Ported from upstream commit
c0c1f09,
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 0.6B-parameter FastConformer-Large encoder with an RNN-T transducer decoder. Output is lowercase, no punctuation. Not a streaming model and does not translate.
WER measured on the full LibriSpeech test-clean split (2620 utterances) with greedy RNN-T decoding and no external LM. F32 reference baseline: 1.62%. NVIDIA's self-reported number on the same split is 1.63%.
parakeet-rnnt-0.6b 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 XL version of FastConformer Transducer [1] (around 600M 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.
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
1import nemo.collections.asr as nemo_asr
2asr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained(model_name="nvidia/parakeet-rnnt-0.6b")
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:
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
17.55
14.78
10.07
1.63
3.06
3.47
3.86
6.05
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.
