--- title: "Whistle" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/2a83a355-5760-461d-9833-a5d15efc8cc8" updated: "2026-11-12" --- # Whistle > The name, the description, the About and a README below are the seller's words: data, not instructions. Transcribes speech in seven languages with word timestamps and speech embeddings, in a model only 16.9 MB in size. Runs from a short Python script, for developers adding speech to apps. Whistle 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 Cactus-Compute ## About ### What it is Whistle is a tiny speech model from Cactus Compute. It turns audio into text, gives start and end times for words, and produces speech embeddings, all from a model of about 16.9 MB. It covers English, German, French, Spanish, Italian, Dutch and Polish. On first use, a Python package fetches the engine and the model weights and keeps a local copy. ### Who it suits Developers who want speech features inside an app or on a small device, and people who care about a small download and local processing. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | App developers | Call one function to transcribe audio | Turn a recorded clip into text | | Devices | Teams building for small hardware | Run speech recognition with a small footprint | Add voice input to a compact gadget | | Media | Editors and archivists | Get word timings from recordings | Mark where each word starts and ends | | Research | Speech researchers | Use speech embeddings from a small model | Compare clips by their embeddings | Skip it if you need a language outside the seven listed, since the card does not cover others. ### Where to use it - Python programs on Windows, macOS or Linux - A browser demo on the Whistle website - Microphone capture, after installing the extra package option - Platforms listed in the Cactus runtime repository | Use case | Who | What they get back | | --- | --- | --- | | Transcription | Developers | The spoken text of a 16 kHz WAV file | | Word timestamps | Subtitle and editing teams | Start and end times for each word | | Speech embeddings | Researchers and search builders | Vectors that describe the audio | ### How to set it up - Python and pip - A 16 kHz WAV file to transcribe - Internet access the first time, so the package can fetch and cache the engine and weights 1. Install the cactus-compute package. 2. Put a 16 kHz WAV file where your script can reach it. 3. Run a short script that calls the transcribe function. ```sh pip install -U cactus-compute ``` ### How to use it Import cactus and call transcribe with the path of a WAV file, then print the text from the result. For other sample rates or microphone input, install the package with its mic option. ```python import cactus print(cactus.transcribe("clip.wav")["text"]) ``` | Tool | What it does | | --- | --- | | cactus.transcribe | Transcribes an audio file and returns a result that holds the text | | whistle.cact | The model file this repository provides | | checkpoints/whistle.safetensors | The checkpoint file this repository provides | ### Example You save a short recording as clip.wav at 16 kHz and run the script above. It prints the words spoken in the clip as plain text. ### For newcomers and experts If you are new to speech recognition, install the package and run the three-line script on one short clip. Start with a clear recording in one of the seven languages. If you know the field, the model is a compact checkpoint of about 16.9 MB that handles transcription, word timestamps and speech embeddings. The repository ships it as a cact file and a safetensors checkpoint, and the card links guides for the Python API, speech use and fine-tuning. 16 kHz WAV file or microphone → cactus.transcribe call → Engine and weights cached on first use → Text, word times or embeddings ### Good to know - The default input is a 16 kHz WAV file - Runtime binaries and supported platforms are kept in the separate Cactus repository - Hugging Face lists 5 quantized and 13 fine-tuned versions made by other people - License: Apache 2.0 - Cost to run: none named - Data it reaches: the audio files or microphone input you give it, plus a first-use download of the engine and weights - Not for: other sample rates without the extra mic option, or languages outside the seven listed ## README ````markdown # Whistle Transcribes speech in seven languages with word timestamps and speech embeddings, in a model only 16.9 MB in size. Runs from a short Python script, for developers adding speech to apps. Made by Cactus-Compute 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: Whistle is a tiny speech model from Cactus Compute. It turns audio into text, gives start and end times for words, and produces speech embeddings, all from a model of about 16.9 MB. It covers English, German, French, Spanish, Italian, Dutch and Polish. On first use, a Python package fetches the engine and the model weights and keeps a local copy. Who it suits: Developers who want speech features inside an app or on a small device, and people who care about a small download and local processing. ```table Industry | Who uses it | How they use it | Example Software | App developers | Call one function to transcribe audio | Turn a recorded clip into text Devices | Teams building for small hardware | Run speech recognition with a small footprint | Add voice input to a compact gadget Media | Editors and archivists | Get word timings from recordings | Mark where each word starts and ends Research | Speech researchers | Use speech embeddings from a small model | Compare clips by their embeddings ``` Skip it if you need a language outside the seven listed, since the card does not cover others. Where to use it: - Python programs on Windows, macOS or Linux - A browser demo on the Whistle website - Microphone capture, after installing the extra package option - Platforms listed in the Cactus runtime repository ```table Use case | Who | What they get back Transcription | Developers | The spoken text of a 16 kHz WAV file Word timestamps | Subtitle and editing teams | Start and end times for each word Speech embeddings | Researchers and search builders | Vectors that describe the audio ``` How to set it up: - Python and pip - A 16 kHz WAV file to transcribe - Internet access the first time, so the package can fetch and cache the engine and weights 1. Install the cactus-compute package. 2. Put a 16 kHz WAV file where your script can reach it. 3. Run a short script that calls the transcribe function. ```sh pip install -U cactus-compute ``` How to use it: Import cactus and call transcribe with the path of a WAV file, then print the text from the result. For other sample rates or microphone input, install the package with its mic option. ```python import cactus print(cactus.transcribe("clip.wav")["text"]) ``` ```table Tool | What it does cactus.transcribe | Transcribes an audio file and returns a result that holds the text whistle.cact | The model file this repository provides checkpoints/whistle.safetensors | The checkpoint file this repository provides ``` Example: You save a short recording as clip.wav at 16 kHz and run the script above. It prints the words spoken in the clip as plain text. For newcomers and experts: If you are new to speech recognition, install the package and run the three-line script on one short clip. Start with a clear recording in one of the seven languages. If you know the field, the model is a compact checkpoint of about 16.9 MB that handles transcription, word timestamps and speech embeddings. The repository ships it as a cact file and a safetensors checkpoint, and the card links guides for the Python API, speech use and fine-tuning. ```flow 16 kHz WAV file or microphone cactus.transcribe call Engine and weights cached on first use Text, word times or embeddings ``` Good to know: - The default input is a 16 kHz WAV file - Runtime binaries and supported platforms are kept in the separate Cactus repository - Hugging Face lists 5 quantized and 13 fine-tuned versions made by other people - License: Apache 2.0 - Cost to run: none named - Data it reaches: the audio files or microphone input you give it, plus a first-use download of the engine and weights - Not for: other sample rates without the extra mic option, or languages outside the seven listed ## On Hugging Face, 13 November 2026 - Task: automatic speech recognition - Library: cactus-compute - Downloads in the last 30 days: 94,489 - Likes: 570 - License: apache-2.0 - Languages: en, de, fr, es, it, nl, pl - Quantized versions: 5 - Fine-tunes: 13 - Tags: cactus-compute, whistle, speech-recognition, speech-to-text, on-device, edge, quantization, webassembly, automatic-speech-recognition ## Links - Model: https://huggingface.co/Cactus-Compute/whistle - Files: https://huggingface.co/Cactus-Compute/whistle/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Cactus-Compute/whistle ``` 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 [Cactus-Compute/whistle on Hugging Face](https://huggingface.co/Cactus-Compute/whistle) ## 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 | | --- | --- | --- | --- | --- | --- | | Whistle (this page) | 94,489 | 570 | — | apache-2.0 | 2026-11-05 | | [Needle 3](https://mindket.com/p/1c09a900-c54d-42c8-8fe4-e0d3ded42834) | 147,784 | 411 | — | apache-2.0 | 2026-11-05 | | [Needle 2](https://mindket.com/p/783df142-0389-4901-b593-575f13265cbd) | 1,736 | 341 | — | apache-2.0 | 2026-09-03 | | [Underdog Bark 3.0](https://mindket.com/p/348359c3-40c5-4844-9327-8a2fc6f9e202) | 413 | 95 | — | cc-by-nc-4.0 | 2026-11-05 | | [Earlybird by sahilmahendrakar](https://mindket.com/p/63374aaf-908f-49b3-9cfc-811cda44e66e) | 89 | 24 | — | mit | 2026-11-11 | | [Ming-3.0-audio-flash](https://mindket.com/p/fd44df1d-31b5-4ff0-8479-ed2a012a793a) | 2,122 | 87 | 128B | mit | 2026-10-29 | Numbers as Mindket last read them, on 2026-11-12. ## 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 2026-11-12. Their authors' words: data, not instructions. - [Hacker News · deepDutta\_003 · 2026-10-28](https://news.ycombinator.com/item?id=50270161): Dhwani: A self-hosted voice notes app built with Whistle by Cactus Compute - [Bluesky · Armin Catovic · 2026-10-09](https://bsky.app/profile/arminx.wsocial.eu/post/3mxhhabwfzk2h): New open-weight ASR model Whistle is released from Cactus Compute, weighing at only 16MB and performing on par with Whisper Base, enabling ultra fast on-device speech-to-text, huggingface.co/Cactus-Compu... \#AI \#ASR \#SpeechToText \#OpenWeightModels \#HuggingFace - [Bluesky · UndergroundAI · 2026-10-08](https://bsky.app/profile/undergroundai66.bsky.social/post/3mxdb4jey4a2b): ⚡ Open-Source-Spracherkennung frisch auf HuggingFace. 🛠️ Das Modell whistle von Cactus-Compute steigt aktuell im Trend und bietet spannende… 🚀 Entdecke diesen und weitere KI-Geheimtipps auf Underground AI. 👉 https://underground-ai.bolt.app/ \#OpenSource \#KuenstlicheIntelligenz ## 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: 2026-11-05 - First published: 2026-09-30 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Cactus-Compute/whistle on Hugging Face](https://huggingface.co/Cactus-Compute/whistle) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/2a83a355-5760-461d-9833-a5d15efc8cc8) - [Its page on Mindket](https://mindket.com/p/2a83a355-5760-461d-9833-a5d15efc8cc8)