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Open Source

Models, datasets and tools Daniel publishes openly, with the data, training configuration and evaluation alongside them.

Featured · v3 released 2026-10-18

Hebrish

English–Hebrew code-switching speech recognition

English speakers in Israel drop Hebrew words into English all the time: “pick up bamba from the makolet”, “an appointment at Misrad Hapnim”. Speech-to-text models trained on English have never heard those words, so they guess an English-looking one. Stock Parakeet v2 hears “Micollette”; the fine-tune writes “makolet”.

Hebrish is on-device English dictation for English speakers in Israel that writes the Hebrew words correctly, in Latin letters (makolet, teudat zehut). It is a fine-tune of NVIDIA's Parakeet TDT 0.6B v2 on about three hours of code-switched speech from a single speaker. It runs offline in the Hebrish apps: an Android voice keyboard plus Ubuntu and Windows tray apps. In v3, code-switched WER fell from 10.9% to 5.3%, and plain English did not get worse. The model and the data are open, so the method and the results can be checked and reused.

v3 results against stock Parakeet v2

TestStock v2v3
Code-switching WER (219 held-out clips)10.9%5.3%
Hebrew terms seen in training, transcribed correctly29%66%
Hebrew terms never seen in training, transcribed correctly27%48%
Long-form WER (~3-minute files)9.0%5.0%
LibriSpeech test-clean WER (general English)1.2%1.0%

WER is normalised (lowercase, no punctuation). Full per-utterance results, the version history and the training configuration are on the model card; the project is tracked on GitHub.

Browse the dataset

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