whatmodel.sh / stt / Granite 4.0 1B Speech

Granite 4.0 1B Speech

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Granite 4.0 1B Speech is an open-weights speech-to-text model from IBM, released March 2026. Its published size is 2.31B parameters. Licence: apache-2.0.

Catalogue identity notes

Exact checkpoint ibm-granite/granite-4.0-1b-speech; distinct from catalogue granite-speech-4-1-2b and granite-speech-5-0-470m-turboctc. Card 'Release Date: March 6, 2026'. Params: HF safetensors total (2,313,207,132 BF16).

62.3±3.2#16 of 162 speech-to-text modelsfrom 3 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 Granite 4.0 1B Speech, out of 176 with a score, then more from IBM.

#modelscorewhereRAMreleased
17Universal-3 Pro AssemblyAI63±2comparison only2026-02
18Granite Speech 4.1 2B IBM62±3local~6GB2026-04
19Granite 4.0 1B Speech IBM62±3local~6GB2026-03
20Granite Speech 5.0 470M TurboCTC IBM62±4local~1.6GB2026-08
21MAI-Transcribe-1.5 Microsoft AI62±8api $0.0060/audio min2026-06
more from IBM
15Granite Speech 5.0 470M TurboCTC NC IBM64±4local~0.5GB2026-08
26Granite Speech 4.1 2B NAR IBM62±4local~2.5GB2026-03
34Granite Speech 4.1 2B Plus IBM61±4local~2.4GB2026-04

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

$ what it's good at

other tests3 not in a task · 96% of its score
testresultvs the others in its rundifficultycountswhy
LibriSpeech test-clean WER1.4% ✎▁···▁▁▂·▄▄██ beats 49/56avg 2.3%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 44%blunt
Open ASR English short-form WER (March 2026)5.5% ✎▁▁▁▃··▁▁▂██▆ beats 84/86avg 8.2%▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 35%blunt
FLEURS English test WER4.7% ✎▁··▁▁▁▁▁▁▃█▁ beats 32/56avg 5.8%▇▇▇▇▇▇▇ 18%blunt

$ where to run it

Other ways to run it

version

  1. Run it as published · needs ~6.04 GB, NVIDIA only · from huggingface.co/ibm-granite
    vllm serve ibm-granite/granite-4.0-1b-speech --max-model-len 2048

Weights and documentation: huggingface.co/ibm-granite ↗

$ how the score was worked out · transcribe.cpp model catalog… 62%, Open ASR Leaderboard 35%

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
transcribe.cpp model catalog · LibriSpeech test-clean WER of validated full-precision ports2▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 62%MIT
Open ASR Leaderboard1▇▇▇▇▇▇▇▇▇▇ 35%Apache-2.0
prior · pull toward the middle▇ 4%
weighted average= 62.3

$ improve this page

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data updated · sources and licences · json