handy-computer/whisper-large-v3-gguf

automatic-speech-recognition model by handy-computer

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GGUF conversions of openai/whisper-large-v3 for use with transcribe.cpp. Ported from upstream commit 06f233f, pinned 2026-04-25. Validated against the transformers reference at transcribe.cpp commit 5.6.1 on 2026-04-26. OpenAI Whisper large-v3 — converted to GGUF for transcribe.cpp. Multilingual transcription, language detection, and speech translation (audio in any supported language → English text). v3 family adds Cantonese (yue) and uses a 128-bin mel input. Encoder-decoder transformer; 30-second windows with chunked long-form decoding. WER on the full LibriSpeech test-clean split (2,620 utterances), batch size 1, timestamps none. Figures without a commit were published before provenance was recorded. OpenAI's self-reported number on the same split is 2.01%. Both are short-form WER decoded without timestamps; OpenAI does not publish its exact evaluation configuration, so small differences are expected. Single-run figures: GPU reductions can shift corpus WER by about 0.1pp between runs, mostly on short-clip hallucination outcomes at the noise floor. Build transcribe.cpp from source: Run on…

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Model overview

handy-computer/whisper-large-v3-gguf is a automatic-speech-recognition model repository published by handy-computer on Hugging Face. The source reports the transcribe.cpp library.

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Model facts

Task: automatic-speech-recognition
Library: transcribe.cpp
Recent downloads (30 days): 307,526
Cumulative likes: 2
Hugging Face trending score: 0
Reported safetensors parameters: Not reported
Architecture: Not reported
License: apache-2.0
Access: Not gated by Hugging Face
Created: 2026-04-27T02:20:38.000Z
Last modified: 2026-09-15T07:05:47.000Z

Compatibility and lineage

Reported languages: af, am, ar, as, az, ba, be, bg, bn, bo, br, bs, ca, cs, cy, da, de, el, en, es, et, eu, fa, fi, fo, fr, gl, gu, haw, ha, he, hi, hr, ht, hu, hy, id, is, it, ja

Reported base models: openai/whisper-large-v3

Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.

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