whatmodel.sh / stt / LiteWhisper large-v3-turbo-acc

LiteWhisper large-v3-turbo-acc

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LiteWhisper large-v3-turbo-acc is an open-weights speech-to-text model from Efficient-Speech (LiteASR), released February 2025. Its published size is 0.66B parameters. Licence: apache-2.0.

Catalogue identity notes

Exact checkpoint efficient-speech/lite-whisper-large-v3-turbo-acc (low-rank compressed Whisper encoder; custom code, trust_remote_code). Params: HF safetensors total. released: HF repo creation month. Command follows the card snippet (processor from openai/whisper-large-v3).

52.8±4.0#84 of 162 speech-to-text modelsfrom 2 benchmark results across 2 sources* thinly tested, treat the rank as provisional

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 LiteWhisper large-v3-turbo-acc, out of 176 with a score, then more from Efficient-Speech (LiteASR).

#modelscorewhereRAMreleased
93MOSS-Transcribe-Diarize 0.9B OpenMOSS54±4local~1GB2026-07
94GPT-4o Transcribe OpenAI53±1api $0.0060/audio min2025-03
95LiteWhisper large-v3-turbo-acc Efficient-Speech (LiteASR)53±4local~2.1GB2025-02
96Voxtral Mini 4B Realtime (2602) Mistral53±2api $0.0060/audio min +local~3.2GB2026-02
97Parakeet primeline (German) primeLine53±4local~0.7GB2025-12
more from Efficient-Speech (LiteASR)
79LiteWhisper large-v3 Efficient-Speech (LiteASR)55±3local~3.7GB2025-02
82LiteWhisper large-v3-acc Efficient-Speech (LiteASR)55±3local~3.9GB2025-02
101LiteWhisper large-v3-fast Efficient-Speech (LiteASR)52±3local~3.6GB2025-02

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

$ what it's good at

other tests2 not in a task · 94% of its score
testresultvs the others in its rundifficultycountswhy
Open ASR English short-form WER (March 2026)7.8% ✎▁▁▁▃··▁▁▂██▆ beats 42/86avg 8.2%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 56%blunt
LiteASR paper · ESB 8-set average WER lab10.2% ✎▂·······▂··█ beats 2/6avg 13%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 38%typical

$ where to run it

Other ways to run it

  1. Run it as published · needs ~2.08 GB, NVIDIA only · from huggingface.co/efficient-speech (official)
    uv run --with transformers --with torch --with librosa python -c "import librosa, torch; from transformers import AutoProcessor, AutoModel; m=AutoModel.from_pretrained('efficient-speech/lite-whisper-large-v3-turbo-acc', trust_remote_code=True).to(torch.float16).to('cuda'); p=AutoProcessor.from_pretrained('openai/whisper-large-v3'); f=p(librosa.load('audio.wav', sr=16000)[0], sampling_rate=16000, return_tensors='pt').input_features.to(torch.float16).to('cuda'); print(p.batch_decode(m.generate(f), skip_special_tokens=True)[0])"

Official weights and documentation: huggingface.co/efficient-speech ↗

$ how the score was worked out · Open ASR Leaderboard 56%, LiteASR (arXiv:2502.20583v2)… 38%

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 Leaderboard1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 56%Apache-2.0
LiteASR (arXiv:2502.20583v2) · ESB WER of Whisper and LiteASR-compressed encoders · lab-reported1▇▇▇▇▇▇▇▇▇▇▇ 38%CC BY 4.0
prior · pull toward the middle▇▇ 6%
weighted average= 52.8

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