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Sabich v4.0

Sabich is the speech recognition model in the Hebrish apps: English dictation with Hebrew (and some Yiddish) words dropped in, written in Latin letters, plus tech vocabulary, numbers and Israeli names. Fine-tuned from nvidia/parakeet-tdt-0.6b-v2. This repository is the line the apps use and keep updating; the older Hebrish/Sabich (v1.0 to v2.4) is no longer updated.

v4.0 (2026-11-10): a fresh baseline, trained from scratch

Every release in this repository before it (v2.5 to v3.4, from 2026-11-02) was an incremental update that continued from the previous weights. v4.0 is not: it was trained from scratch from stock nvidia/parakeet-tdt-0.6b-v2 on the whole, freshly standardised dataset, and it is the new baseline that later updates continue from.

Dataset standardisation first (2026-11-09). One set of transcript conventions was settled and applied to every published take: 7 PM and 4:30 AM; units abbreviated with a space (150 km); digits for 10 and above and words for one to nine in ordinary counts (quoted texts such as Tanach and Talmud exempt); times in digits; one spelling for each same-sounding term (kupat cholim, Be'er Sheva); different words kept apart (Jaffa / Yafo, Ram / RAM); names never respelled; canonical Hebrew romanisations and proper-noun casing; shekel amounts as NIS. Changed rows keep their old text in text_before_normalization. Each topic also became its own Hebrish dataset in one standard format. The aim is consistent formatting in the output.
Training compute: Nebius (Nebius AI Cloud, Serverless AI jobs, one NVIDIA H100, eu-north1). The first Hebrish model trained there; earlier releases were trained on Modal.
Recipe: 20 epochs, learning rate 3e-5, batch 16, speed perturbation 0.9 to 1.1x, public-English replay (LibriSpeech-PC). 192 minutes of training. The released weights are the best epoch (17).
Held-out validation WER: 4.82% on 1,857 clips never trained on. The validation set grows with the data, so this is not comparable with earlier releases' numbers, and it is not a benchmark.

Dataset at training time

Master dataset revision b87b8df (2026-11-10): 29,099 clips, 2,754 minutes (about 45.9 hours) of unique audio, recorded 2025-11-17 to 2026-11-10. Priority-word clips are repeated three times, so the training set was 32,619 rows (3,029 minutes).

Source	Minutes
Daniel's own recordings (phone)	2,536
LibriSpeech-PC (public English replay)	191
EHMS import	27

Code-switching sentences: 1,106 minutes; everything else (tech, names, general English, tasks): 1,648 minutes.

Category (first tag)	Clips	Minutes
tech	7,007	668
message	3,724	394
code_switch	2,660	225
names	2,512	187
voice-commands	2,418	87
email	1,607	199
general	1,089	105
librispeech	900	191
israeli-streets	577	54
dates-times	406	29
brand-names	362	40
whispered	292	27
tanach	291	36
numerics	282	19
irish-english	242	22
other tasks (architecture, October 7, rabbis, web and email addresses, Talmud, rabbinic works, stories, ...)	4,730	471

Earlier versions

v2.5 to v3.4 (2026-11-02 to 2026-11-10): incremental updates, each continuing from the previous weights on the cumulative recordings; the first started from Sabich v2.4.
v1.0 to v2.4: Hebrish/Sabich.

Files

Sabich-Rolling.nemo: the NeMo model (ASRModel.restore_from("Sabich-Rolling.nemo")).
Hebrish/Sabich-Rolling-sherpa-onnx-int8: the int8 sherpa-onnx build the apps use.
training/<run>/config.json and result.json: the exact settings and result of each run.

Contact: access@hebrish.app