Open-Source Otter AI Alternatives That Run on Your Own Machine
Seven open-source Otter.ai replacements, each with its licence named, plus setup effort and hardware needs. Sorted by job: live meetings, interview files, dictation, team server.
For live meeting notes with summaries, which is what most people used Otter for, install Meetily first. It is MIT-licensed, runs Whisper transcription locally on Windows and macOS, and records without sending a bot into the call. For interview files, Buzz is simpler. For a shared team server, use Speakr.
Every tool below has a public repository and a named licence, and we flag each paid "Pro" tier that sits on top of the open code. We haven't installed or used any of them: everything here comes from project READMEs, licence files, pricing pages and published discussion, checked in October 2026.
What you're replacing on Otter
Otter's free Basic plan caps you at 300 transcription minutes a month, allows 3 file imports for the life of the account, and limits each conversation. Scribbl, which makes a competing notetaker, reported in October 2026 that the free plan shows only the first 30 minutes of each conversation and your 25 most recent conversations. On the paid side, Pro is $8.33 a month billed annually or $16.99 month to month, and Business is $13.33 a month billed annually. Otter has changed these tiers more than once, so check its pricing page before you cancel.
Those caps send people looking. A journalist with a backlog of two-hour interviews uses up 3 imports on day one. Open-source tools have no minute meter, because you pay in your own CPU or GPU time. The trade-offs are covered after the list.
Comparison table: open source Otter AI alternatives
| Tool | Licence | Runs where | Meeting bot | Speaker labels | Summaries | Setup | Paid tier |
|---|---|---|---|---|---|---|---|
| Meetily | MIT (open-core) | Windows, macOS | No, local capture | Limited | Yes, local model or API key | Installer | Pro $10/user/mo (annual) |
| Speakr | AGPL-3.0 | Self-hosted server, browser | No | Yes, via WhisperX | Yes, OpenAI-compatible LLM | Docker | None |
| Buzz | MIT | macOS, Windows, Linux | No | Limited | No | Installer | Mac App Store build |
| WhisperX | BSD-2-Clause | Linux, Windows, macOS (CUDA preferred) | No | Yes, pyannote | No | pip, CLI | None |
| OpenWhispr | MIT (open-core) | macOS, Windows, Linux, iOS | No | No | No | Installer | Pro $8/mo |
| whisper.cpp | MIT | Nearly everything, CPU-friendly | No | Basic | No | Build or binary, CLI | None |
| Vosk | Apache 2.0 | Desktop, Android, Raspberry Pi | No | Basic | No | Library | None |
1. Meetily
Official website: meetily.ai
Of the open-source options, Meetily comes closest to a drop-in Otter replacement. It's a desktop app for Windows and macOS that records your Zoom, Teams or Google Meet calls on your own computer without joining as a participant. Its Community edition runs Whisper-based transcription locally, so audio and transcripts stay on the device. Summaries come from whichever model you choose: a local one, or a cloud model through your own API key.
The Community edition is MIT-licensed and, per Meetily's own comparison page, has no subscription fee or usage limit. The product is open-core, though. Meetily Pro costs $10 per user per month billed annually ($120 a year) and adds extra features, and Enterprise is priced on request.
Setup is a normal desktop installer, which makes it the easiest meeting-capture option here. Speed depends on your hardware. A recent Apple Silicon Mac or a machine with an NVIDIA GPU can run the larger Whisper models. An older laptop CPU will push you toward smaller, less accurate ones.
Meetily's vendor page says it is "GDPR/HIPAA compliant by design" and that its accuracy is comparable to or better than Otter's. Both are the vendor's own claims. The compliance line describes architecture (audio stays local), not a third-party certification. It also doesn't cover a cloud LLM you might send transcripts to for summaries.
Pricing: free and open source (MIT); Pro at $10/user/month billed annually. Best for: individuals who want Otter-style meeting notes with no bot and no cloud upload. Wrong for: Linux users, and anyone who needs notes from meetings they don't attend.
Pros: MIT licence, no meeting bot, local Whisper, choice of local or API-key LLM for summaries, simple installer. Cons: no Linux desktop build listed, speaker labelling weaker than Otter's, compliance wording is marketing rather than certification.
Try Meetily →2. Speakr
Official website: github.com/murtaza-nasir/speakr
Speakr is a self-hosted web app. Pick it if your team wants one shared server rather than software on every laptop. You deploy it with Docker, then upload recordings through the browser. It keeps a searchable library of transcripts and generates summaries.
Speakr doesn't transcribe anything itself. You connect it to a speech endpoint, either an OpenAI-compatible Whisper API or a self-hosted WhisperX service if you want speaker identification. Summaries run through an OpenAI-compatible LLM endpoint, which can be a cloud key or a local server. If both run locally, nothing leaves your network.
At the time of writing the repository is licensed AGPL-3.0. That is fully open, but if you modify the app and offer it to others as a network service, AGPL requires you to publish your changes. A company planning to customise it should read the licence file first.
Setup takes moderate to serious effort. You need a Linux box or VM and Docker. For usable speed with diarization, the machine running the speech service also needs a GPU.
Pricing: free, no paid tier. Best for: small teams, newsrooms or labs that want a private, multi-user Otter on their own hardware. Wrong for: a solo user with no server and no interest in Docker.
Pros: browser access for every user, speaker identification via WhisperX, summaries, fully local option, no per-seat cost. Cons: Docker and speech-endpoint wiring required, no live meeting capture, AGPL obligations if you modify and serve it.
Try Speakr →3. Buzz
Official website: buzzcaptions.com
Journalists on Reddit keep asking for a free way to transcribe interview recordings offline, and Buzz is the usual answer. It's an MIT-licensed desktop app for macOS, Windows and Linux that puts Whisper behind a normal graphical interface. You drag in an audio or video file, pick a model size, and export the transcript as plain text or as SRT or VTT subtitles.
It can run on several Whisper back ends, including whisper.cpp. It can also call the OpenAI API if you'd rather pay for speed than wait on a slow laptop. It offers live transcription from the microphone too, but files are its main job.
Buzz has no summaries and no meeting capture. It doesn't focus on speaker labels either, so a two-person interview comes out as one block of text that you split by hand. A reporter who mainly wants to keep a source's audio off third-party servers will usually accept that.
You download it, install it and run it. The developer also sells a paid build that helps fund the project, but the free GitHub release does the same core job.
Pricing: free (MIT); optional paid Mac App Store version. Best for: journalists, researchers and podcasters transcribing recorded files privately. Wrong for: anyone who needs meeting summaries or clean speaker separation.
Pros: MIT licence, runs on all three desktop OSes, plain installer, subtitle exports, offline by default. Cons: no summaries, limited speaker separation, no live meeting workflow.
Try Buzz →4. WhisperX
Official website: github.com/m-bain/whisperX
WhisperX is a command-line engine. It's the strongest open option when you need speaker labels on files, and it's BSD-2-Clause licensed. It transcribes on the faster-whisper backend, adds an alignment step for word-level timestamps, and then uses pyannote for speaker diarization.
The README promises large speed gains on a GPU. Those figures come from the maintainers' own hardware. The project is built around NVIDIA CUDA, and on a CPU it runs far slower.
Diarization comes with a catch. pyannote's models are gated on Hugging Face, so speaker labels only work once you have a Hugging Face account and an access token and have accepted the model's terms. The README also says diarization is imperfect, especially when speakers overlap. That applies to every open tool here, and it is where Otter's polished pipeline still wins.
You install it with pip into a Python environment. It exports TXT, SRT, VTT and JSON, among other formats. Speakr and other front ends use it as a back end, so learning it pays off if you later build a team server.
Pricing: free (BSD-2-Clause). Best for: technical users with an NVIDIA GPU who need speaker-labelled transcripts of interviews or panels. Wrong for: anyone who won't open a terminal, or who only has a CPU.
Pros: speaker diarization, word-level timestamps, fast on GPU, many export formats, powers other tools. Cons: command line only, needs a gated Hugging Face token for diarization, slow without a CUDA GPU.
Try WhisperX →5. OpenWhispr
Official website: openwhispr.com
OpenWhispr is for dictation. You press a hotkey, speak, and the text appears in whatever app has focus. The project is MIT-licensed, with builds for macOS, Windows and Linux plus an iOS app. It runs Whisper or Parakeet models on your device, or calls a cloud model through your own API key.
Some Otter users only ever used the app to dictate notes, and for them OpenWhispr is a more direct replacement than any meeting tool. It doesn't record meetings, label speakers or write meeting summaries.
OpenWhispr is open-core: the free local mode has no usage meter, and OpenWhispr Pro costs $8 a month. Its "private" and "unlimited" wording comes from the vendor's own Otter comparison page. Unlimited applies to local processing, and the privacy goes away once you route audio to a cloud key.
Setup is an installer. On older CPUs you'll want a small model, which costs accuracy on names and jargon.
Pricing: free (MIT) for local use; Pro at $8/month. Best for: people who used Otter for voice notes and dictation rather than meetings. Wrong for: anyone who needs meeting records.
Pros: MIT licence, widest platform list here including iOS, local or bring-your-own-key models, hotkey workflow. Cons: no meeting capture, no speaker labels, Pro tier blurs the free/paid line.
Try OpenWhispr →6. whisper.cpp
Official website: github.com/ggml-org/whisper.cpp
whisper.cpp is a C/C++ port of OpenAI's Whisper, MIT-licensed like the original. Pick this engine if you don't have an NVIDIA card. It runs on plain CPUs, accelerates on Apple Silicon, supports quantised models that use less memory, and builds for desktop and mobile platforms as well as small boards like the Raspberry Pi.
You compile it or download a release binary, fetch a model file, and run it from the terminal. It writes TXT, SRT, VTT and JSON. It has an experimental speaker-turn feature for limited cases, but nothing like WhisperX's full diarization.
Model size determines how usable it is. OpenAI's Whisper README lists approximate memory needs for its reference implementation, from about 1 GB for the tiny and base models up to about 10 GB for large. whisper.cpp needs less thanks to its leaner runtime and quantisation, which is why it's popular on laptops. Tiny and base run fast almost anywhere but struggle with accents and crosstalk. Whisper's accuracy reputation comes from the medium model and up.
Buzz and many other GUI apps can use whisper.cpp as a back end. Use it directly if you want to script batch transcription of a folder of interviews.
Pricing: free (MIT). Best for: developers and tinkerers transcribing files on Macs or CPU-only machines. Wrong for: anyone who wants a window and buttons.
Pros: MIT licence, no GPU needed, Apple Silicon acceleration, runs on almost any platform, scriptable. Cons: terminal only, no real diarization, no summaries.
Try whisper.cpp →7. Vosk
Official website: alphacephei.com/vosk
Vosk is an Apache-2.0 offline speech recognition toolkit from Alpha Cephei. It predates the Whisper wave and takes a lighter approach, with small models that run on a Raspberry Pi or an Android phone and bindings for several programming languages.
That small footprint is the reason to choose it. Vosk streams results in real time with low latency, so it suits developers building live captioning into their own app or working on hardware too weak for Whisper. It also has a basic speaker identification model.
For someone who just wants to replace Otter for themselves, it's the weakest choice here. It has no consumer app and no summaries. On long, messy meeting audio, Whisper-family models are generally regarded as more accurate.
Pricing: free (Apache 2.0). Best for: developers who need offline, real-time recognition on low-power hardware. Wrong for: anyone who wants a finished app.
Pros: Apache 2.0, small models, real-time streaming, runs on Raspberry Pi and Android, many language bindings. Cons: developer toolkit only, lower accuracy than Whisper on hard audio, no summaries.
Try Vosk →Which one to pick
Live meetings: Meetily. It's the only option here built to capture calls and summarise them. Interviews and recorded files: Buzz if you want a GUI. WhisperX if you need speaker labels and have an NVIDIA GPU. For a journalist on no budget, Buzz is the cheapest route: it's free and keeps audio on your laptop. Dictation and voice notes: OpenWhispr. Team server: Speakr, with WhisperX behind it. Building your own product: whisper.cpp for accuracy, Vosk for small footprints.
We ranked the list by how much of Otter's job each tool covers for an ordinary user (capture, transcript, speaker labels, summary), then by licence clarity, then by setup effort. That's why Speakr sits above Buzz: Buzz is easier, but Speakr adds a library, summaries, diarization and multiple users. OpenWhispr is fifth only because dictation is a narrower job. None of these tools pays us. The /go/ links are tracked outbound links and played no part in the order.
What local transcription gives up
No bot. None of these tools joins Zoom or Teams the way Otter's Notetaker does. Meetily records audio on your own machine, so you have to be in the call yourself with your computer on. That keeps uninvited bots out of meetings, but it also means nothing can attend a meeting you're skipping.
GPU needs. You don't need a GPU to run Whisper: whisper.cpp works on a CPU, and Apple Silicon can run mid-size models. You do need a decent GPU to run the large models fast enough to clear a backlog, and WhisperX's speed claims assume CUDA.
Accuracy and diarization. Accuracy drops on noisy rooms, phone audio and overlapping speakers, and weak hardware makes it worse by forcing you onto smaller models. Diarization is the weakest part of the open stack, as the WhisperX README admits.
Summary privacy. Summaries are only as private as the LLM behind them. A local model keeps everything in-house, and a cloud API key doesn't.
For journalists and lawyers, the privacy case is still strong. Audio that never leaves your laptop can't be exposed by a vendor breach or by a vendor's data policy. But "HIPAA by design" or "GDPR ready" is not a certification or a signed business associate agreement. None of the open projects here offers either, and compliance becomes your job once you self-host.
Closed-source fallbacks if self-hosting is too much work
If Docker files and model sizes sound like a weekend you don't have, the tools below are free to start. None of them is open source, whatever some listicles claim.
Fireflies is the closest Otter rival: a bot that joins calls and builds a searchable team archive, with CRM integrations aimed at sales teams. Between Otter and Fireflies, choose Fireflies if your team works in a CRM and Otter if you mainly want real-time transcription. Notta focuses on multilingual transcription and translation, and has a separate desktop Privacy Mode. Fyxer's comparison counts six languages for Otter, so Notta suits teams that record in several languages better. Fathom is free with unlimited recordings for individuals, according to Read AI's comparison, and team plans start at $19 per user per month. Tactiq takes bot-free notes from the browser, and Jamie records bot-free from your desktop, with Pro plans from $24 a month.
Moving your Otter archive out before you cancel
- Export the transcripts you need first, in every format your plan offers. Subtitle files keep timestamps, which helps if you re-align later.
- Download the original audio for anything you might re-transcribe with a better model or diarize with WhisperX.
- Check whether your plan allows bulk export. If it doesn't, paying for one more month while you export can save hours of clicking through files one at a time.
- Before cancelling, run a few of the exported audio files through your new tool and compare names and jargon against Otter's version.
- Disconnect Otter's calendar and meeting integrations so the Notetaker stops joining calls. Then cancel, and request account deletion if you want the data gone.
FAQ
Is Otter.ai being sued? Yes. A privacy class action filed in 2025 accuses Otter's Notetaker of recording meeting participants without their consent. According to Scribbl, a competitor, a federal judge let most of the case proceed on August 13, 2026. We haven't seen a final ruling as of October 2026, so check the court docket for the current status.
What is the best open-source audio transcription tool? For most people it's Whisper, run through an app. Use Buzz for files and Meetily for meetings. whisper.cpp is the best raw engine without a GPU, and WhisperX is the best one if you need speaker labels.
What are the best Otter.ai alternatives on GitHub? Meetily, Speakr, Buzz, WhisperX, OpenWhispr, whisper.cpp and Vosk all have public repositories under the licences in the table above. Meetily and OpenWhispr also sell paid tiers.
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Founder and editor of infobro.ai
We research every tool from its documentation, pricing pages, product screenshots and public developer discussion, and say plainly what the evidence does and does not show. Drafts are written with AI assistance under our editorial rules, and we revisit articles to reflect platform changes. Learn more about our process.
