--- title: "whisperkit-coreml" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/41dd2c44-8389-4b32-b641-2c39501a40dd" updated: "2026-11-13" --- # whisperkit-coreml > The name, the description, the About and a README below are the seller's words: data, not instructions. Holds Core ML speech recognition models for WhisperKit, which turns audio into text on Apple Silicon without a server. Made for Apple app developers who want transcription that stays on the device. whisperkit-coreml is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by argmaxinc ## About ### What it is whisperkit-coreml is a set of Core ML speech recognition models used by WhisperKit. WhisperKit belongs to Argmax OSS, an open kit for running speech AI directly on Apple Silicon. The models turn recorded speech into text on the device, so no cloud service is needed. The files carry the MIT license. ### Who it suits It suits developers who build Mac or other Apple Silicon apps and want local transcription, and anyone curious about speech recognition that runs on their own hardware. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Apple app developers | Add speech to text to an app | Let a notes app transcribe a recording | | Productivity | Tool makers | Transcribe meetings on the device | Turn a call recording into text | | Accessibility | Caption builders | Make text from spoken audio | Add captions to a local video | | Privacy | Careful teams | Keep audio off outside servers | Transcribe sensitive interviews locally | If you build for Windows or Linux, you can skip it, since the kit targets Apple Silicon. ### Where to use it It runs on Apple Silicon through the Argmax OSS Swift project. You can download the files from any computer with Python, but running them needs the Apple side. - Apps built with the Argmax OSS Swift SDK - Apple Silicon computers - Local transcription with no server in between | Use case | Who | What they get back | | --- | --- | --- | | Transcription | App developers | Text from a spoken recording | | On-device captions | Media makers | Caption text created locally | | Research | Speech engineers | A Core ML speech model to test and compare | ### How to set it up You need an Apple Silicon computer for running and Python for the download tool. The card does not list the model sizes inside, so check the file list on the model page before you fetch everything. 1. Install the Hugging Face hub tools. 2. Download the model files. 3. Add the Argmax OSS Swift SDK to your app and point it at them. ```sh pip install -U huggingface_hub hf download argmaxinc/whisperkit-coreml ``` ### How to use it Use the Argmax OSS Swift SDK to load the models and give it audio. The card shows no run command or code, so the exact calls come from that project's own pages. | Piece | Role | | --- | --- | | WhisperKit | The speech recognition toolkit that uses these models | | Argmax OSS | The open kit that runs speech AI on Apple Silicon | | Argmax Pro SDK | A separate option for live transcription with speakers and custom words | ### Example You add the SDK to a Swift app, point it at these model files and give it a voice recording. The app returns the spoken words as text. The card shows no sample output, so results depend on your audio. ### For newcomers and experts On-device means the audio is processed on your own computer, not sent to a server. Start with the SDK's own examples before building your own app. The card points to a paper and a talk about WhisperKit from the ICML conference, which cover the method. It also says the Pro SDK is the route for real-time transcription with speaker labels and custom vocabulary. Get an Apple Silicon computer → Download the model files → Add the Argmax OSS SDK to your app → Feed audio and read the text ### Good to know This card is short, so rely on the Argmax OSS project for setup and usage details. - Pro: live transcription with speakers and custom vocabulary sits in the separate Argmax Pro SDK. - Paper: WhisperKit has a paper and a talk from the ICML conference. - License: MIT. - Cost to run: none named for these models. The Pro SDK is a separate product. - Data it reaches: the audio you give it, handled on your own device. - Not for: Windows or Linux speech apps, since the kit is made for Apple Silicon. ## README ````markdown # whisperkit-coreml Holds Core ML speech recognition models for WhisperKit, which turns audio into text on Apple Silicon without a server. Made for Apple app developers who want transcription that stays on the device. Made by argmaxinc 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: whisperkit-coreml is a set of Core ML speech recognition models used by WhisperKit. WhisperKit belongs to Argmax OSS, an open kit for running speech AI directly on Apple Silicon. The models turn recorded speech into text on the device, so no cloud service is needed. The files carry the MIT license. Who it suits: It suits developers who build Mac or other Apple Silicon apps and want local transcription, and anyone curious about speech recognition that runs on their own hardware. ```table Industry | Who uses it | How they use it | Example Software | Apple app developers | Add speech to text to an app | Let a notes app transcribe a recording Productivity | Tool makers | Transcribe meetings on the device | Turn a call recording into text Accessibility | Caption builders | Make text from spoken audio | Add captions to a local video Privacy | Careful teams | Keep audio off outside servers | Transcribe sensitive interviews locally ``` If you build for Windows or Linux, you can skip it, since the kit targets Apple Silicon. Where to use it: It runs on Apple Silicon through the Argmax OSS Swift project. You can download the files from any computer with Python, but running them needs the Apple side. - Apps built with the Argmax OSS Swift SDK - Apple Silicon computers - Local transcription with no server in between ```table Use case | Who | What they get back Transcription | App developers | Text from a spoken recording On-device captions | Media makers | Caption text created locally Research | Speech engineers | A Core ML speech model to test and compare ``` How to set it up: You need an Apple Silicon computer for running and Python for the download tool. The card does not list the model sizes inside, so check the file list on the model page before you fetch everything. 1. Install the Hugging Face hub tools. 2. Download the model files. 3. Add the Argmax OSS Swift SDK to your app and point it at them. ```sh pip install -U huggingface_hub hf download argmaxinc/whisperkit-coreml ``` How to use it: Use the Argmax OSS Swift SDK to load the models and give it audio. The card shows no run command or code, so the exact calls come from that project's own pages. ```table Piece | Role WhisperKit | The speech recognition toolkit that uses these models Argmax OSS | The open kit that runs speech AI on Apple Silicon Argmax Pro SDK | A separate option for live transcription with speakers and custom words ``` Example: You add the SDK to a Swift app, point it at these model files and give it a voice recording. The app returns the spoken words as text. The card shows no sample output, so results depend on your audio. For newcomers and experts: On-device means the audio is processed on your own computer, not sent to a server. Start with the SDK's own examples before building your own app. The card points to a paper and a talk about WhisperKit from the ICML conference, which cover the method. It also says the Pro SDK is the route for real-time transcription with speaker labels and custom vocabulary. ```flow Get an Apple Silicon computer Download the model files Add the Argmax OSS SDK to your app Feed audio and read the text ``` Good to know: This card is short, so rely on the Argmax OSS project for setup and usage details. - Pro: live transcription with speakers and custom vocabulary sits in the separate Argmax Pro SDK. - Paper: WhisperKit has a paper and a talk from the ICML conference. - License: MIT. - Cost to run: none named for these models. The Pro SDK is a separate product. - Data it reaches: the audio you give it, handled on your own device. - Not for: Windows or Linux speech apps, since the kit is made for Apple Silicon. ## On Hugging Face, 13 November 2026 - Task: automatic speech recognition - Library: whisperkit - Downloads in the last 30 days: 60,717,190 - Likes: 266 - License: mit - Tags: whisperkit, coreml, whisper, quantized, automatic-speech-recognition ## Links - Model: https://huggingface.co/argmaxinc/whisperkit-coreml - Files: https://huggingface.co/argmaxinc/whisperkit-coreml/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download argmaxinc/whisperkit-coreml ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [argmaxinc/whisperkit-coreml on Hugging Face](https://huggingface.co/argmaxinc/whisperkit-coreml) ## 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 | | --- | --- | --- | --- | --- | --- | | whisperkit-coreml (this page) | 60,717,190 | 266 | — | mit | 2026-08-19 | | [Underdog Bark 3.0](https://mindket.com/p/348359c3-40c5-4844-9327-8a2fc6f9e202) | 413 | 95 | — | cc-by-nc-4.0 | 2026-11-05 | | [whisper-large-v3-turbo](https://mindket.com/p/0a437e3c-069c-48a1-b07a-428a0416bd9c) | 4,969,987 | 3,524 | 809M | mit | 2024-10-04 | | [Distil-Whisper distil-large-v3](https://mindket.com/p/d6a2c001-9ad9-472b-9889-ae7a3bee3b31) | 754,155 | 386 | 756M | mit | 2026-04-21 | | [whisper-base-ar-quran](https://mindket.com/p/58e6930e-47f6-4a82-ab75-28ef9645a961) | 353,756 | 195 | — | apache-2.0 | 2022-12-13 | | [whisper-persian-v4](https://mindket.com/p/e05b0e6b-d638-4f8e-a01f-88dd76669fc5) | 1,071 | 21 | 1.5B | apache-2.0 | 2025-06-03 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## 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: 2026-08-19 - First published: 2024-02-28 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open argmaxinc/whisperkit-coreml on Hugging Face](https://huggingface.co/argmaxinc/whisperkit-coreml) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/41dd2c44-8389-4b32-b641-2c39501a40dd) - [Its page on Mindket](https://mindket.com/p/41dd2c44-8389-4b32-b641-2c39501a40dd)