **Nigerian Pidgin, Yoruba, and Igbo**, to make speech-to-text technology more inclusive of Nigeria's language diversity.

# RDA AI: Robotics Development Assistant RDA AI is an advanced, full-stack AI assistant tailored for robotics engineers and software developers. The platform streamlines hardware configuration, embedded programming, ROS (Robot Operating System) node generation, and physical simulation modeling. By pairing multi-modal generation with persistent state tracking, RDA AI acts as an end-to-end co-pilot for hardware engineering workflows. --- ## 🚀 Key Features * **Intelligent Code Generation:** Generates and debugs production-ready firmware code (Arduino C++, ESP-IDF, Python) for microcontrollers, sensors, and actuators. * **Schematic & Circuit Assistance:** Analyzes pinout layouts, visualizes component wirings, and troubleshoots electrical hardware logic. * **High-Fidelity Concept Visualizations:** Uses advanced text-to-image pipelines to generate visual design mockups, CAD concepts, and chassis structural layouts. --- ## 🛠️ Tech Stack & Architecture The system utilizes an asynchronous backend layer integrated with modern multi-modal foundation models and cloud database services: * **Frontend UI:** Built with **Streamlit**, providing a clean, responsive, dashboard-driven user interface for real-time streaming text interaction and image rendering. * **Orchestration & Compute:** Driven entirely by **Python**, handling data transformations, environment variables, and asynchronous API communication. * **AI Engine & Foundations:** * **Google Vertex AI:** Hosts and provides enterprise-grade orchestration for our cognitive pipelines. * **Gemini 2.5 Text (`gemini-2.5-text`):** Powers deep reasoning, logical sequence planning, context retrieval, and structured hardware code generation. * **Imagen 4 Model:** Handles high-fidelity spatial configurations, generating visual CAD blueprints, part prototypes, and structural robotic chassis mockups. * **Database & Persistence:** **Supabase** (PostgreSQL) manages secure user authentication, stores historical chat threads, keeps vector-mapped hardware device contexts, and handles generation logs. --- ## 📂 Project Structure ```text ├── .env.example # Sample environment configuration file ├── requirements.txt # Python dependencies ├── app.py # Main Streamlit application entrypoint ├── core/ # Core backend logic │ ├── __init__.py │ ├── ai_engine.py # Vertex AI, Gemini, and Imagen configurations │ └── database.py # Supabase client wrapper and CRUD operations ├── utils/ # Helper modules │ └── formatters.py # Code blocks, markdown, and layout parsers └── assets/ # App icons, static graphics, and styling sheets