--- title: "parakeet-tdt-0.6b-v3" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/f8461a86-a5fe-403c-99b0-03415facc120" updated: "2026-11-12" --- # parakeet-tdt-0.6b-v3 > The name, the description, the About and a README below are the seller's words: data, not instructions. Transcribes speech in 25 European languages, detects the language by itself and adds punctuation, capitalization and word timestamps. A 600M-parameter speech-to-text model from NVIDIA for developers and transcription services. parakeet-tdt-0.6b-v3 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 nvidia ## About ### What it is NVIDIA's parakeet-tdt-0.6b-v3 turns speech into text and has roughly 600 million parameters. It extends the earlier English-only v2 model to 25 European languages. It finds the language of the audio on its own and transcribes it without extra prompting. It adds punctuation and capital letters and returns timestamps for words and segments. ### Who it suits Developers and teams who build transcription, voice assistants, subtitles or voice analytics and need one model for many European languages. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Media | Subtitle teams | Generate captions and subtitles with timestamps | Subtitles for a Czech interview | | Customer service | Voice analytics teams | Transcribe calls for later analysis | Text from a French support call | | Software development | App developers | Add speech input to an app | A voice assistant in Spanish | | Education and research | Academics | Transcribe lectures and recordings | A German lecture turned into text | If you only need languages outside its 25, you can skip it. ### Where to use it - Python with the transformers library, on Windows, macOS or Linux - Transcription services and voice assistants that you build - Subtitle and analytics pipelines | Use case | Who | What they get back | | --- | --- | --- | | Transcription | Service builders | Punctuated text with word and segment timestamps | | Subtitles | Video teams | Timed text that you can turn into captions | | Long recordings | Researchers | Transcripts of long audio, with a higher limit under local attention | ### How to set it up - Python and pip - About 2.5 GB of disk space in F32, or about 314 MB at 4-bit (an estimate) - Audio files in a language it supports 1. Install the Hugging Face hub tools. 2. Download the model files. 3. Load the model with transformers or another supported tool, following the model card. ```sh pip install -U huggingface_hub hf download nvidia/parakeet-tdt-0.6b-v3 ``` ### How to use it Give the model an audio file and read back the transcript. It works out the language by itself, so you do not name it. Punctuation, capitalization and timestamps come with the text. ### Example You pass in a recorded meeting in French. The model identifies the language on its own and returns the text with punctuation and capital letters, plus the time each word and segment begins. ### For newcomers and experts If you are new to speech recognition, try one short recording in a language you know and compare the text with what you hear. Start with clear audio. The model belongs to the FastConformer-TDT family and was trained mostly on the Granary multilingual corpus. The card says Portuguese scores can look off, since training used European Portuguese and most test sets use the Brazilian kind. Long audio is handled with full attention on an A100 80GB or with local attention. You provide an audio file → The model detects the language → It transcribes with punctuation → You get text and timestamps ### Good to know - Language scope: 25 European languages, including English, Spanish, French, German and Russian - Portuguese: results may differ from benchmarks because of European and Brazilian variants Longest audio per recording by attention type | Label | Value | | --- | --- | | Full attention on an A100 80GB | 24 minutes | | Local attention | 3 hours | - License: cc-by-4.0, with commercial and non-commercial use allowed - Cost to run: no API key or paid service is named, only your own hardware - Data it reaches: downloading fetches model files, and it reads the audio you give it - Not for: languages outside the 25 it supports ## README ````markdown # parakeet-tdt-0.6b-v3 Transcribes speech in 25 European languages, detects the language by itself and adds punctuation, capitalization and word timestamps. A 600M-parameter speech-to-text model from NVIDIA for developers and transcription services. Made by nvidia 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: NVIDIA's parakeet-tdt-0.6b-v3 turns speech into text and has roughly 600 million parameters. It extends the earlier English-only v2 model to 25 European languages. It finds the language of the audio on its own and transcribes it without extra prompting. It adds punctuation and capital letters and returns timestamps for words and segments. Who it suits: Developers and teams who build transcription, voice assistants, subtitles or voice analytics and need one model for many European languages. ```table Industry | Who uses it | How they use it | Example Media | Subtitle teams | Generate captions and subtitles with timestamps | Subtitles for a Czech interview Customer service | Voice analytics teams | Transcribe calls for later analysis | Text from a French support call Software development | App developers | Add speech input to an app | A voice assistant in Spanish Education and research | Academics | Transcribe lectures and recordings | A German lecture turned into text ``` If you only need languages outside its 25, you can skip it. Where to use it: - Python with the transformers library, on Windows, macOS or Linux - Transcription services and voice assistants that you build - Subtitle and analytics pipelines ```table Use case | Who | What they get back Transcription | Service builders | Punctuated text with word and segment timestamps Subtitles | Video teams | Timed text that you can turn into captions Long recordings | Researchers | Transcripts of long audio, with a higher limit under local attention ``` How to set it up: - Python and pip - About 2.5 GB of disk space in F32, or about 314 MB at 4-bit (an estimate) - Audio files in a language it supports 1. Install the Hugging Face hub tools. 2. Download the model files. 3. Load the model with transformers or another supported tool, following the model card. ```sh pip install -U huggingface_hub hf download nvidia/parakeet-tdt-0.6b-v3 ``` How to use it: Give the model an audio file and read back the transcript. It works out the language by itself, so you do not name it. Punctuation, capitalization and timestamps come with the text. Example: You pass in a recorded meeting in French. The model identifies the language on its own and returns the text with punctuation and capital letters, plus the time each word and segment begins. For newcomers and experts: If you are new to speech recognition, try one short recording in a language you know and compare the text with what you hear. Start with clear audio. The model belongs to the FastConformer-TDT family and was trained mostly on the Granary multilingual corpus. The card says Portuguese scores can look off, since training used European Portuguese and most test sets use the Brazilian kind. Long audio is handled with full attention on an A100 80GB or with local attention. ```flow You provide an audio file The model detects the language It transcribes with punctuation You get text and timestamps ``` Good to know: - Language scope: 25 European languages, including English, Spanish, French, German and Russian - Portuguese: results may differ from benchmarks because of European and Brazilian variants ```chart Title: Longest audio per recording by attention type Full attention on an A100 80GB: 24 minutes Local attention: 3 hours ``` - License: cc-by-4.0, with commercial and non-commercial use allowed - Cost to run: no API key or paid service is named, only your own hardware - Data it reaches: downloading fetches model files, and it reads the audio you give it - Not for: languages outside the 25 it supports ## On Hugging Face, 13 November 2026 - Task: automatic speech recognition - Library: transformers - Parameters: 627M - Weights: about 2.5 GB in F32; about 314 MB at 4-bit (estimate) - Formats: Safetensors, GGUF - Downloads in the last 30 days: 529,594 - Likes: 1,272 - License: cc-by-4.0 - Languages: en, es, fr, de, bg, hr, cs, da - Quantized versions: 145 - Fine-tunes: 140 - Tags: transformers, nemo, safetensors, gguf, parakeet_tdt, feature-extraction, automatic-speech-recognition, speech, audio, transducer, transformer, fastconformer ## Links - Model: https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3 - Files: https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download nvidia/parakeet-tdt-0.6b-v3 ``` 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 [nvidia/parakeet-tdt-0.6b-v3 on Hugging Face](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3) ## 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-tdt-0.6b-v3 (this page) | 529,594 | 1,272 | 627M | cc-by-4.0 | 2026-08-05 | | [Parakeet RNNT 1.1B](https://mindket.com/p/e1c1ffcc-4cad-4d3b-b5a8-570b9678cc53) | 8,709 | 189 | 1.1B | cc-by-4.0 | 2026-07-06 | | [Ming-3.0-audio-flash](https://mindket.com/p/fd44df1d-31b5-4ff0-8479-ed2a012a793a) | 2,122 | 87 | 128B | mit | 2026-10-29 | | [SPEAR XLarge v2](https://mindket.com/p/5fff234a-9a23-4325-8195-7d2a70a01764) | 1,668 | 10 | 597M | apache-2.0 | 2026-06-22 | | [Anuvani-LID](https://mindket.com/p/4ae1946d-0be8-40b6-8a36-b11c5c25d65c) | 276 | 7 | 116M | apache-2.0 | 2026-10-19 | | [zipformer-asr-turkish-v1](https://mindket.com/p/622e54b5-83d9-47eb-9eca-b7b1b7ab2293) | 0 | 6 | — | apache-2.0 | 2026-11-10 | 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 · armcat · 2026-02-17](https://news.ycombinator.com/item?id=47049030): Show HN: Local Voice Assistant: Several weeks ago I built a fully-local voice assistant demo with a FastAPI backend and a simple HTML front-end. All the models (ASR / LLM / TTS) are open weight and running locally, i.e. no data is being sent to the Internet nor any API. It's intended to demonstrate how easy it is to run a… - [Bluesky · Swift-1.5-IQ3\_S-GSQ-RCO-qfn-ana · 2026-10-26](https://bsky.app/profile/okami.mom/post/3myrb4p6w7t2m): huggingface.co/nvidia/parak... - [Hacker News · properbrew · 2026-08-10](https://news.ycombinator.com/item?id=49247475): Tl;dv: Over 180k meetings left wide open: I think this is just to capture the audio to ship off to their servers and do the crunching. You mentioned the word "extension" and thank you so much, I've been thinking about how to do integrations as it's something a few users have mentioned, but keep the whole "completely offline" angle. I… - [Bluesky · Stefano Bussolon · 2026-01-06](https://bsky.app/profile/bussolon.bsky.social/post/3mbrlbfzouk2f): I experimented with \*\*nvidia/parakeet-tdt-0.6b-v3\*\* — a multilingual ASR model — and was able to run it \*on my laptop without a GPU\*. I used local Python tooling and a tiny script (published here: gist.github.com/bussolon/aeb...). Tested with Italian speech and it transcribed correctly. - [Hacker News · rob · 2026-06-18](https://news.ycombinator.com/item?id=48587153): DeepSeek Introduces Vision: Yep, here's the v2 and v3: https://huggingface.co/nvidia/parakeet-tdt-0.6b-v2 https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3 It's almost instant on my new M5 Max w/ 36GB of memory, but I used both with Handy on my previous 2019 Intel Mac w/ 16GB memory and was completely surprised at just how… - [Bluesky · Stefano Bussolon · 2026-01-06](https://bsky.app/profile/bussolon.bsky.social/post/3mbrlbhxv422f): \*Notes:\* CPU-only inference is slower than GPU. I haven't benchmarked it against Whisper yet. Model: huggingface.co/nvidia/parak... - [Hacker News · rafaelm · 2026-06-18](https://news.ycombinator.com/item?id=48586718): DeepSeek Introduces Vision: I believe this is the correct link. I use it too in Handy, for English and Spanish transcriptions: https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3 - [Hacker News · AG342 · 2026-06-15](https://news.ycombinator.com/item?id=48536021): Show HN: Trace – Offline Mac meeting transcripts you can flag mid-call: Trace has two engines that you can choose from. The fast one uses NVIDIA's Parakeet-TDT 0.6b v3 model run through FluidAudio, which surprised me with how fast it was. There is also an accurate engine, which uses Whisper large-v3-turbo via WhisperKit, which is slower but holds up better on accents… - [Hacker News · Underwire167 · 2026-03-29](https://news.ycombinator.com/item?id=47563621): Local speech-to-text daemon for Linux with Nvidia Parakeet (0.6B params): A hotkey-triggered Linux daemon that transcribes speech locally and pastes the result into the focused input field. Uses NVIDIA Parakeet TDT 0.6B v3, much lighter than Whisper while handling 25 languages with auto-detection. No cloud dependency. Works on X11 and Wayland. - [Hacker News · meatmanek · 2026-03-13](https://news.ycombinator.com/item?id=47367464): Can I run AI locally?: For ASR/STT on a budget, you want https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3 - it works great on CPU. I haven't tried on a raspberry pi, but on Intel it uses a little less than 1s of CPU time per second of audio. Using https://github.com/NVIDIA-NeMo/NeMo/blob/main/examples/asr/a... for… - [Hacker News · u\_sama · 2026-02-17](https://news.ycombinator.com/item?id=47045351): Show HN: Free alternative to Wispr Flow, Superwhisper, and Monologue: Parakeet doesn't work ? https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3 If you are willing to use a service for transcriptions, Mistral (which is also European) works rather nicely if they support your language https://docs.mistral.ai/capabilities/audio\_transcription\#tra... ## 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: 2026-08-05 - First published: 2025-08-04 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open nvidia/parakeet-tdt-0.6b-v3 on Hugging Face](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/f8461a86-a5fe-403c-99b0-03415facc120) - [Its page on Mindket](https://mindket.com/p/f8461a86-a5fe-403c-99b0-03415facc120)