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Parakeet CTC 1.1B

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Parakeet CTC 1.1B is an open-weights speech-to-text model from NVIDIA, released January 2024. Its published size is 1.06B parameters. Licence: cc-by-4.0.

Catalogue identity notes

Exact checkpoint nvidia/parakeet-ctc-1.1b (NeMo). Params: HF safetensors total. released aligned with catalogue sibling parakeet-rnnt-1-1b (repo created 2023-12-28).

54.8±2.7#74 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 CTC 1.1B, out of 176 with a score, then more from NVIDIA.

#modelscorewhereRAMreleased
83Omnilingual ASR CTC 3B v2 Meta55±3local~7.9GB2025-12
84Omnilingual ASR CTC 7B Meta55±2local~16GB2025-11
85Parakeet CTC 1.1B NVIDIA55±3local~5.6GB2024-01
86SeamlessM4T v2 Large Meta55±4local~9.2GB2023-11
87Qwen3-ASR-0.6B Alibaba (Qwen)55±1local~2.8GB2026-01
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▁▁▄▁·▁▂█▄▁▁▁ #10 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 Rev AI Machine (Reverb ASR) here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Open ASR English long-form WER9.3% ✎▁▁▅▁·▃▅█▆▁▂▂ beats 18/30avg 12%▇▇▇▇▇▇▇▇▇▇ 26%typical
other tests3 not in a task · 72% of its score
testresultvs the others in its rundifficultycountswhy
LibriSpeech test-clean WER1.8% ✎▁···▁▁▂·▄▄██ beats 34/56avg 2.3%▇▇▇▇▇▇▇▇▇▇▇▇▇ 33%blunt
Open ASR English short-form WER (March 2026)7.4% ✎▁▁▁▃··▁▁▂██▆ beats 52/86avg 8.2%▇▇▇▇▇▇▇▇▇▇ 26%blunt
FLEURS English test WER5.6% ✎▁··▁▁▁▁▁▁▃█▁ beats 19/56avg 5.8%▇▇▇▇▇ 13%blunt

$ where to run it

Other ways to run it

  1. Run it as published · needs ~5.59 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-ctc-1.1b').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= 54.8

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data updated · sources and licences · json