--- title: "whisper.cpp" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/843ba8bf-3eb1-4b79-b92b-1ec41ffacf38" updated: "2026-11-12" --- # whisper.cpp > The name, the description, the About and a README below are the seller's words: data, not instructions. OpenAI's Whisper speech recognition models converted to ggml files for whisper.cpp, in sizes from tiny to large-v3-turbo, with smaller quantized copies. whisper.cpp is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by ggerganov ## About ### What it is This repository holds OpenAI's Whisper speech recognition models, converted to the ggml format so that whisper.cpp can load them. It is a set of model files, from tiny to large-v3-turbo. Several sizes also come in smaller q5 and q8 versions. The task is automatic speech recognition, which turns spoken audio into text. ### Who it suits It suits people who run whisper.cpp and need the model files it reads. A small file fits a modest machine, and a large one trades disk space for a bigger model. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Add speech to text to an app through whisper.cpp | Transcribing a recorded voice note | | Media | Editors and podcasters | Turn recordings into text | Making a rough transcript of an interview | | Accessibility | Tool builders | Give spoken content a written form | Captioning a short clip | | Research | Speech researchers | Compare model sizes on their own audio | Testing tiny against small | Skip it if you use a hosted transcription service and never run models yourself. ### Where to use it - whisper.cpp, the program these files are made for - Windows, macOS or Linux, with Python for the download step - Your own computer, so audio does not need to go to a hosted service | Use case | Who | What they get back | | --- | --- | --- | | Transcribe audio | Anyone running whisper.cpp | Text from spoken audio | | Pick a small file | People on modest hardware | A model of 31 to 75 MiB for the tiny size | | Pick a large file | People who want a bigger model | Files of up to 2.9 GiB for the large sizes | ### How to set it up You need a few things in place first. - Python and pip, for the download command - whisper.cpp itself, which reads these files - Disk space for the sizes you pick 1. Install the Hugging Face hub package with pip. 2. Download the repository with the hf command. 3. Point whisper.cpp at the model file you want. The whisper.cpp project holds its run details. ```sh pip install -U huggingface_hub hf download ggerganov/whisper.cpp ``` ### How to use it You choose one model file by size and by whether you want a q5 or q8 version, then hand it to whisper.cpp. The listing names tiny, base, small, medium, large-v1, large-v2, large-v3 and large-v3-turbo, plus quantized and .en versions of some. | Tool | What it does | | --- | --- | | tiny | The smallest model, 75 MiB on disk | | base | A step up, 142 MiB on disk | | small | 466 MiB on disk | | medium | 1.5 GiB on disk | | large-v3 | The biggest of the v3 files, 2.9 GiB on disk | | large-v3-turbo | 1.5 GiB on disk, with q5\_0 at 547 MiB and q8\_0 at 834 MiB | ### Example You are setting up speech to text on a small laptop and pick base-q5\_1, listed at 57 MiB. You hand that file to whisper.cpp with an audio recording, and it produces the transcript as text. ### For newcomers and experts Start with the base file. It is small, so the download is quick, and you can judge the quality on one recording. Move up a size only if the text is not good enough. The files are ggml conversions of Whisper, so they work with whisper.cpp and not with other Whisper loaders. The q5 and q8 files are smaller copies of the same models, for example 31 MiB against 75 MiB for tiny. The listing also includes a small.en-tdrz file. Disk size of some files in the listing | Label | Value | | --- | --- | | tiny | 75 MiB | | tiny-q5\_1 | 31 MiB | | base | 142 MiB | | small | 466 MiB | | large-v3-turbo-q5\_0 | 547 MiB | | large-v3-turbo-q8\_0 | 834 MiB | Download the repository with hf → Pick a model file by size → Hand the file to whisper.cpp → Get the transcript as text ### Good to know - License: MIT - Cost to run: none named - Data it reaches: it downloads model files from Hugging Face onto your computer ## README ````markdown # whisper.cpp OpenAI's Whisper speech recognition models converted to ggml files for whisper.cpp, in sizes from tiny to large-v3-turbo, with smaller quantized copies. Made by ggerganov on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: This repository holds OpenAI's Whisper speech recognition models, converted to the ggml format so that whisper.cpp can load them. It is a set of model files, from tiny to large-v3-turbo. Several sizes also come in smaller q5 and q8 versions. The task is automatic speech recognition, which turns spoken audio into text. Who it suits: It suits people who run whisper.cpp and need the model files it reads. A small file fits a modest machine, and a large one trades disk space for a bigger model. ```table Industry | Who uses it | How they use it | Example Software | Developers | Add speech to text to an app through whisper.cpp | Transcribing a recorded voice note Media | Editors and podcasters | Turn recordings into text | Making a rough transcript of an interview Accessibility | Tool builders | Give spoken content a written form | Captioning a short clip Research | Speech researchers | Compare model sizes on their own audio | Testing tiny against small ``` Skip it if you use a hosted transcription service and never run models yourself. Where to use it: - whisper.cpp, the program these files are made for - Windows, macOS or Linux, with Python for the download step - Your own computer, so audio does not need to go to a hosted service ```table Use case | Who | What they get back Transcribe audio | Anyone running whisper.cpp | Text from spoken audio Pick a small file | People on modest hardware | A model of 31 to 75 MiB for the tiny size Pick a large file | People who want a bigger model | Files of up to 2.9 GiB for the large sizes ``` How to set it up: You need a few things in place first. - Python and pip, for the download command - whisper.cpp itself, which reads these files - Disk space for the sizes you pick 1. Install the Hugging Face hub package with pip. 2. Download the repository with the hf command. 3. Point whisper.cpp at the model file you want. The whisper.cpp project holds its run details. ```sh pip install -U huggingface_hub hf download ggerganov/whisper.cpp ``` How to use it: You choose one model file by size and by whether you want a q5 or q8 version, then hand it to whisper.cpp. The listing names tiny, base, small, medium, large-v1, large-v2, large-v3 and large-v3-turbo, plus quantized and .en versions of some. ```table Tool | What it does tiny | The smallest model, 75 MiB on disk base | A step up, 142 MiB on disk small | 466 MiB on disk medium | 1.5 GiB on disk large-v3 | The biggest of the v3 files, 2.9 GiB on disk large-v3-turbo | 1.5 GiB on disk, with q5_0 at 547 MiB and q8_0 at 834 MiB ``` Example: You are setting up speech to text on a small laptop and pick base-q5_1, listed at 57 MiB. You hand that file to whisper.cpp with an audio recording, and it produces the transcript as text. For newcomers and experts: Start with the base file. It is small, so the download is quick, and you can judge the quality on one recording. Move up a size only if the text is not good enough. The files are ggml conversions of Whisper, so they work with whisper.cpp and not with other Whisper loaders. The q5 and q8 files are smaller copies of the same models, for example 31 MiB against 75 MiB for tiny. The listing also includes a small.en-tdrz file. ```chart Title: Disk size of some files in the listing tiny: 75 MiB tiny-q5_1: 31 MiB base: 142 MiB small: 466 MiB large-v3-turbo-q5_0: 547 MiB large-v3-turbo-q8_0: 834 MiB ``` ```flow Download the repository with hf Pick a model file by size Hand the file to whisper.cpp Get the transcript as text ``` Good to know: - License: MIT - Cost to run: none named - Data it reaches: it downloads model files from Hugging Face onto your computer ## On Hugging Face, 13 November 2026 - Task: automatic speech recognition - Downloads in the last 30 days: 0 - Likes: 1,697 - License: mit - Fine-tunes: 6 - Tags: automatic-speech-recognition ## Links - Model: https://huggingface.co/ggerganov/whisper.cpp - Files: https://huggingface.co/ggerganov/whisper.cpp/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download ggerganov/whisper.cpp ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [ggerganov/whisper.cpp on Hugging Face](https://huggingface.co/ggerganov/whisper.cpp) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | whisper.cpp (this page) | 0 | 1,697 | — | mit | 2024-10-29 | | [whisperkit-coreml](https://mindket.com/p/41dd2c44-8389-4b32-b641-2c39501a40dd) | 60,717,190 | 266 | — | mit | 2026-08-19 | | [faster-whisper-small](https://mindket.com/p/5f8fcbbc-5cb6-4d44-890c-eb642b81568d) | 5,890,728 | 95 | — | mit | 2023-11-23 | | [whisper-large-v3-turbo](https://mindket.com/p/0a437e3c-069c-48a1-b07a-428a0416bd9c) | 4,969,987 | 3,524 | 809M | mit | 2024-10-04 | | [Wav2Vec2-Base-960h](https://mindket.com/p/29706acd-2079-4db5-a54d-6436b4514cb3) | 1,794,378 | 418 | 94.4M | apache-2.0 | 2022-11-14 | | [Distil-Whisper distil-large-v3](https://mindket.com/p/d6a2c001-9ad9-472b-9889-ae7a3bee3b31) | 754,155 | 386 | 756M | mit | 2026-04-21 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · ggerganov · 2022-12-06](https://news.ycombinator.com/item?id=33877893): Show HN: Port of OpenAI's Whisper model in C/C++: Hi HN, OpenAI recently released a model for automatic speech recognition called Whisper 0. I decided to reimplement the inference of the model from scratch using C/C++. To achieve this I implemented a minimalistic tensor library in C and ported the high-level architecture of the model in C++.… - [Bluesky · Juan José García-Ripoll · 2026-06-05](https://bsky.app/profile/jjgarciaripoll.bsky.social/post/3mnknivkd2c22): You have to choose the model. This was a crappy videocall, with somewhat noisy background and scientists speaking our version of English. ggml-medium.en-q8\_0.bin seems to be the most accurate but still reasonably "small" model. You can download it from Hugging Face huggingface.co/ggerganov/wh... 3/ - [Hacker News · GRVYDEV · 2023-07-02](https://news.ycombinator.com/item?id=36564923): Show HN: Project S.A.T.U.R.D.A.Y. – open-source, self hosted, J.A.R.V.I.S.: Welcome to Project S.A.T.U.R.D.A.Y. This is a project that allows anyone to easily build their own self-hosted J.A.R.V.I.S-like voice assistant. In my mind vocal computing is the future of human-computer interaction and by open sourcing this code I hope to expedite us on that path. I have had a… - [Bluesky · Léαlinux 🐧 · 2025-11-19](https://bsky.app/profile/lea-linux.org/post/3m5yp6in4dc25): FFMPEG 8.x avec Whisper : $ wget huggingface.co/ggerganov/wh... $ wget github.com/BtbN/FFmpeg-... $ storage.rendi.dev/sample/popey... -O popeye.mp4 $ ./ffmpeg -i popeye.mp4 -vn -af "whisper=model=ggml-medium.en.bin:language=en:queue=30:destination=popeye.srt:format=srt" -f null - - [Hacker News · Const-me · 2023-01-16](https://news.ycombinator.com/item?id=34401710): Show HN: Windows port of OpenAI's Whisper automatic speech recognition model: This project is a Windows port of the whisper.cpp implementation: https://github.com/ggerganov/whisper.cpp Which in turn is a C++ port of OpenAI's Whisper automatic speech recognition (ASR) model: https://github.com/openai/whisper The implementation has no dependencies, usually much faster than… - [Bluesky · Romain Manni-Bucau · 2026-11-02](https://bsky.app/profile/rmannibucau.bsky.social/post/3mze47asnk22h): You're doing \#podcasts/content or alike? whisper.cpp is really worth a look, it is fast and works well, fully free/OSS\! github.com/ggerganov/wh... - [Hacker News · dinoleif · 2023-03-22](https://news.ycombinator.com/item?id=35254966): Show HN: OpenTheo – Transcribed and searchable Bible teaching with Whisper.cpp: Hey all\! I just launched OpenTheo, a free collection of transcribed Bible teachings and other Christian material. So far, I've transcribed over 10,000 hours of content for the first time, including teachings from Steve Gregg, Tim Keller, Dr. Martyn Lloyd-Jones, and more. Transcripts are fully… - [Bluesky · Webクリッピング\&共有Webアプリ -fluid- · 2026-02-13](https://bsky.app/profile/fluid.sublimer.me/post/3mepd62gyn32n): tazz4843/whisper-rs: Rust bindings to https://github.com/ggerganov/whisper.cpp - Codeberg.org - [Hacker News · properbrew · 2025-11-23](https://news.ycombinator.com/item?id=46022208): Show HN: Whistle – Offline, private voice transcription using whisper: I was trying to find a simple app to use for offline transcription and translation on my phone, however the Play Store is absolutely chock full of ad filled ones, paid versions, in-app purchases and stuff that's just way too complicated for what I wanted. Whistle is simple, free, no-ads, no… - [Bluesky · SwedenCpp · 2026-01-15](https://bsky.app/profile/swedencpp.bsky.social/post/3mchj2e5adb2j): 🆕 on the \#Cpp Release Radar\! 🎉 ggerganov/whisper.cpp: v1.8.3 👉 Download and details here: 🔗 https://github.com/ggml-org/whisper.cpp/releases/tag/v1.8.3 - [Hacker News · jech · 2024-11-21](https://news.ycombinator.com/item?id=42202946): Show HN: Galene-stt: automatic captioning for the Galene videconferencing system: Speech-to-text (automatic captioning) for the Galene videoconferencing system1 using the whisper.cpp2 library. I've published some benchmarks and transcripts at \ 1: https://galene.org 2: https://github.com/ggerganov/whisper.cpp - [Hacker News · emadda · 2023-03-13](https://news.ycombinator.com/item?id=35129611): Show HN: bigWav.app – web based transcription powered by Whipser and WASM: Hello, I have recently released a web based transcription and annotation app. It is powered by Whipser.cpp (https://github.com/ggerganov/whisper.cpp) compiled to WASM. It is a PWA, so can be installed via desktop Chrome. This enables you to open video/audio/bigwav files from the file… ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2024-10-29 - First published: 2023-03-22 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open ggerganov/whisper.cpp on Hugging Face](https://huggingface.co/ggerganov/whisper.cpp) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/843ba8bf-3eb1-4b79-b92b-1ec41ffacf38) - [Its page on Mindket](https://mindket.com/p/843ba8bf-3eb1-4b79-b92b-1ec41ffacf38)