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🎙️ AI Debate Coach

Real-Time AI Debate Coach with Predictive Turn-Taking

A full-stack web application that listens to a user debating in real time, transcribes their speech, predicts natural turn boundaries, and responds with AI-generated coaching feedback — all over a single WebSocket connection.

Built as a final-year project exploring low-latency human–AI spoken interaction.

Features

Feature Detail
Streaming STT Deepgram Nova-3 via WebSocket — real-time interim + final transcripts
Predictive turn-taking Hybrid VAD (RMS energy + optional Silero) with adaptive silence thresholds
LLM coaching Groq-hosted models respond to user arguments with debate feedback
TTS playback AI responses synthesised to audio and streamed back to the browser
Google OAuth One-tap sign-in, JWT session tokens
Session history Past debates stored in PostgreSQL, browseable from the dashboard
Latency metrics End-to-end pipeline timing (STT → LLM → TTS) tracked per session
Single-container prod Multi-stage Docker build bundles the Vite SPA into the FastAPI server

Tech Stack

Backend

  • Python 3.12 / FastAPI / Uvicorn
  • WebSockets — persistent bidirectional audio + control channel
  • Deepgram SDK — streaming speech-to-text (Nova-3)
  • OpenAI Python SDK → Groq — LLM inference
  • SQLAlchemy 2 + asyncpg — async PostgreSQL ORM
  • Alembic — database migrations
  • PyJWT + bcrypt — authentication

Frontend

  • React 18 / TypeScript / Vite
  • Zustand — state management
  • React Router 7 — client-side routing
  • Web Audio API — microphone capture, 48 kHz → 16 kHz resampling, PCM16 encoding
  • Google OAuth (@react-oauth/google)

Infrastructure

  • Docker Compose — dev (3 containers) and prod (2 containers) configurations
  • PostgreSQL 16 (Alpine)
  • GitHub Actions — CI/CD with test gate → SSH deploy
  • Azure VM — production host

Project Structure

finalyear/
├── backend/
│   ├── Dockerfile              # Multi-stage: Node build → Python runtime
│   ├── requirements.txt
│   ├── alembic/                # Database migrations
│   ├── app/
│   │   ├── main.py             # FastAPI app, CORS, SPA static mount
│   │   ├── config.py           # Pydantic settings (env vars)
│   │   ├── db/                 # SQLAlchemy models, session, init
│   │   ├── routers/
│   │   │   ├── api.py          # REST endpoints (sessions, health)
│   │   │   ├── auth.py         # Google OAuth + JWT auth routes
│   │   │   └── ws_handler.py   # WebSocket: audio → STT → turn-taking → LLM → TTS
│   │   ├── schemas/            # Pydantic message schemas
│   │   └── services/
│   │       ├── stt_service.py          # Deepgram / Groq / local STT
│   │       ├── llm_service.py          # LLM coaching responses
│   │       ├── tts_service.py          # Text-to-speech synthesis
│   │       ├── turn_taking_service.py  # Hybrid VAD + silence detection
│   │       ├── session_service.py      # DB session CRUD
│   │       ├── auth_service.py         # JWT + Google token verification
│   │       ├── metrics_service.py      # Pipeline latency tracking
│   │       └── latency_tracker.py      # Per-stage timing
│   └── tests/                  # Unit + integration tests (pytest)
├── web/
│   ├── src/
│   │   ├── components/         # Layout, Transcript, TurnIndicator, etc.
│   │   ├── hooks/              # useAudioCapture, useWebSocket
│   │   ├── pages/              # Auth, Debate, Dashboard, History, etc.
│   │   └── stores/             # Zustand stores (auth, app, debate)
│   └── public/
├── shared/types/               # TypeScript message type definitions
├── docker-compose.yml          # Development stack
├── docker-compose.prod.yml     # Production stack
├── Makefile                    # Convenience commands
└── .github/workflows/
    └── deploy-azure.yml        # CI/CD: test → deploy over SSH

Getting Started

Prerequisites

1. Clone the repo

git clone https://github.com/nekumartins/finalyear.git
cd finalyear

2. Create your .env file

cp .env.example .env   # or create manually

Required variables:

# API Keys
DEEPGRAM_API_KEY=your_deepgram_key
GROQ_API_KEY=your_groq_key

# Auth
SECRET_KEY=some-random-secret-string
VITE_GOOGLE_CLIENT_ID=your_google_oauth_client_id.apps.googleusercontent.com

# STT provider: deepgram (default), groq, or faster-whisper
STT_PROVIDER=deepgram

# CORS (production domain, or * for dev)
ALLOWED_ORIGINS=*

3. Run the dev stack

make dev
# or: docker compose up --build

This starts:

Service URL
Frontend (Vite HMR) http://localhost:3000
Backend (FastAPI) http://localhost:8000
PostgreSQL localhost:5432

4. Run the production stack

make prod
# or: docker compose -f docker-compose.prod.yml up --build -d

In production, the Vite SPA is compiled into the backend container and served from FastAPI at http://localhost:8000.

Makefile Commands

Command Description
make dev Start dev stack (hot-reload, foreground)
make dev-d Start dev stack (detached)
make prod Build + start production stack
make down Stop dev stack
make prod-down Stop production stack
make logs Tail all logs (make logs s=backend for one service)
make ps Show running containers
make shell Open bash in backend container
make migrate Run Alembic migrations

Running Tests

# With conda/venv (from repo root):
pip install -r backend/requirements.txt
python -m pytest backend/tests/ -x --tb=short

# Inside the backend container:
make shell
pytest backend/tests/ -x --tb=short

CI/CD

The GitHub Actions workflow (.github/workflows/deploy-azure.yml) runs on every push to master:

  1. Test — installs dependencies and runs pytest (unit tests, no integration/edge)
  2. Deploy — SSHs into the production VM, pulls the latest code, rebuilds Docker containers

Required GitHub Secrets

Secret Value
AZURE_SSH_PRIVATE_KEY SSH private key for the production server
AZURE_SSH_HOST Server hostname / IP
AZURE_SSH_USER SSH username

Optional: AZURE_SSH_PORT (default 22), AZURE_SSH_KNOWN_HOSTS, AZURE_APP_DIR.

Architecture

Browser                          Server (Azure VM)
┌──────────┐    WebSocket       ┌────────────────────────────────┐
│  React   │◄──────────────────►│  FastAPI  ws_handler.py        │
│  App     │  audio_chunk (b64) │                                │
│          │  transcripts       │  ┌──────────┐  ┌───────────┐  │
│  Audio   │  ai_response       │  │ Deepgram │  │ Groq LLM  │  │
│  Capture │  tts_audio         │  │ STT (WS) │  │ (REST)    │  │
│  16kHz   │  state changes     │  └──────────┘  └───────────┘  │
└──────────┘                    │                                │
                                │  ┌──────────┐  ┌───────────┐  │
                                │  │ Turn-    │  │ TTS       │  │
                                │  │ Taking   │  │ Service   │  │
                                │  │ (VAD)    │  │           │  │
                                │  └──────────┘  └───────────┘  │
                                │                                │
                                │  ┌──────────────────────────┐  │
                                │  │ PostgreSQL (sessions,    │  │
                                │  │ users, metrics)          │  │
                                │  └──────────────────────────┘  │
                                └────────────────────────────────┘

Audio pipeline: Mic → ScriptProcessorNode (48 kHz) → resample to 16 kHz → PCM16 LE → base64 → WebSocket → Deepgram streaming → interim/final transcripts → hybrid turn-taking → Groq LLM → TTS → audio back to browser.

Environment Variables

Variable Default Description
DATABASE_URL postgresql+asyncpg://... Async Postgres connection string
SECRET_KEY Random per-process JWT signing key (set explicitly in prod)
DEEPGRAM_API_KEY — Deepgram API key for streaming STT
GROQ_API_KEY — Groq API key for LLM inference
STT_PROVIDER deepgram STT engine: deepgram, groq, or faster-whisper
VITE_GOOGLE_CLIENT_ID — Google OAuth client ID (build-time)
ALLOWED_ORIGINS — Comma-separated CORS origins, or *
DEBUG false Enable debug logging
VAD_THRESHOLD 0.5 Silero VAD confidence threshold

License

This project is part of a final-year academic submission. All rights reserved.

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