--- title: "parakeet-ultra" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/5647b30a-a58e-4b54-b4fc-18517ea9dde6" updated: "2026-11-12" --- # parakeet-ultra > The name, the description, the About and a README below are the seller's words: data, not instructions. Transcribes speech to text with word and sentence timestamps. A post-trained version of NVIDIA parakeet-tdt-0.6b-v3 from moondream, built for developers who need accurate transcripts in many languages. parakeet-ultra 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 moondream ## About ### What it is parakeet-ultra is a speech recognition model from moondream. It turns audio into text and can add start and end times for each sentence or word. It is a post-trained version of NVIDIA parakeet-tdt-0.6b-v3 with the same architecture, tokenizer and roughly 0.6B parameters. The authors report lower error rates than the original on their benchmarks. ### Who it suits Developers who add transcription to an app, and teams that work with recorded meetings, calls, podcasts or talks. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers building voice features | Call the model from Python on audio files | Turn an uploaded recording into text | | Media | Podcast and video teams | Transcribe episodes with word timings | Subtitles aligned to each spoken word | | Business | Teams with meeting or call recordings | Convert audio into searchable text | A written record of a recorded meeting | | Education | Lecture and talk archives | Transcribe long recordings | Text versions of recorded talks | Skip it if you want a finished transcription app, because the source shows only a Python route. ### Where to use it - Python scripts that use the moondream package and its Photon runtime - NVIDIA GPU machines, where every accuracy figure on the card was measured - Windows, macOS or Linux with Python, to download the model files | Use case | Who | What they get back | | --- | --- | --- | | Plain transcription | Developers | The spoken text of an audio file | | Sentence timestamps | Subtitle makers | One entry per sentence with start and end in seconds | | Word timestamps | Editors and indexers | Start and end times for every word | ### How to set it up - Python and pip - An NVIDIA GPU if you want the setup the authors measured - The moondream Python package, which provides the Photon runtime 1. Install the moondream package. 2. Optionally download the model files with the Hugging Face tools. 3. Put an audio file such as speech.wav next to your script. ```bash pip install moondream ``` ```bash pip install -U huggingface_hub hf download moondream/parakeet-ultra ``` ### How to use it Open the model with md.photon, then call transcribe on an audio file. Setting timestamps to segment gives one entry per sentence, and word adds the timing of every word. ```python # pip install moondream import moondream as md with md.photon("moondream/parakeet-ultra") as speech: result = speech.transcribe(audio="speech.wav") print(result["text"]) # timestamps="segment": one entry per sentence, with its start # and end in seconds; "word" adds the start and end of every word result = speech.transcribe(audio="speech.wav", timestamps="word") for segment in result["segments"]: print(segment["start"], segment["end"], segment["text"]) for word in segment["words"]: print(word["start"], word["end"], word["word"]) ``` | Tool | What it does | | --- | --- | | md.photon | Opens the model so you can transcribe audio | | speech.transcribe | Transcribes an audio file and returns a result | | timestamps option | Set to segment for sentences or word for every word | | result text | The full transcript as one string | | result segments | Sentences with start and end times, and words when requested | ### Example You pass speech.wav to transcribe with timestamps set to word. The result holds the whole transcript first. After it come the sentences with start and end seconds, and each sentence lists its words with their own timings. ### For newcomers and experts If you are new to speech recognition, think of this as software that listens to a recording and types it out. Start by installing the package and transcribing one short wav file, then print the text. If you know the field, this is a post-trained parakeet-tdt-0.6b-v3 that keeps the original architecture and tokenizer. The card says it runs faster in Photon than the original does in NeMo, with 9,743 times real time on LibriSpeech test-clean against 6,005 for the original on one NVIDIA B200. Audio file such as speech.wav → Photon loads parakeet-ultra → transcribe returns the text → Optional timestamps per sentence or word ### Good to know - The accuracy and speed numbers come from the model authors and were measured with Photon on an NVIDIA GPU - On one set, the cleaned VoxPopuli test, the new model scored 3.10 against 3.02 for the original, so it is not better everywhere - Weights: about 1.3 GB in F16, according to Hugging Face Average word error rate in percent, lower is better | Label | Value | | --- | --- | | Original on 7 English sets | 6.26 percent | | Ultra on 7 English sets | 5.80 percent | | Original on FLEURS, 25 languages | 11.62 percent | | Ultra on FLEURS, 25 languages | 9.55 percent | - License: CC BY 4.0 - Cost to run: none named - Data it reaches: the audio files you pass in, plus the model files you download - Not for: CPU or Apple silicon use, where the card points to the smaller Parakeet Redux sibling ## README ````markdown # parakeet-ultra Transcribes speech to text with word and sentence timestamps. A post-trained version of NVIDIA parakeet-tdt-0.6b-v3 from moondream, built for developers who need accurate transcripts in many languages. Made by moondream 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: parakeet-ultra is a speech recognition model from moondream. It turns audio into text and can add start and end times for each sentence or word. It is a post-trained version of NVIDIA parakeet-tdt-0.6b-v3 with the same architecture, tokenizer and roughly 0.6B parameters. The authors report lower error rates than the original on their benchmarks. Who it suits: Developers who add transcription to an app, and teams that work with recorded meetings, calls, podcasts or talks. ```table Industry | Who uses it | How they use it | Example Software | Developers building voice features | Call the model from Python on audio files | Turn an uploaded recording into text Media | Podcast and video teams | Transcribe episodes with word timings | Subtitles aligned to each spoken word Business | Teams with meeting or call recordings | Convert audio into searchable text | A written record of a recorded meeting Education | Lecture and talk archives | Transcribe long recordings | Text versions of recorded talks ``` Skip it if you want a finished transcription app, because the source shows only a Python route. Where to use it: - Python scripts that use the moondream package and its Photon runtime - NVIDIA GPU machines, where every accuracy figure on the card was measured - Windows, macOS or Linux with Python, to download the model files ```table Use case | Who | What they get back Plain transcription | Developers | The spoken text of an audio file Sentence timestamps | Subtitle makers | One entry per sentence with start and end in seconds Word timestamps | Editors and indexers | Start and end times for every word ``` How to set it up: - Python and pip - An NVIDIA GPU if you want the setup the authors measured - The moondream Python package, which provides the Photon runtime 1. Install the moondream package. 2. Optionally download the model files with the Hugging Face tools. 3. Put an audio file such as speech.wav next to your script. ```bash pip install moondream ``` ```bash pip install -U huggingface_hub hf download moondream/parakeet-ultra ``` How to use it: Open the model with md.photon, then call transcribe on an audio file. Setting timestamps to segment gives one entry per sentence, and word adds the timing of every word. ```python # pip install moondream import moondream as md with md.photon("moondream/parakeet-ultra") as speech: result = speech.transcribe(audio="speech.wav") print(result["text"]) # timestamps="segment": one entry per sentence, with its start # and end in seconds; "word" adds the start and end of every word result = speech.transcribe(audio="speech.wav", timestamps="word") for segment in result["segments"]: print(segment["start"], segment["end"], segment["text"]) for word in segment["words"]: print(word["start"], word["end"], word["word"]) ``` ```table Tool | What it does md.photon | Opens the model so you can transcribe audio speech.transcribe | Transcribes an audio file and returns a result timestamps option | Set to segment for sentences or word for every word result text | The full transcript as one string result segments | Sentences with start and end times, and words when requested ``` Example: You pass speech.wav to transcribe with timestamps set to word. The result holds the whole transcript first. After it come the sentences with start and end seconds, and each sentence lists its words with their own timings. For newcomers and experts: If you are new to speech recognition, think of this as software that listens to a recording and types it out. Start by installing the package and transcribing one short wav file, then print the text. If you know the field, this is a post-trained parakeet-tdt-0.6b-v3 that keeps the original architecture and tokenizer. The card says it runs faster in Photon than the original does in NeMo, with 9,743 times real time on LibriSpeech test-clean against 6,005 for the original on one NVIDIA B200. ```flow Audio file such as speech.wav Photon loads parakeet-ultra transcribe returns the text Optional timestamps per sentence or word ``` Good to know: - The accuracy and speed numbers come from the model authors and were measured with Photon on an NVIDIA GPU - On one set, the cleaned VoxPopuli test, the new model scored 3.10 against 3.02 for the original, so it is not better everywhere - Weights: about 1.3 GB in F16, according to Hugging Face ```chart Title: Average word error rate in percent, lower is better Original on 7 English sets: 6.26 percent Ultra on 7 English sets: 5.80 percent Original on FLEURS, 25 languages: 11.62 percent Ultra on FLEURS, 25 languages: 9.55 percent ``` - License: CC BY 4.0 - Cost to run: none named - Data it reaches: the audio files you pass in, plus the model files you download - Not for: CPU or Apple silicon use, where the card points to the smaller Parakeet Redux sibling ## On Hugging Face, 13 November 2026 - Task: automatic speech recognition - Parameters: 627M - Weights: about 1.3 GB in F16; about 314 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 51,675 - Likes: 142 - License: cc-by-4.0 - Languages: en, de, fr, es, it, pt, ru, uk - Quantized versions: 53 - Fine-tunes: 26 - Tags: safetensors, parakeet_tdt, parakeet, speech-recognition, automatic-speech-recognition ## Links - Model: https://huggingface.co/moondream/parakeet-ultra - Files: https://huggingface.co/moondream/parakeet-ultra/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download moondream/parakeet-ultra ``` 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 [moondream/parakeet-ultra on Hugging Face](https://huggingface.co/moondream/parakeet-ultra) ## 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 | | --- | --- | --- | --- | --- | --- | | parakeet-ultra (this page) | 51,675 | 142 | 627M | cc-by-4.0 | 2026-09-23 | | [Moondream Parakeet Redux](https://mindket.com/p/0b472c3b-79ae-4a8c-a542-ddffc53eee4c) | 9,577 | 320 | 149M | cc-by-4.0 | 2026-09-23 | | [parakeet-tdt-0.6b-v3](https://mindket.com/p/f8461a86-a5fe-403c-99b0-03415facc120) | 529,594 | 1,272 | 627M | cc-by-4.0 | 2026-08-05 | | [Underdog Bark 3.0](https://mindket.com/p/348359c3-40c5-4844-9327-8a2fc6f9e202) | 413 | 95 | — | cc-by-nc-4.0 | 2026-11-05 | | [Whistle](https://mindket.com/p/2a83a355-5760-461d-9833-a5d15efc8cc8) | 94,489 | 570 | — | apache-2.0 | 2026-11-05 | | [Qwen3-ASR-1.7B GGUF](https://mindket.com/p/10bca442-bcbf-45b9-9c6c-930624df43f9) | 11,523 | 17 | 2.3B | apache-2.0 | 2026-08-02 | 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 · GaggiX · 2026-09-27](https://news.ycombinator.com/item?id=49864691): Moondream Parakeet Redux and Parakeet Ultra ## 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: 2026-09-23 - First published: 2026-09-22 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open moondream/parakeet-ultra on Hugging Face](https://huggingface.co/moondream/parakeet-ultra) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/5647b30a-a58e-4b54-b4fc-18517ea9dde6) - [Its page on Mindket](https://mindket.com/p/5647b30a-a58e-4b54-b4fc-18517ea9dde6)