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FloatNote 1.0.0

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@Parth-Gupta-github Parth-Gupta-github released this 27 Jul 15:03
· 13 commits to main since this release
v1.0.0
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FloatNote 1.0.0 🎙️

Be present in the meeting — FloatNote remembers it for you.

FloatNote is a Windows desktop app that captures a meeting as it happens — every
word, who said it, and what's on screen — and turns it into a clean transcript,
an on-demand summary, and a chatbot you can actually ask about it. It's built
local-first: the recording, transcription, speaker separation, and screen
reading all run on your own machine, and only the final summary/chat step reaches
a language model.

⭐ At a glance

Capability Powered by Runs
Live transcription (mic + system audio) OpenAI Whisper 🖥️ On your PC
Speech / silence gating Silero VAD 🖥️ On your PC
Speaker separation & labels Resemblyzer voice embeddings 🖥️ On your PC
Screen Reader (OCR) OpenCV + Tesseract (bundled) 🖥️ On your PC
Keywords & action items spaCy 🖥️ On your PC
Summaries · chat · keyword cleanup Qwen2.5-7B-Instruct ☁️ HuggingFace
Meeting history SQLite 🖥️ On your PC

🧩 A closer look at the features

🎤 Two-way live transcription

Captures your microphone and the meeting's system audio at the same time, so
your side and the remote participants both land in a single transcript. You can
mute either stream independently mid-meeting without stopping the recording.

🗣️ Automatic speaker separation

Tells voices apart as the meeting runs — no need to process the whole
recording afterward — and tags each line (Speaker 1, Speaker 2, …). You can
rename a speaker to a real name and it sticks for that meeting.

🖥️ Screen Reader (OCR)

Watches your screen and, only when the content actually changes, reads the
text off it — slides, shared docs, whiteboards — and adds it to the meeting as
its own stream, separate from the spoken transcript. Tesseract is bundled, so
there's nothing extra to install.

✅ Keywords & action items

A lightweight language pipeline picks out the key topics and detects
tasks and who they're for in real time, so follow-ups don't slip through.

🧠 Summaries & chat — one model, zero fuss

A single language model (Qwen2.5-7B via HuggingFace) powers the on-demand
summary, the chatbot, and keyword cleanup — so there's just one setup and
one place your data goes. The chatbot answers only from the saved meeting, so
it stays grounded instead of guessing.

💾 Everything saved locally

Meetings, transcripts (tagged by mic / speaker / screen), speaker names, and
action items are stored in a local database you own — ready to summarize or
revisit any time.

🚀 Install & first run

  1. Download FloatNote Setup 1.0.0.exe from the Assets below and run it.
  2. SmartScreen? If Windows warns "unknown publisher," it's just because the
    build isn't code-signed yet — click More info → Run anyway.
  3. First launch downloads the speech models once (~250 MB) — give it a minute.
  4. Add a token when prompted: a free HuggingFace token powers the summary and
    chat. Create one at
    huggingface.co/settings/tokens with
    the "Make calls to Inference Providers" permission. It's stored only on
    your device.

🎬 Using it, step by step

  1. Optionally name the session, then hit ▶ Start Meeting.
  2. The transcript streams in with speaker labels; keywords and action
    items     appear live. Have a slide up? The Screen Reader grabs its text too.
  3. Mute the mic or system audio whenever you need to go off the record.
  4. Hit ⏹ Stop to end the session.
  5. Press Generate Summary for the recap, then chat with the meeting to
    pull out specifics.
  6. Your next meeting starts on a clean slate.

🏗️ How it's built

FloatNote ships as one installer that carries both the interface and the whole
AI engine — no Python, no manual setup on the user's side.

  • Desktop shell — Electron. On launch it starts the local engine, waits for
    it, then loads the interface; on exit it shuts the engine down.
  • Interface — React + Vite + Tailwind CSS.
  • Engine — a FastAPI (Python) service frozen into a standalone .exe with
    PyInstaller, streaming to the UI over a WebSocket.
  • On-device AI — Whisper (transcription), Silero (voice detection),
    Resemblyzer (speaker matching), OpenCV + Tesseract (screen OCR), and spaCy
    (action items) all run offline.
  • Cloud AI — only summaries and chat call out, to the Qwen2.5-7B model on
    HuggingFace.
  • Storage — a local SQLite database via async SQLAlchemy.
 speak / share screen
        │
        ▼
 ┌──────────────── local engine (on your PC) ────────────────┐
 │  Whisper + Silero + Resemblyzer   →  transcript + speakers │
 │  OpenCV + Tesseract               →  on-screen text        │
 │  spaCy                            →  keywords + action items│
 └───────────────┬───────────────────────────┬───────────────┘
                 ▼                           ▼
          live to the app             saved to SQLite
                 │
                 ▼
   "summarize" / "ask a question"  →  Qwen2.5-7B (HuggingFace)  →  answer

✅ Requirements

  • Windows 10 or 11, 64-bit
  • ~2.5 GB free disk space (the app bundles its full AI runtime)
  • Internet — for the one-time model download and for summaries/chat
  • A microphone for your own voice (remote audio is captured automatically)

🔒 Privacy

  • Nothing is captured until you press Start.
  • Audio, speaker analysis, screen text, and meeting history all stay on your
    device    . Only the text needed for a summary or chat reply is sent to the
    model, and your token never leaves your machine otherwise.
  • The FloatNote window is excluded from screen capture, so the Screen Reader
    never reads its own interface and the app won't appear in recordings.

⚠️ Known limitations

  • Windows only for now (system-audio capture is Windows-specific).
  • Not code-signed yet — expect the one-time SmartScreen prompt.
  • Summaries and chat need an internet connection and a HuggingFace token.
Assets 5
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