Byte-identical re-hosting of handy-computer/parakeet-rnnt-1.1b-gguf at revision 58c3f5108699, serving as the primary model source for the Panita desktop app.

GGUF conversion by handy-computer (transcribe.cpp project) — re-hosted unmodified.
Upstream model: nvidia/parakeet-rnnt-1.1b.
License: cc-by-4.0 — inherited from the upstream model; see the original model card below.

Every file's sha256 matches the source repository; the app verifies each download against the catalog's pinned hashes.

parakeet-rnnt-1.1b: transcribe.cpp GGUF

GGUF conversions of nvidia/parakeet-rnnt-1.1b for use with transcribe.cpp.

Ported from upstream commit a07b19e, pinned 2026-05-10. Validated against the NeMo reference at transcribe.cpp commit 42528dd on 2026-05-10.

Offline English speech-to-text with greedy RNN-T decoding. A 1.1B-parameter FastConformer-XL encoder with an RNN-T transducer decoder. Output is lowercase, no punctuation. Not a streaming model and does not translate.

Downloads

Quantization	Download	Size	WER (LibriSpeech test-clean)
F32	parakeet-rnnt-1.1b-F32.gguf	4.28 GB	1.45%
F16	parakeet-rnnt-1.1b-F16.gguf	2.15 GB	1.45%
Q8_0	parakeet-rnnt-1.1b-Q8_0.gguf	1.27 GB	1.46%
Q6_K	parakeet-rnnt-1.1b-Q6_K.gguf	1.04 GB	1.43%
Q5_K_M	parakeet-rnnt-1.1b-Q5_K_M.gguf	936 MB	1.43%
Q4_K_M	parakeet-rnnt-1.1b-Q4_K_M.gguf	825 MB	1.41%

WER measured on the full LibriSpeech test-clean split (2620 utterances) with greedy RNN-T decoding and no external LM. F32 reference baseline: 1.45%. NVIDIA's self-reported number on the same split is 1.46%.

Usage

Build transcribe.cpp from source:

bash

git clone git@github.com:handy-computer/transcribe.cpp.git
cd transcribe.cpp
cmake -B build && cmake --build build

Run on a 16 kHz mono WAV:

bash

build/bin/transcribe-cli \
  -m parakeet-rnnt-1.1b-Q8_0.gguf \
  input.wav

If your audio isn't already 16 kHz mono WAV, convert it first:

ffmpeg -i input.mp3 -ar 16000 -ac 1 output.wav

See the transcribe.cpp model page for performance numbers, numerical validation, and reproduction steps.

License

Inherited from the base model: CC-BY-4.0. See the upstream model card for full terms.

Original Model Card

The section below is reproduced from nvidia/parakeet-rnnt-1.1b at commit a07b19e for offline reference. The upstream card is the authoritative source.

Parakeet RNNT 1.1B (en)

parakeet-rnnt-1.1b is an ASR model that transcribes speech in lower case English alphabet. This model is jointly developed by NVIDIA NeMo and Suno.ai teams. It is an XXL version of FastConformer Transducer [1] (around 1.1B parameters) model. See the model architecture section and NeMo documentation for complete architecture details.

Licence/Terms Of Use

License to use this model is covered by the CC-BY-4.0. By downloading the public and release version of the model, you accept the terms and conditions of the CC-BY-4.0 license.

Discover more from NVIDIA:

For documentation, deployment guides, enterprise-ready APIs, and the latest open models—including Nemotron and other cutting-edge speech, translation, and generative AI—visit the NVIDIA Developer Portal at developer.nvidia.com. Join the community to access tools, support, and resources to accelerate your development with NVIDIA’s NeMo, Riva, NIM, and foundation models.

Explore more from NVIDIA:

What is Nemotron?
NVIDIA Developer Nemotron
NVIDIA Riva Speech
NeMo Documentation

NVIDIA NeMo: Training

To train, fine-tune or play with the model you will need to install NVIDIA NeMo. We recommend you install it after you've installed latest PyTorch version.

pip install nemo_toolkit['all']

How to Use this Model

The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.

Automatically instantiate the model

python

import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained(model_name="nvidia/parakeet-rnnt-1.1b")

Transcribing using Python

First, let's get a sample

wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav

Then simply do:

output = asr_model.transcribe(['2086-149220-0033.wav'])
print(output[0].text)

Transcribing many audio files

shell

python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py 
 pretrained_name="nvidia/parakeet-rnnt-1.1b" 
 audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"

Input

This model accepts 16000 Hz mono-channel audio (wav files) as input.

Output

This model provides transcribed speech as a string for a given audio sample.

Model Architecture

FastConformer [1] is an optimized version of the Conformer model with 8x depthwise-separable convolutional downsampling. The model is trained in a multitask setup with a Transducer decoder (RNNT) loss. You may find more information on the details of FastConformer here: Fast-Conformer Model.

Training

The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this example script and this base config.

The tokenizers for these models were built using the text transcripts of the train set with this script.

Datasets

The model was trained on 64K hours of English speech collected and prepared by NVIDIA NeMo and Suno teams.

The training dataset consists of private subset with 40K hours of English speech plus 24K hours from the following public datasets:

Librispeech 960 hours of English speech
Fisher Corpus
Switchboard-1 Dataset
WSJ-0 and WSJ-1
National Speech Corpus (Part 1, Part 6)
VCTK
VoxPopuli (EN)
Europarl-ASR (EN)
Multilingual Librispeech (MLS EN) - 2,000 hour subset
Mozilla Common Voice (v7.0)
People's Speech - 12,000 hour subset

Performance

The performance of Automatic Speech Recognition models is measuring using Word Error Rate. Since this dataset is trained on multiple domains and a much larger corpus, it will generally perform better at transcribing audio in general.

The following tables summarizes the performance of the available models in this collection with the Transducer decoder. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding.

Version	Tokenizer	Vocabulary Size	AMI	Earnings-22	Giga Speech	LS test-clean	SPGI Speech	TEDLIUM-v3	Vox Populi	Common Voice
1.22.0	SentencePiece Unigram	1024	17.10	14.11	9.96	1.46	2.47	3.11	3.92	5.39

These are greedy WER numbers without external LM. More details on evaluation can be found at HuggingFace ASR Leaderboard

NVIDIA Riva: Deployment

NVIDIA Riva, is an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, on edge, and embedded. Additionally, Riva provides:

World-class out-of-the-box accuracy for the most common languages with model checkpoints trained on proprietary data with hundreds of thousands of GPU-compute hours
Best in class accuracy with run-time word boosting (e.g., brand and product names) and customization of acoustic model, language model, and inverse text normalization
Streaming speech recognition, Kubernetes compatible scaling, and enterprise-grade support

Although this model isn’t supported yet by Riva, the list of supported models is here.
Check out Riva live demo.

References

[1] Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition

[2] Google Sentencepiece Tokenizer

[3] NVIDIA NeMo Toolkit

[4] Suno.ai

[5] HuggingFace ASR Leaderboard

epapanita/parakeet-rnnt-1.1b-gguf

Provenance