--- title: "Parakeet RNNT 1.1B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/e1c1ffcc-4cad-4d3b-b5a8-570b9678cc53" updated: "2026-11-13" --- # Parakeet RNNT 1.1B > The name, the description, the About and a README below are the seller's words: data, not instructions. Transcribes English speech into lowercase text. A 1.1B FastConformer Transducer model from the NVIDIA NeMo and Suno.ai teams, for developers who add speech recognition to apps. Parakeet RNNT 1.1B 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 nvidia ## About ### What it is Parakeet RNNT 1.1B turns spoken English into written text. NVIDIA NeMo and Suno.ai built it together as an XXL FastConformer Transducer with about 1.1B parameters. It outputs text in the lowercase English alphabet and runs through the NeMo toolkit or Hugging Face Transformers. ### Who it suits It suits developers and researchers who need English speech to text on their own machines, either with the checkpoint as it is or as a base for fine-tuning. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Media | Podcast and video teams | Transcribe English recordings into text | A folder of interview recordings turned into text | | Customer service | Support teams | Turn English call audio into text for search or review | A recorded support call transcribed | | Software | App developers | Add English speech to text to a product through NeMo or Transformers | A voice note feature in an app | | Research | Speech researchers | Fine-tune the checkpoint on another dataset | Adapting the model to a specialist vocabulary | You can skip it if your audio is not in English, since the card lists English only. ### Where to use it - The NeMo toolkit, installed after the latest PyTorch - Hugging Face Transformers, from version 5.13.0 - A command line, using the transcription script from the NeMo repository - Windows, macOS or Linux with Python, to download the files | Use case | Who | What they get back | | --- | --- | --- | | Transcribe one file | Developers | The spoken words as lowercase text | | Transcribe a folder | Data teams | A transcript for each audio file in the folder | | Fine-tune on your data | Researchers | A model adapted to another dataset | ### How to set it up - Python - The latest PyTorch, installed before NeMo - NeMo, or Transformers 5.13.0 or newer - Room for about 4.3 GB of weights in F32, or about 535 MB at 4-bit by estimate 1. Install the latest PyTorch. 2. Install the NeMo toolkit. 3. Load the model by its name in Python, as shown below. ```sh pip install nemo_toolkit['all'] ``` ```python import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained(model_name="nvidia/parakeet-rnnt-1.1b") ``` To fetch the files yourself first: ```sh pip install -U huggingface_hub hf download nvidia/parakeet-rnnt-1.1b ``` ### How to use it Pass a list of audio files to the transcribe call and print the first result. The file name below is the card's sample, so swap in your own English recording. ```python output = asr_model.transcribe(['2086-149220-0033.wav']) print(output[0].text) ``` For a whole folder, run the NeMo script with the model name and the folder of audio files. ```sh python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="nvidia/parakeet-rnnt-1.1b" audio_dir="" ``` To use Transformers instead, install version 5.13.0 or newer. ```bash pip install "transformers>=5.13.0" ``` | Tool | What it does | | --- | --- | | from\_pretrained | Loads the model by its name | | asr\_model.transcribe | Turns a list of audio files into text, one result per file | | transcribe\_speech.py | A NeMo script that transcribes every audio file in a folder | | AutoModelForRNNT | Loads the model when you use Transformers | ### Example Give it a short English WAV recording and print the first result's text. You get the spoken words back as lowercase text. ### For newcomers and experts It listens to an English recording and writes down what was said. For a first try, install NeMo after PyTorch, load the model with the Python above, and transcribe one short English WAV file. Underneath, it is a FastConformer Transducer in its XXL size, about 1.1B parameters, built jointly by the NVIDIA NeMo and Suno.ai teams. You can use the checkpoint for inference as it is, or fine-tune it on another dataset. The Transformers route needs version 5.13.0 or newer. English audio file goes in → NeMo or Transformers loads the checkpoint → The FastConformer Transducer decodes the speech → Lowercase English text comes out ### Good to know The output uses the lowercase English alphabet only, so expect no capital letters. Downloading the public release counts as accepting its license terms. - License: CC BY 4.0 - Cost to run: no API key or paid service is named - Data it reaches: reads the audio files you give it and downloads model files from Hugging Face, and the card's sample first downloads an example WAV file from the web - Not for: languages other than English, since the card lists English only ## README ````markdown # Parakeet RNNT 1.1B Transcribes English speech into lowercase text. A 1.1B FastConformer Transducer model from the NVIDIA NeMo and Suno.ai teams, for developers who add speech recognition to apps. 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: Parakeet RNNT 1.1B turns spoken English into written text. NVIDIA NeMo and Suno.ai built it together as an XXL FastConformer Transducer with about 1.1B parameters. It outputs text in the lowercase English alphabet and runs through the NeMo toolkit or Hugging Face Transformers. Who it suits: It suits developers and researchers who need English speech to text on their own machines, either with the checkpoint as it is or as a base for fine-tuning. ```table Industry | Who uses it | How they use it | Example Media | Podcast and video teams | Transcribe English recordings into text | A folder of interview recordings turned into text Customer service | Support teams | Turn English call audio into text for search or review | A recorded support call transcribed Software | App developers | Add English speech to text to a product through NeMo or Transformers | A voice note feature in an app Research | Speech researchers | Fine-tune the checkpoint on another dataset | Adapting the model to a specialist vocabulary ``` You can skip it if your audio is not in English, since the card lists English only. Where to use it: - The NeMo toolkit, installed after the latest PyTorch - Hugging Face Transformers, from version 5.13.0 - A command line, using the transcription script from the NeMo repository - Windows, macOS or Linux with Python, to download the files ```table Use case | Who | What they get back Transcribe one file | Developers | The spoken words as lowercase text Transcribe a folder | Data teams | A transcript for each audio file in the folder Fine-tune on your data | Researchers | A model adapted to another dataset ``` How to set it up: - Python - The latest PyTorch, installed before NeMo - NeMo, or Transformers 5.13.0 or newer - Room for about 4.3 GB of weights in F32, or about 535 MB at 4-bit by estimate 1. Install the latest PyTorch. 2. Install the NeMo toolkit. 3. Load the model by its name in Python, as shown below. ```sh pip install nemo_toolkit['all'] ``` ```python import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained(model_name="nvidia/parakeet-rnnt-1.1b") ``` To fetch the files yourself first: ```sh pip install -U huggingface_hub hf download nvidia/parakeet-rnnt-1.1b ``` How to use it: Pass a list of audio files to the transcribe call and print the first result. The file name below is the card's sample, so swap in your own English recording. ```python output = asr_model.transcribe(['2086-149220-0033.wav']) print(output[0].text) ``` For a whole folder, run the NeMo script with the model name and the folder of audio files. ```sh python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="nvidia/parakeet-rnnt-1.1b" audio_dir="" ``` To use Transformers instead, install version 5.13.0 or newer. ```bash pip install "transformers>=5.13.0" ``` ```table Tool | What it does from_pretrained | Loads the model by its name asr_model.transcribe | Turns a list of audio files into text, one result per file transcribe_speech.py | A NeMo script that transcribes every audio file in a folder AutoModelForRNNT | Loads the model when you use Transformers ``` Example: Give it a short English WAV recording and print the first result's text. You get the spoken words back as lowercase text. For newcomers and experts: It listens to an English recording and writes down what was said. For a first try, install NeMo after PyTorch, load the model with the Python above, and transcribe one short English WAV file. Underneath, it is a FastConformer Transducer in its XXL size, about 1.1B parameters, built jointly by the NVIDIA NeMo and Suno.ai teams. You can use the checkpoint for inference as it is, or fine-tune it on another dataset. The Transformers route needs version 5.13.0 or newer. ```flow English audio file goes in NeMo or Transformers loads the checkpoint The FastConformer Transducer decodes the speech Lowercase English text comes out ``` Good to know: The output uses the lowercase English alphabet only, so expect no capital letters. Downloading the public release counts as accepting its license terms. - License: CC BY 4.0 - Cost to run: no API key or paid service is named - Data it reaches: reads the audio files you give it and downloads model files from Hugging Face, and the card's sample first downloads an example WAV file from the web - Not for: languages other than English, since the card lists English only ## On Hugging Face, 13 November 2026 - Task: automatic speech recognition - Library: nemo - Parameters: 1.1B - Weights: about 4.3 GB in F32; about 535 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 8,709 - Likes: 189 - License: cc-by-4.0 - Languages: en - Quantized versions: 14 - Fine-tunes: 2 - Tags: nemo, safetensors, parakeet_rnnt, feature-extraction, automatic-speech-recognition, speech, audio, transducer, fastconformer, conformer, pytorch, hf-asr-leaderboard ## Links - Model: https://huggingface.co/nvidia/parakeet-rnnt-1.1b - Files: https://huggingface.co/nvidia/parakeet-rnnt-1.1b/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download nvidia/parakeet-rnnt-1.1b ``` 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 [nvidia/parakeet-rnnt-1.1b on Hugging Face](https://huggingface.co/nvidia/parakeet-rnnt-1.1b) ## 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 RNNT 1.1B (this page) | 8,709 | 189 | 1.1B | cc-by-4.0 | 2026-07-06 | | [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 | | [Anuvani-LID](https://mindket.com/p/4ae1946d-0be8-40b6-8a36-b11c5c25d65c) | 276 | 7 | 116M | apache-2.0 | 2026-10-19 | | [Wav2Vec2-Base-960h](https://mindket.com/p/29706acd-2079-4db5-a54d-6436b4514cb3) | 1,794,378 | 418 | 94.4M | apache-2.0 | 2022-11-14 | | [ARK-ASR-3B](https://mindket.com/p/c33d475a-2543-47ca-93db-67b7dd111d35) | 1,655 | 115 | 4.1B | apache-2.0 | 2026-06-27 | | [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, 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 · pabs3 · 2025-08-20](https://news.ycombinator.com/item?id=44958672): Show HN: Whispering – Open-source, local-first dictation you can trust: Whisper doesn't list its training data (or code?), so can't be an open-source model, just an open weights model. Parakeet does list its training data, and at least one of those is not FOSS, but some of them definitely are FOSS. I wonder if they nVidia would create a fully FOSS model by retraining… - [Bluesky · Retweeted by Garry Tan UNOFFICIAL · 2026-06-05](https://bsky.app/profile/garrytan-mir-rt.selfhosted.social/post/3mnqxphj2yy22): multimodalart ported it to PyTorch within hours with live ZeroGPU demos. → NVIDIA Nemotron-3.5 ASR: 600M streaming, 17x more concurrent streams vs Parakeet RNNT 1.1B. 👁️ Vision \& VLMs → PaddleOCR-VL-1.6: SOTA document parsing at 1B params, Apache 2.0. ## 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: 2026-07-06 - First published: 2023-12-27 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open nvidia/parakeet-rnnt-1.1b on Hugging Face](https://huggingface.co/nvidia/parakeet-rnnt-1.1b) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/e1c1ffcc-4cad-4d3b-b5a8-570b9678cc53) - [Its page on Mindket](https://mindket.com/p/e1c1ffcc-4cad-4d3b-b5a8-570b9678cc53)