Hebrish

Voice dictation for English speakers in Israel

Say it in Hebrish.
Get the Hebrew words right.

Speech recognition hears an English sentence with a Hebrew word in it and guesses an English word that sounds close. Hebrish is a fine-tune of NVIDIA's Parakeet that writes the Hebrew word itself, in Latin letters, and runs on your own phone or computer.

Stock Parakeet

I want to go to the Micollette today to buy my groceries.

Hebrish

I want to go to the makolet today to buy my groceries.

The same recording, through both models (2026-10-15). מכולת, makolet, is the corner grocery shop.

Words stock models trip over, and Hebrish writes as one consistent spelling:

  • teudat zehut
  • arnona
  • Bituach Leumi
  • makolet
  • cheshbonit
  • osek patur
  • tachana merkazit
  • Motzei Shabbat
  • sabich
  • kupat holim
  • misrad hapnim

Download Hebrish

Free. Runs entirely on your device. The model downloads once (about 660 MB) and the app checks now and then for a newer version.

Install steps and other platforms: download page. No install: try it in the browser.

The apps

Hebrish runs inside Parlance, a free dictation app for Android, Ubuntu and Windows. Pick the English-Hebrew code-switching model in it; Parlance lets you switch back to stock Parakeet in one tap.

Android keyboard

Parlance is a voice keyboard. Pick the code-switching model, tap the mic in HeliBoard, talk, and the text lands where you're typing. Try it now opens a notepad with a mic button, so you can test it before setting anything up.

Works with HeliBoard. Early build. Setup guide

Ubuntu

A tray app. Press a hotkey or a foot pedal anywhere, talk, and the text is pasted at your cursor. A Transcribe tab handles longer recordings and audio files.

Install: sudo apt install ./parlance_0.1.0-1_amd64.deb

Windows

The same tray app as a portable zip: unzip it and run Parlance.exe.

Early build, not yet tried on real hardware.

Parlance on Linux: the Model tab, choosing between stock Parakeet and the English-Hebrew code-switching model
Parlance on Ubuntu, choosing the model.
Other ways to run it
  • Offline Voice Input (Google Play): import the Q8_0 GGUF (730 MB), or the Q4_K_M GGUF (475 MB) on phones with 4 GB of RAM or less.
  • Handy on Linux (GitHub) loads the same GGUF.
  • Mac (Apple Silicon): the MLX build with pip install parakeet-mlx.
  • Python and GPUs: the full-precision .nemo model with NVIDIA NeMo.

How it works

Parakeet's vocabulary has no Hebrew letters, so Hebrish doesn't output Hebrew script. It learns one romanized spelling for each Hebrew word and writes that, punctuated and cased like the rest of the sentence.

It was trained on read-aloud sentences recorded on a phone: English with one or two Hebrew words dropped in, about 300 minutes so far. About 30% of the training audio is plain English, because fine-tuning on code-switched speech alone is known to wreck a model's English.

One voice, for now

The training recordings are one speaker's: an Irish-accented English speaker living in Jerusalem. It works best for voices like that. If yours is very different, stock Parakeet may suit you better; Parlance lets you switch between them in one tap.

Results

Version 3, scored on held-out recordings that were never trained on. Some of the Hebrew terms in the test set never appear in training at all.

TestStock ParakeetHebrish v3
Code-switched English, word error rate10.9%5.3%
Hebrew terms seen in training, written correctly29%66%
Hebrew terms never seen in training, written correctly27%48%
Plain English, same speaker, word error rate3.5%2.9%
LibriSpeech test-clean, word error rate1.2%1.0%
Long recordings (~3 min), word error rate9.0%5.0%

Lower word error rate is better. English got slightly better, not worse. Versions 4 and 5 are published and not yet benchmarked. On the separate public benchmark, v3 leads on Hebrew term recall at 67.4%, against 45.9% for Whisper Large V3 and 32.6% for stock Parakeet. Every version and how it was trained: model card. Full history: project tracker.