惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
U
Unit 42
Google DeepMind News
Google DeepMind News
博客园 - 司徒正美
Y
Y Combinator Blog
F
Fortinet All Blogs
云风的 BLOG
云风的 BLOG
T
Tailwind CSS Blog
G
Google Developers Blog
酷 壳 – CoolShell
酷 壳 – CoolShell
罗磊的独立博客
D
DataBreaches.Net
T
The Blog of Author Tim Ferriss
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
MyScale Blog
MyScale Blog
N
Netflix TechBlog - Medium
Microsoft Security Blog
Microsoft Security Blog
GbyAI
GbyAI
P
Proofpoint News Feed
Jina AI
Jina AI
B
Blog RSS Feed
腾讯CDC
阮一峰的网络日志
阮一峰的网络日志
D
Docker

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant
How to Transcribe Meetings Locally in 2026 (Whisper, On-D...
Mohammed Ali Chherawalla · 2026-06-25 · via DEV Community

OpenAI's Whisper model runs accurate speech-to-text on a laptop GPU, and the C++ port runs it without Python or a cloud account. That capability sits unused while teams pay per-minute fees to upload their calls to a transcription service. Off Grid AI Desktop is a free, open-source app that records and transcribes your meetings directly on your Mac or PC.

GitHub →

Free, open-source, runs offline. No account, no API key, no telemetry.

What This Gets You

You want a searchable record of what was said, not a subscription to a notetaker bot. Here is the difference local transcription makes.

The audio never leaves your machine, so a confidential call stays confidential. There is no per-minute meter, so a three-hour planning session costs the same as a five-minute standup, which is nothing. The transcript and summary save to your disk, so you own the record instead of renting access to it.

What You Need

Whisper comes in several sizes. The bigger ones transcribe better and want more memory.

Tier macOS Windows RAM / VRAM Free disk
Minimum Apple Silicon M1 Any modern CPU or iGPU 8 GB 10 GB
Recommended M2 / M3 / M4 NVIDIA RTX (6 GB+ VRAM) 16 GB+ 20 GB

On the minimum tier, a smaller Whisper model transcribes a one-hour call in a few minutes after it ends. The recommended tier runs the larger, more accurate models and finishes faster. CPU transcription works everywhere; a GPU just speeds it up.

How the Meeting Recorder Works

The recorder captures three streams at once: the screen video, the system audio coming out of your speakers, and your microphone. That covers both sides of a Google Meet or Zoom call, the remote participants on system audio and you on the mic.

When you stop, the bundled whisper.cpp engine transcribes the captured audio locally. There is no upload step. The transcription is the same model the cloud services use, running on your own hardware.

After the transcript is ready, the local LLM reads it and generates a title, a summary, and a list of the people mentioned. You get a usable meeting note without rewatching the recording or paying a notetaker.

Why On-Device Transcription Is Viable Now

Whisper ships as quantized weights, which stores the model at lower precision so it fits in consumer RAM without losing meaningful accuracy. That is the change that moved good transcription off the cloud and onto a laptop.

On macOS, transcription runs through Metal against Apple Silicon's unified memory. On Windows, you get CUDA on NVIDIA cards or a CPU path when there is no GPU. Either way the math runs locally, so the longer a call runs, the more you save versus a per-minute service.

Getting Cleaner Transcripts

Audio quality drives transcription quality more than model size does. Use a headset or a decent mic so your own track is clean, and the result improves immediately.

Pick the model to match the job. A smaller Whisper model is fine for a quick internal sync where you just need the gist. Reach for a larger model when accuracy matters, like a client call you will quote from later. Recording system audio captures the remote side at the source, which beats a microphone picking it up off your speakers.

Privacy: Stronger Than a Cloud Notetaker

A cloud notetaker uploads your full meeting audio and video to a server you do not control. The recording, the transcript, and the summary all live there. Off Grid AI Desktop keeps every part of that on your machine, because there is no server in the path.

It is AGPL-3.0 licensed, so the code is auditable. There is no account and no telemetry. The recorder requires an explicit start and stop, and a visible recording indicator stays on the whole time, so nothing records silently in the background.

Getting Started

  1. Download or clone from the GitHub repo.
  2. Install and launch the app on your Mac or PC.
  3. Open the Models browser and download a Whisper model plus a local LLM for summaries.
  4. Start a recording before your call, confirm the indicator is on.
  5. Stop when the call ends. Read the transcript, title, and summary.
git clone https://github.com/off-grid-ai/desktop
cd desktop
npm install
npm run dev

What's Coming

  • Cross-device sync so meeting notes follow you between machines.
  • Unified search across transcripts and the rest of your captured work.
  • More transcription models as new open-weight releases ship.

FAQ

Q: Is it really free?

Yes. The app is free and open-source under AGPL-3.0. There are no per-minute charges and no subscription.

Q: Does it work offline?

Yes. Recording and transcription both run with no network connection.

Q: Does it work with Zoom and Google Meet?

Yes. It records the screen, system audio, and mic, so it captures both sides of any call in your browser or a desktop client.

Q: How accurate is the transcription?

It runs Whisper, the same model behind many cloud transcription tools. Accuracy depends on the model size you pick and your audio quality.

Q: How much RAM do I need?

8 GB runs a smaller Whisper model. 16 GB or more is comfortable for the larger, more accurate ones.

Q: Is my meeting audio private?

Yes. The audio, transcript, and summary stay on your machine. Nothing uploads.

Transcribe your calls on hardware you already own. GitHub →