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STT En Conformer-Transducer Small

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STT En Conformer-Transducer Small is an open-weights speech-to-text model from NVIDIA, released September 2021. Its published size is 0.014B parameters. Licence: ngc-terms-of-use.

Catalogue identity notes

NeMo NGC model stt_en_conformer_transducer_small (no Hugging Face repo; the CSV entry 'nvidia/stt_en_conformer_transducer_small' is its NeMo pretrained name). NGC latest version 1.6.0, created 2021-09-16; licence: NGC Terms of Use. Params: NGC description (~14M).

42.6±3.2#131 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 STT En Conformer-Transducer Small, out of 176 with a score, then more from NVIDIA.

#modelscorewhereRAMreleased
142Whisper medium OpenAI43±2local~2.3GB2022-09
143Omnilingual ASR CTC 300M v2 Meta43±3local~1.3GB2025-12
144STT En Conformer-Transducer Small NVIDIA43±3local~0.6GB2021-09
145ESPnet Conformer 2020 + RNN LM ESPnet43±4local~1GB2020-11
146Vosk en-us 0.22 Alpha Cephei (Vosk)42±6local~16GB2021-10
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▁▁▄▃·▁▂█▄▁▁▁ #21 of 27 * · 35% of its score · 1 test

Beats Distil-Whisper distil-large-v2, STT En Conformer-Transducer Large, STT En FastConformer-Transducer Large here, though they rank higher overall.

testresultvs the others in its rundifficultycountswhy
Open ASR English long-form WER13.7% ✎▁▁▅▃·▃▅█▆▁▂▂ beats 6/30avg 12%▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 35%typical
other tests3 not in a task · 61% of its score
testresultvs the others in its rundifficultycountswhy
Open ASR English short-form WER (March 2026)10.3% ✎▁▁▁▃··▁▁▃██▆ beats 20/86avg 8.2%▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 35%blunt
LibriSpeech test-other WER lab6.6% ✎▁▁▁▂▅▃▅▄█▂▂▁ beats 7/54avg 3.5%▇▇▇▇▇▇▇▇ 21%typical
LibriSpeech test-clean WER lab2.5% ✎▁·▁·▁▁▁▁▃▇█▆ beats 32/100avg 1.8%▇▇ 6.0%blunt

$ where to run it

Other ways to run it

  1. Run it as published · needs ~0.57 GB, NVIDIA only · from catalog.ngc.nvidia.com (official)
    uvx --with 'nemo_toolkit[asr]' python -c "import nemo.collections.asr as a; print(a.models.ASRModel.from_pretrained('stt_en_conformer_transducer_small').transcribe(['audio.wav']))"
$ how the score was worked out · Open ASR Leaderboard 69%, NVIDIA NeMo docs: ASR scores… 27%

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▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 69%Apache-2.0
NVIDIA NeMo docs: ASR scores tables (English Conformer / FastConformer) · lab-reported2▇▇▇▇▇▇▇▇ 27%Apache-2.0
prior · pull toward the middle▇ 4%
weighted average= 42.6

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data updated · sources and licences · json