Whisper Hebrish Small ACFT

At a glance

Formats and quants

Hugging Face issues a DOI per repository, so each format can be cited on its own. Repositories, sizes and DOIs checked on the Hub on 2026-10-20.

Versions

About it, and suggested implementations

A full fine-tune of openai/whisper-small.en, then FUTO's ACFT procedure. FUTO Keyboard runs Whisper with a dynamic audio context: it only processes as much of the 30-second window as the clip is long, which makes short dictations fast. Stock and ordinary fine-tuned Whisper models break under that setting, repeating themselves or never stopping. ACFT fixes it. The base is English-only, so it never writes Hebrew script.

Training data. Read-Aloud-Voice-Sentences at revision 36845b0d (2026-10-17), the same parent dataset the Parakeet and Nemotron fine-tunes use, but two days earlier: 152.5 minutes of code-switched speech against 339.2 for their latest checkpoints. Whisper Hebrish goes back further: its first version (2025-11-17) was trained on 516 desktop clips in English-Hebrew-Mixed-Sentences, recorded that day. 498 of those clips were later brought into Read-Aloud-Voice-Sentences with their spellings made canonical, so they are part of what every current model trains on.

FUTO Keyboard (Android): download ggml-whisper-hebrish-small-en-acft-q8_0.bin (264 MB) on the phone. Open it with FUTO Keyboard, or go to Settings → Languages & Models → Import resource file, and choose English when asked. Revert to Default on the same screen undoes it. These steps come from FUTO Keyboard's source code and were not checked on a phone.

whisper.cpp, with the same dynamic context FUTO uses (for a 6-second clip, 6 × 50 + 32 = 332):

whisper-cli -m ggml-whisper-hebrish-small-en-acft-q8_0.bin -f clip.wav -bs 5 -ac 332

Transformers:

from transformers import pipeline
asr = pipeline("automatic-speech-recognition", model="Hebrish/Whisper-Hebrish-Small-ACFT")
print(asr("clip.wav")["text"])   # English-only model: do not pass language/task

Results from the model card, in FUTO's own runtime (q8_0, beam 5), on a 155-sentence code-switching test set whose Hebrew terms were never trained on:

This test set is not the one Parakeet's benchmark uses, so these numbers do not compare with Parakeet's. Clips longer than 30 seconds score much worse (21 to 27% WER). A larger sibling, Whisper Hebrish on whisper-large-v3-turbo (809M), scored 5.2% on the same set at full context. Both are MIT-licensed.

Cite

Cite Whisper Hebrish Small ACFT with its DOI, 10.57967/hf/11106 (Hebrish/Whisper-Hebrish-Small-ACFT). To cite one format, use that repository's DOI from the table above.

@misc{rosehill_whisper_small_2026,
  author    = {Daniel Rosehill},
  title     = {Whisper-Hebrish-Small-ACFT},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/Hebrish/Whisper-Hebrish-Small-ACFT},
  doi       = {10.57967/hf/11106}
}

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