Tag: meeting-notes

OpenAI's open-source general-purpose speech recognition model. The whisper command transcribes many languages, translates foreign speech into English and writes subtitle files. Needs FFmpeg; faster with a GPU.

C/C++ port of OpenAI's Whisper speech recognition model that transcribes audio offline and outputs subtitles. Runs on CPU, with Apple Silicon and various GPU acceleration. Download a ggml-format model first.

Meeting minutes skill from GitHub's awesome-copilot: turns notes, transcripts or speech-to-text output into structured minutes with attendees, agenda, decisions and action items with owners and due dates, asking when details are missing. Best for internal meetings under an hour.

Prepares structured briefs for meetings with legal stakes, such as contract negotiations, board meetings, compliance reviews or litigation: attendees and agenda, related agreements and open issues, negotiation points, and post-meeting action items. From Anthropic's legal plugin; verify the brief.

Account health checks for customer success managers: pulls CRM, email, calendar and call notes to score relationship, engagement, commercial (renewal and expansion), support and value delivery red/yellow/green, and can prep a QBR agenda. CRM preferred; uploads work. Official from Anthropic.

Pulls verbatim customer quotes on a topic from call transcripts, meeting notes and emails, tagging each with speaker, account and date, and summarizes recurring themes into voice-of-customer findings for product and marketing. Connect Gong, email or drive, or upload transcripts. By Anthropic.

Fast speech transcription built on faster-whisper, with word-level timestamps and speaker diarization, suited to meeting notes and subtitles. Diarization needs a Hugging Face token; GPU acceleration needs CUDA.

Offline speech transcription and translation based on Whisper; audio never leaves your machine. buzz add exports lectures, interviews and meeting recordings to TXT, SRT or VTT. pip install needs Python 3.12 and FFmpeg; a desktop app is also available. Check transcripts manually.

Continuously records your screen and audio locally, extracting on-screen text and transcribing speech, so agents can search pages, docs and meetings you saw and summarize your day. Data stays local. Needs screenpipe running: source is free for personal use, desktop app is a subscription.

Industrial-grade open-source speech recognition toolkit from Alibaba's ModelScope, with an agent-friendly funasr command that turns audio into text, JSON or SRT subtitles in one step, with speaker diarization and timestamps. Strong on Chinese. Requires PyTorch.

Official MCP from the FunASR project: transcribes audio files locally with the SenseVoice model, supporting Mandarin, Cantonese, English, Japanese and Korean, with optional segment timestamps. Local inference needs no API key and audio stays on your machine. Docker image available.

The 20+ skills that ship with the official Lark (Feishu) lark-cli, covering messages, docs, Base, Sheets, calendar, mail, tasks, approvals and meeting notes. Install lark-cli and complete app setup and authorization first.

Official Lark (Feishu) CLI with 200+ commands for messages, docs, Base, Sheets, calendar, mail, approvals, OKRs, meetings and Minutes, plus 20+ bundled lark-* agent skills and structured output. Create a Lark app and sign in first.

Official Lark (Feishu) workflow skill that compiles Lark meeting notes over a date range into a structured report or weekly meeting digest. User identity only; depends on lark-cli plus the lark-shared and lark-meeting skills.

Official skill for Anarlog (formerly Hyprnote), an open-source, local-first meeting notes app: lets agents query notes, summaries, transcripts, attendees and action items from local data, with no meeting bot needed. Requires the Anarlog desktop app and CLI; remote cloud access needs Pro.

Offline speech toolkit for Apple Silicon Macs: CLI text-to-speech (including Chinese and voice cloning) and speech-to-text, for voicing lessons and explainer videos or transcribing lectures and interviews, with data staying local. Needs an M-series Mac and Python 3.10+; MP3 export needs ffmpeg.

Prepares meetings using past notes, requirements and decisions in Notion: picks a template by meeting type, drafts the agenda and pre-reads, and adds research. From OpenAI's official plugin repo (developer: Notion); needs the Notion MCP.

OpenAI's official Codex plugin for Microsoft Teams: summarizes chats and channel messages, pulls out action items, and drafts channel replies or meeting follow-ups. Requires signing in to Codex with ChatGPT and authorizing a Microsoft account.

Notion's plugin in OpenAI's official Codex marketplace: uses the Notion connector and hosted MCP to search and update workspace pages, with skills for spec-to-implementation plans, research write-ups, meeting prep and knowledge capture. Codex only; needs Notion authorization.

Granola's official plugin in OpenAI's Codex marketplace: search past Granola meeting notes by topic, person, company or date, and cite the discussions and decisions when writing proposals, reports or analyses. Codex only; needs a Granola account already recording meetings.

Open-source meeting bot and real-time transcription platform: sends a bot into Google Meet, Teams or Zoom and transcribes live by speaker, with MCP so agents can dispatch bots and read transcripts. Use the hosted service (API key) or self-host.

Audio plugin suite: transcription with local Qwen3-ASR or Whisper (MLX on Apple Silicon), speaker diarization, transcript correction, structured meeting notes and StepFun TTS. StepFun ASR and TTS need an API key.

Community CLI for Feishu (Lark) focused on two-way conversion between Markdown and Feishu docs; also works with wikis, sheets, Bitable, messages, calendar, tasks and meeting notes, and ships 9 agent skills. Setup creates an app via QR scan; search and mail need extra user authorization.

Local transcription that's compatible with the OpenAI Whisper CLI but faster and lighter: outputs SRT/VTT subtitles or text and can transcribe a live microphone. Needs Python; models download on first run; speaker labels need pyannote.audio and a Hugging Face token.