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Parakeet RNNT 0.6B

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Parakeet RNNT 0.6B is an open-weights speech-to-text model from NVIDIA, released January 2024. Its published size is 0.62B parameters. Licence: cc-by-4.0.

Catalogue identity notes

Exact checkpoint nvidia/parakeet-rnnt-0.6b (NeMo). Params: HF safetensors total. released aligned with catalogue sibling parakeet-rnnt-1-1b (repo created 2023-12-28).

56.6±2.7#60 of 162 speech-to-text modelsfrom 4 benchmark results across 2 sources

Score within the Open ASR English short-form WER (March 2026) comparison set only.

$ how it compares

The speech-to-text models ranked either side of Parakeet RNNT 0.6B, out of 176 with a score, then more from NVIDIA.

#modelscorewhereRAMreleased
67Whisper large-v3 OpenAI57±1api $0.0018/audio min +local~4.2GB2023-11
68GLM-ASR-Nano-2512 Z.ai57±4local~5.9GB2025-12
69Parakeet RNNT 0.6B NVIDIA57±3local~3.5GB2024-01
70Rev AI Fusion (Whisper Fusion) Rev57±4comparison only2024-01
71Orukeet Oruk AI57±4local~0.7GB2026-09
more from NVIDIA
28Canary-Qwen-2.5B NVIDIA61±2local~6.6GB2025-07
35Parakeet TDT 0.6B v2 NVIDIA60±1local~1.9GB2025-05
37Parakeet Unified EN 0.6B NVIDIA60±4local~0.7GB2026-04

see all speech-to-text models · find the ones that fit your machine

$ what it's good at

long-form transcription▁▁▄▁·▁▂█▄▁▁▁ #13 of 27 * · 26% of its score · 1 test

Beats Chirp 3, Parakeet TDT 1.1B, Parakeet RNNT 1.1B here, though they rank higher overall. Trails Whisper large (v1), Whisper large-v2, Rev AI Machine (Reverb ASR) here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Open ASR English long-form WER9.5% ✎▁▁▅▁·▃▅█▆▁▂▂ beats 15/30avg 12%▇▇▇▇▇▇▇▇▇▇ 26%typical
other tests3 not in a task · 72% of its score
testresultvs the others in its rundifficultycountswhy
LibriSpeech test-clean WER1.6% ✎▁···▁▁▂·▄▄██ beats 41/56avg 2.3%▇▇▇▇▇▇▇▇▇▇▇▇▇ 33%blunt
Open ASR English short-form WER (March 2026)7.5% ✎▁▁▁▃··▁▁▂██▆ beats 47/86avg 8.2%▇▇▇▇▇▇▇▇▇▇ 26%blunt
FLEURS English test WER4.6% ✎▁··▁▁▁▁▁▁▃█▁ beats 35/56avg 5.8%▇▇▇▇▇ 13%blunt

$ where to run it

Other ways to run it

  1. Run it as published · needs ~3.48 GB, NVIDIA only · from huggingface.co/nvidia (official)
    uvx --with 'nemo_toolkit[asr]' python -c "import nemo.collections.asr as a; print(a.models.ASRModel.from_pretrained('nvidia/parakeet-rnnt-0.6b').transcribe(['audio.wav']))"

Official weights and documentation: huggingface.co/nvidia ↗

$ how the score was worked out · Open ASR Leaderboard 51%, transcribe.cpp model catalog… 46%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
Open ASR Leaderboard2▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 51%Apache-2.0
transcribe.cpp model catalog · LibriSpeech test-clean WER of validated full-precision ports2▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 46%MIT
prior · pull toward the middle▇ 3%
weighted average= 56.6

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json