--- title: "Underdog Bark 3.0" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/348359c3-40c5-4844-9327-8a2fc6f9e202" updated: "2026-11-12" --- # Underdog Bark 3.0 > The name, the description, the About and a README below are the seller's words: data, not instructions. Turns English speech into text with a 0.6B model in a 163 MB download, built on NVIDIA's Parakeet TDT 0.6B v3. For developers and Apple-silicon users who want small local transcription. Non-commercial license. Underdog Bark 3.0 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 ConwayResearch ## About ### What it is Bark 3.0 is an English speech recognition model with 0.6B parameters, built on NVIDIA's Parakeet TDT 0.6B v3. The download is only 163 MB. Its authors report an average word error of 4.94 percent across seven English test sets from the Open ASR Leaderboard. ### Who it suits Developers and Mac users who want accurate English transcription from a small file that can run on their own device. It writes what was said, fillers included, so it suits verbatim transcripts. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Meetings | Teams | Turn recorded meetings into text | Run a recording through the model and read the transcript | | App development | App developers | Run speech recognition on the device | Use the model inside Underdog on Apple silicon | | Speech research | ML researchers | Compare small recognition models | Score it against Phonon-2 on the same test sets | | Media | Editors | Write out spoken audio | Transcribe a recorded talk in English | You can skip it if you need languages other than English, commercial use, or dictation that drops the um and uh sounds. ### Where to use it - Underdog, which runs the model with no Python, using a native Swift runtime on MLX - Python on Windows, macOS or Linux, through the transformers library the listing names - mlx-audio on an Apple computer, which the card used for its speed test | Use case | Who | What they get back | | --- | --- | --- | | Transcription | Developers | English text from speech, fillers included | | On-device dictation | Mac users | Text from your voice without sending audio away | | Benchmarking | Researchers | Word error rates to compare against other models | ### How to set it up You need Python and the Hugging Face tool. Underdog needs an Apple-silicon Mac. - Python on Windows, macOS or Linux - About 163 MB of disk space for the download - An Apple-silicon Mac, if you want to run it in Underdog 1. Install Python if you do not have it. 2. Install the Hugging Face hub tool, in the version the card pins. 3. Download the model files. 4. Follow the card's Run it section for the rest, which continues past what is shown here. ```bash pip install huggingface_hub==2.2.0 hf download ConwayResearch/Underdog-Bark-3.0 ``` ### How to use it In Underdog you pick the model and it reads the file directly. In Python, follow the card's Run it section after the download. For dictation that leaves out the fillers, the card points to a sibling called Underdog Bark 3.0 Clean. | Tool | What it does | | --- | --- | | Underdog | Runs the model file with a native Swift runtime on MLX, with no Python | | mlx-audio | A tool the card used to time transcription on an Apple M2 laptop | | Underdog Bark 3.0 Clean | A sibling model for dictation that leaves out the fillers | ### Example You record a meeting and run it through the model. The text comes back as spoken, with the um and uh sounds left in. On an Apple M2 laptop the card measured about 1.5 seconds for one minute of audio. ### For newcomers and experts Speech recognition turns spoken audio into written text. Word error rate is the share of words the model gets wrong, so lower is better. A first small step is to download the 163 MB files and read the card's Run it section. The card compares it with larger models on seven English sets. It reports 4.937 against 4.941 for Parakeet TDT 0.6B v3 at three decimals, and 6.04 for Whisper large-v3. The Bark rows use the leaderboard's scoring code but are not on the leaderboard yet. Underdog reads the model file into memory in roughly 2 seconds on Apple silicon. It then checks each download against a fixed size and a SHA-256 hash. Download the 163 MB model → Load it in Underdog or in Python → Feed it English audio → Get a transcript with fillers kept Average word error in percent on seven English sets, lower is better | Label | Value | | --- | --- | | Bark 3.0 | 4.94% | | Parakeet TDT 0.6B v3 | 4.94% | | Qwen3-ASR-0.6B | 5.16% | | Phonon-2 | 5.21% | | Whisper large-v3 | 6.04% | ### Good to know - It is an English model and it does not clean up speech, so fillers stay in the text. - The accuracy numbers are the authors' own, scored with the leaderboard's code but not yet listed there. - In Underdog the card says your audio never leaves the device. - Download size is 163 MB, against 2.5 GB for the Parakeet model it builds on. - License: cc-by-nc-4.0, which allows non-commercial use only. - Cost to run: none named, you use your own hardware. - Data it reaches: your audio, plus files downloaded from Hugging Face. Underdog keeps audio on the device. - Not for: commercial use, or speech in languages other than English. ## README ````markdown # Underdog Bark 3.0 Turns English speech into text with a 0.6B model in a 163 MB download, built on NVIDIA's Parakeet TDT 0.6B v3. For developers and Apple-silicon users who want small local transcription. Non-commercial license. Made by ConwayResearch 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: Bark 3.0 is an English speech recognition model with 0.6B parameters, built on NVIDIA's Parakeet TDT 0.6B v3. The download is only 163 MB. Its authors report an average word error of 4.94 percent across seven English test sets from the Open ASR Leaderboard. Who it suits: Developers and Mac users who want accurate English transcription from a small file that can run on their own device. It writes what was said, fillers included, so it suits verbatim transcripts. ```table Industry | Who uses it | How they use it | Example Meetings | Teams | Turn recorded meetings into text | Run a recording through the model and read the transcript App development | App developers | Run speech recognition on the device | Use the model inside Underdog on Apple silicon Speech research | ML researchers | Compare small recognition models | Score it against Phonon-2 on the same test sets Media | Editors | Write out spoken audio | Transcribe a recorded talk in English ``` You can skip it if you need languages other than English, commercial use, or dictation that drops the um and uh sounds. Where to use it: - Underdog, which runs the model with no Python, using a native Swift runtime on MLX - Python on Windows, macOS or Linux, through the transformers library the listing names - mlx-audio on an Apple computer, which the card used for its speed test ```table Use case | Who | What they get back Transcription | Developers | English text from speech, fillers included On-device dictation | Mac users | Text from your voice without sending audio away Benchmarking | Researchers | Word error rates to compare against other models ``` How to set it up: You need Python and the Hugging Face tool. Underdog needs an Apple-silicon Mac. - Python on Windows, macOS or Linux - About 163 MB of disk space for the download - An Apple-silicon Mac, if you want to run it in Underdog 1. Install Python if you do not have it. 2. Install the Hugging Face hub tool, in the version the card pins. 3. Download the model files. 4. Follow the card's Run it section for the rest, which continues past what is shown here. ```bash pip install huggingface_hub==2.2.0 hf download ConwayResearch/Underdog-Bark-3.0 ``` How to use it: In Underdog you pick the model and it reads the file directly. In Python, follow the card's Run it section after the download. For dictation that leaves out the fillers, the card points to a sibling called Underdog Bark 3.0 Clean. ```table Tool | What it does Underdog | Runs the model file with a native Swift runtime on MLX, with no Python mlx-audio | A tool the card used to time transcription on an Apple M2 laptop Underdog Bark 3.0 Clean | A sibling model for dictation that leaves out the fillers ``` Example: You record a meeting and run it through the model. The text comes back as spoken, with the um and uh sounds left in. On an Apple M2 laptop the card measured about 1.5 seconds for one minute of audio. For newcomers and experts: Speech recognition turns spoken audio into written text. Word error rate is the share of words the model gets wrong, so lower is better. A first small step is to download the 163 MB files and read the card's Run it section. The card compares it with larger models on seven English sets. It reports 4.937 against 4.941 for Parakeet TDT 0.6B v3 at three decimals, and 6.04 for Whisper large-v3. The Bark rows use the leaderboard's scoring code but are not on the leaderboard yet. Underdog reads the model file into memory in roughly 2 seconds on Apple silicon. It then checks each download against a fixed size and a SHA-256 hash. ```flow Download the 163 MB model Load it in Underdog or in Python Feed it English audio Get a transcript with fillers kept ``` ```chart Title: Average word error in percent on seven English sets, lower is better Bark 3.0: 4.94% Parakeet TDT 0.6B v3: 4.94% Qwen3-ASR-0.6B: 5.16% Phonon-2: 5.21% Whisper large-v3: 6.04% ``` Good to know: - It is an English model and it does not clean up speech, so fillers stay in the text. - The accuracy numbers are the authors' own, scored with the leaderboard's code but not yet listed there. - In Underdog the card says your audio never leaves the device. - Download size is 163 MB, against 2.5 GB for the Parakeet model it builds on. - License: cc-by-nc-4.0, which allows non-commercial use only. - Cost to run: none named, you use your own hardware. - Data it reaches: your audio, plus files downloaded from Hugging Face. Underdog keeps audio on the device. - Not for: commercial use, or speech in languages other than English. ## On Hugging Face, 13 November 2026 - Task: automatic speech recognition - Library: transformers - Downloads in the last 30 days: 413 - Likes: 95 - License: cc-by-nc-4.0 - Languages: en - Based on: nvidia/parakeet-tdt-0.6b-v3 - Quantized versions: 5 - Fine-tunes: 3 - Tags: transformers, coreml, underdog, speech-to-text, low-bit, on-device, apple-silicon, parakeet, automatic-speech-recognition ## Links - Model: https://huggingface.co/ConwayResearch/Underdog-Bark-3.0 - Files: https://huggingface.co/ConwayResearch/Underdog-Bark-3.0/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download ConwayResearch/Underdog-Bark-3.0 ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [ConwayResearch/Underdog-Bark-3.0 on Hugging Face](https://huggingface.co/ConwayResearch/Underdog-Bark-3.0) ## 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 | | --- | --- | --- | --- | --- | --- | | Underdog Bark 3.0 (this page) | 413 | 95 | — | cc-by-nc-4.0 | 2026-11-05 | | [Moondream Parakeet Redux](https://mindket.com/p/0b472c3b-79ae-4a8c-a542-ddffc53eee4c) | 9,577 | 320 | 149M | cc-by-4.0 | 2026-09-23 | | [Whistle](https://mindket.com/p/2a83a355-5760-461d-9833-a5d15efc8cc8) | 94,489 | 570 | — | apache-2.0 | 2026-11-05 | | [Ming-3.0-audio-flash](https://mindket.com/p/fd44df1d-31b5-4ff0-8479-ed2a012a793a) | 2,122 | 87 | 128B | mit | 2026-10-29 | | [parakeet-ultra](https://mindket.com/p/5647b30a-a58e-4b54-b4fc-18517ea9dde6) | 51,675 | 142 | 627M | cc-by-4.0 | 2026-09-23 | | [whisperkit-coreml](https://mindket.com/p/41dd2c44-8389-4b32-b641-2c39501a40dd) | 60,717,190 | 266 | — | mit | 2026-08-19 | 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: 2026-11-05 - First published: 2026-10-15 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open ConwayResearch/Underdog-Bark-3.0 on Hugging Face](https://huggingface.co/ConwayResearch/Underdog-Bark-3.0) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/348359c3-40c5-4844-9327-8a2fc6f9e202) - [Its page on Mindket](https://mindket.com/p/348359c3-40c5-4844-9327-8a2fc6f9e202)