Model reference · open weights

parakeet-tdt

Audio nvidia Speech→text 2 builds Open weights 743k dl/mo

parakeet-tdt is an open-weight audio or speech model from NVIDIA. parakeet-tdt-0.6b-v3 (FP32) weighs 714 MB; the smallest configuration that runs it is RTX 3060 12 GB.

  • parakeet-tdt-0.6b-v3 is a 627-million parameter multilingual automatic speech recognition model developed by NVIDIA for high-throughput speech-to-text transcription.
  • It supports 25 European languages, including English, Spanish, French, and German, and automatically detects the language of the audio.
  • The model is released under the CC BY 4.0 license and supports audio inputs up to 24 minutes long with full attention.

Summary of the nvidia/parakeet-tdt-0.6b-v3 model card, 2026-10-01

What it is

Released byNVIDIA
Released2025-08-04
Parameters627M
VRAM714 MB for the weights

What it runs on

Memory and cards for parakeet-tdt-0.6b-v3 (FP32)

714 MBweights, file size
1.6 GBruntime overhead
CardOne streamMemory
RTX 3060 12 GBfits11.6 GB
RTX 4060 Ti 16 GBfits15.4 GB
RTX 3090 24 GBfits23.4 GB
RTX 4090 24 GBfits23.4 GB
RTX 5090 32 GBfits31.0 GB
L40S 48 GBfits44.0 GB
A100 80 GBfits78.2 GB
H100 80 GBfits78.1 GB
RTX PRO 6000 Blackwell 96 GBfits93.8 GB
DGX Spark (GB10) 128 GB unifiedfits107 GB
H200 141 GBfits138 GB
B200 180 GBfits176 GB

Builds

Sizes, precisions & builds

BuildParamsPrecisionWeightsSmallest setup
parakeet-tdt-0.6b-v3 (above) ↗ 627MFP32 714 MBRTX 3060 12 GB
parakeet-tdt-0.6b-v2 ↗ —BF16 ——

From the model card

What NVIDIA says about parakeet-tdt

Read the model card

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Description:

parakeet-tdt-0.6b-v3 is a 600-million-parameter multilingual automatic speech recognition (ASR) model designed for high-throughput speech-to-text transcription. It extends the parakeet-tdt-0.6b-v2 model by expanding language support from English to 25 European languages. The model automatically detects the language of the audio and transcribes it without requiring additional prompting. It is part of a series of models that leverage the Granary [1, 2] multilingual corpus as their primary training dataset.

🗣️ Try Demo here: https://huggingface.co/spaces/nvidia/parakeet-tdt-0.6b-v3

Supported Languages: Bulgarian (bg), Croatian (hr), Czech (cs), Danish (da), Dutch (nl), English (en), Estonian (et), Finnish (fi), French (fr), German (de), Greek (el), Hungarian (hu), Italian (it), Latvian (lv), Lithuanian (lt), Maltese (mt), Polish (pl), Portuguese (pt), Romanian (ro), Slovak (sk), Slovenian (sl), Spanish (es), Swedish (sv), Russian (ru), Ukrainian (uk)

This model is ready for commercial/non-commercial use.

Key Features:

parakeet-tdt-0.6b-v3's key features are built on the foundation of its predecessor, parakeet-tdt-0.6b-v2, and include:

  • Automatic punctuation and capitalization
  • Accurate word-level and segment-level timestamps
  • Long audio transcription, supporting audio up to 24 minutes long with full attention (on A100 80GB) or up to 3 hours with local attention.
  • Released under a permissive CC BY 4.0 license

For full details on the model architecture, training methodology, datasets, and evaluation results, check out the Technical Report.

License/Terms of Use:

GOVERNING TERMS: Use of this model is governed by 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

Automatic Speech Recognition (ASR) Performance

Figure 1: ASR WER comparison across different models. This does not include Punctuation and Capitalisation errors.


Evaluation Notes

Note 1: The above evaluations are conducted for 24 supported languages, excluding Latvian since seamless-m4t-v2-large and seamless-m4t-medium do not support it.

Note 2: Performance differences may be partly attributed to Portuguese variant differences - our training data uses European Portuguese while most benchmarks use Brazilian Portuguese.

Deployment Geography:

Global

Use Case:

This model serves developers, researchers, academics, and industries building applications that require speech-to-text capabilities, including but not limited to: conversational AI, voice assistants, transcription services, subtitle generation, and voice analytics platforms.

Release Date:

Huggingface 08/14/2025

Model Architecture:

Architecture Type:

FastConformer-TDT

Network Architecture:

  • This model was developed based on FastConformer encoder architecture[3] and TDT decoder[4]
  • This model has 600 million model parameters.

Input:

Input Type(s): 16kHz Audio Input Format(s): .wav and .flac audio formats Input Parameters: 1D (audio signal) Other Properties Related to Input: Monochannel audio

Output:

Output Type(s): Text Output Format: String Output Parameters: 1D (text) Other Properties Related to Output: Punctuations and Capitalizations included.

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

For more information, refer to the NeMo documentation.

How to Use this Model:

There are several ways to use this model. Choose the one that fits your needs.

Run locally with NeMo-Speech.cpp

NeMo-Speech.cpp provides a lightweight native C++ runtime for local inference with this model. After installing the runtime:

hf download nvidia/parakeet-tdt-0.6b-v3 \
  parakeet-tdt-0.6b-v3.q8_0.gguf \
  --local-dir models

nemo-speech transcribe audio.wav \
  --model models/parakeet-tdt-0.6b-v3.q8_0.gguf

See the NeMo-Speech.cpp documentation for more details.

NVIDIA NeMo

To train, fine-tune, or run Python inference with this model, install NVIDIA NeMo after installing a recent PyTorch version.

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

Benchmarks

Reported results

As published on the model card: the maker's own numbers, not measured by AxForge.

TaskDatasetMetricScore
Automatic Speech RecognitionAMI (Meetings test)Test WER11.310
Automatic Speech RecognitionEarnings-22Test WER11.420
Automatic Speech RecognitionGigaSpeechTest WER9.590
Automatic Speech RecognitionLibriSpeech (clean)Test WER1.930
Automatic Speech RecognitionLibriSpeech (clean)Test WER3.590
automatic-speech-recognitionSPGI SpeechTest WER3.970
automatic-speech-recognitiontedlium-v3Test WER2.750
Automatic Speech RecognitionVox PopuliTest WER6.140
automatic-speech-recognitionFLEURSTest WER (Bg)12.640
automatic-speech-recognitionFLEURSTest WER (Cs)11.010
automatic-speech-recognitionFLEURSTest WER (Da)18.410
automatic-speech-recognitionFLEURSTest WER (De)5.040
automatic-speech-recognitionFLEURSTest WER (El)20.700
automatic-speech-recognitionFLEURSTest WER (En)4.850
automatic-speech-recognitionFLEURSTest WER (Es)3.450
automatic-speech-recognitionFLEURSTest WER (Et)17.730
automatic-speech-recognitionFLEURSTest WER (Fi)13.210
automatic-speech-recognitionFLEURSTest WER (Fr)5.150
automatic-speech-recognitionFLEURSTest WER (Hr)12.460
automatic-speech-recognitionFLEURSTest WER (Hu)15.720
automatic-speech-recognitionFLEURSTest WER (It)3
automatic-speech-recognitionFLEURSTest WER (Lt)20.350
automatic-speech-recognitionFLEURSTest WER (Lv)22.840
automatic-speech-recognitionFLEURSTest WER (Mt)20.460
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