Watch any debate. See who's actually telling the truth.

Debatly listens to a live debate (or an uploaded recording), transcribes it with per-speaker labels, fact-checks the claims against the web, tracks contradictions, and turns it all into a clear credibility score and a readable post-debate report — no winner declared, just the facts and an honest reading.

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🎬 Demo

debatly-demo-web.mp4

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✨ What it does

• 🎙️ Live capture — Streams your mic to the server over a WebSocket. Words are transcribed in real time by Speechmatics; who is speaking is decided by a local diarization engine (Silero VAD + pyannote/wespeaker voice embeddings with persistent speaker profiles). Each transcribed word is matched to a voice fingerprint, so a speaker keeps the same label across the whole debate.
• 📁 Import a recording — Upload an audio/video file or paste a video URL. Debatly extracts the audio, diarizes and transcribes the whole thing, then runs it through the exact same analysis pipeline as a live debate.
• 🧠 Live Debate Desk — As people talk, the app builds the board:
  • Debate points grouped into themed, collapsible families per side.
  • Claims & fact-checks with a verdict (Verified / False / Misleading / Unverified), a one-line reason, and real sources.
  • Self-contradictions & double standards caught across the debate.
• ⚖️ Credibility score — A deterministic score that rewards verified claims and penalizes false/misleading ones and self-contradictions, so volume never beats accuracy. It measures honesty, not who talked more or whose opinion you like.
• 📊 Post-debate report — Generated when the debate ends:
  • A plain-English verdict (no winner is declared — you decide).
  • A score-over-time chart with markers for when each speaker entered.
  • A per-speaker breakdown with stats, a judging note, and a standout quote.
  • Side summaries, turning points (with gained/lost-ground impact), and the scoring methodology.
• 📄 Native PDF export — Download the whole report as a text-based PDF with vector charts.
• 🔁 Long, uninterrupted sessions — Built to run for hours without dropping the connection.

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🛠️ How it works

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flowchart TD
    U(["🎙️ Browser — live mic · file · video URL"])
    API["⚙️ Node · Express · WebSocket"]
    U == "audio · file · link" ==> API

    subgraph LIVE["⚡ Live"]
      direction LR
      SM["Speechmatics<br/>realtime words · STT"]
      LD["Local diarizer · Python<br/>Silero VAD + pyannote/wespeaker<br/>online speaker clustering"]
    end

    MERGE{{"Word → speaker merge<br/>persistent labels"}}

    subgraph BATCH["📦 Upload / URL"]
      PY["pyannote.ai<br/>precision-2 diarize + Whisper"]
    end

    API -- "live audio" --> SM
    API -- "live audio" --> LD
    API -- "upload / URL" --> PY
    SM --> MERGE
    LD --> MERGE

    subgraph PIPE["🧠 Analysis pipeline"]
      direction TB
      SB["Side Builder"] --> DP["Debate-Point Builder"] --> CB["Claim Builder"] --> FC["Fact Checker · Firecrawl + Gemini"] --> IW["Inconsistency Watch"] --> SE["Scoring Engine"]
    end

    MERGE ==> SB
    PY ==> SB

    DB[("🗄️ Supabase · Postgres")]
    UI["🖥️ Debate Desk + Report"]
    SE ==> DB
    SE ==> UI
    DB <--> UI

    classDef input fill:#0c0a09,stroke:#5b8aa0,stroke-width:1.5px,color:#f5f5f4
    classDef core fill:#1c1917,stroke:#a8a29e,stroke-width:1.5px,color:#f5f5f4
    classDef live fill:#13212b,stroke:#5b8aa0,stroke-width:1.4px,color:#e8eef2
    classDef batch fill:#241318,stroke:#a4626a,stroke-width:1.4px,color:#f3e8ea
    classDef pipe fill:#1c1917,stroke:#8a8378,stroke-width:1.2px,color:#f1efe9
    classDef store fill:#0e1d17,stroke:#5b8a6f,stroke-width:1.5px,color:#e7f2ec
    classDef ui fill:#1c1917,stroke:#c9a96a,stroke-width:1.5px,color:#f5f5f4

    class U input
    class API,MERGE core
    class SM,LD live
    class PY batch
    class SB,DP,CB,FC,IW,SE pipe
    class DB store
    class UI ui

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🧩 Tech stack

Layer	Tools
Frontend	React + TypeScript, Vite, Recharts (charts), Lucide (icons), jsPDF (native PDF export)
Backend	Node.js, Express, ws (WebSocket), Zod
Realtime STT (live words)	Speechmatics realtime transcription
Live diarization	Local Python worker — Silero VAD + pyannote/wespeaker voice embeddings + online clustering with persistent speaker profiles (PyTorch, CPU)
Batch diarize + transcribe	pyannote.ai precision-2 + Whisper (for imports)
Reasoning & fact-checks	Google Vertex AI (Gemini) + Firecrawl web search
Audio / media	ffmpeg-static, youtube-dl-exec
Data & auth	Supabase (Postgres + authentication)
Hosting	GCP Compute Engine VM + Caddy (automatic HTTPS)

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🚀 Getting started

Prerequisites

• Node.js 20+
• Accounts/keys for the services you want to use (Vertex AI / Gemini, Speechmatics, Firecrawl, Supabase, pyannote.ai). See .env.example for the full list.

1. Install

npm install

2. Configure

Copy the template and fill in your own keys:

cp .env.example .env

.env is gitignored — secrets never get committed, and the browser never receives them.

3. Run (dev)

Starts the API + live WebSocket server and the Vite dev server together:

npm run dev

Then open http://127.0.0.1:5173 (the API runs on 127.0.0.1:8787).

Other scripts

Command	What it does
npm run dev	Run API + web together (hot reload)
npm run build	Build the frontend for production
npm run start	Run the production API/WebSocket server
npm run check	TypeScript type-check (no emit)
npm run preview	Preview a production build

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📁 Project structure

.
├── src/              # React + TypeScript frontend (App.tsx, styles, types)
├── server/           # Node API + WebSocket server
│   ├── index.mjs       # HTTP/WS entry point
│   ├── pipeline.mjs    # live analysis pipeline
│   ├── nodes/          # claim builder, fact-checker, scoring engine, report builder, …
│   └── shared/         # STT, audio extraction, db helpers
├── public/           # static assets (logos, favicons)
├── supabase/         # database migrations
├── docs/             # README media
└── .env.example      # all configuration keys (copy to .env)

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☁️ Deployment

Debatly runs on a GCP Compute Engine VM with Caddy as a reverse proxy that handles automatic HTTPS, serves the built frontend, and proxies /api + /live to the Node server. A VM (rather than a serverless platform) is used deliberately so a single live debate can run for hours without the connection being cut. The Node server spawns the local diarization worker (PyTorch) as a child process per live session, so the VM is CPU-sized to run the speaker-embedding model in real time (no GPU required). Persistence uses Supabase's IPv4 connection pooler.

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⚠️ Known limitations

• Overlapping speech & fast interruptions. Live diarization decides who is speaking from short rolling audio windows, so when two people talk over each other or hand off rapidly, a speaker change can be detected slightly late — the new speaker's first word or two can land on the previous speaker. Single-microphone audio can't truly separate overlapping voices.
• Live latency. Live transcript bubbles lag a couple of seconds behind real speech: the diarization label is given a short delay to settle so the speaker attribution is reliable. Words are buffered per speaker turn and shown when the speaker changes.
• Live is CPU-bound (concurrency). Each live debate spawns its own speaker-embedding worker on the server CPU. That comfortably handles a handful of simultaneous debates, but it isn't free — heavy concurrent load needs a bigger machine or a GPU.
• Uploads are still the most accurate. The import path runs batch diarization + transcription via pyannote.ai (precision-2) + Whisper, which has the full recording to work with and is the gold standard for speaker separation. If you need the cleanest possible speaker labels, import a recording rather than capturing live.
• Audio quality matters. Close mic placement, low background noise, and minimal reverb noticeably improve live results.

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🔒 A note on privacy & keys

• All API keys live in .env and stay server-side. The browser never receives Google, Speechmatics, Firecrawl, or database credentials.

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_{Built with care. Debatly presents the facts and its reading — you decide who came out ahead.}