danielrosehill/English-Hebrew-Codeswitching-Sentences

English-Hebrew Code-Switching Sentences

English sentences with everyday Hebrew words and Israeli names written in Latin letters, read aloud by one English speaker living in Israel. Hebrew words follow one canonical spelling convention, and Hebrew proper nouns are capitalised (Yerushalayim, Bituach Leumi).

Website: hebrish.app: get the Hebrish keyboard for Android and the desktop apps, see the results, and find every model, dataset and demo in one place.

This is a subset of danielrosehill/Read-Aloud-Voice-Sentences, the full, undifferentiated dataset. It contains every phone recording there whose categories include code_switch. Rows are copied unchanged, with the same ids, columns and train/test split, by scripts/sync_subsets.py in the project repository. Do not edit this dataset directly: fix the row in the main dataset and re-sync. The subset grows as new takes are published.

Speaker: Daniel Rosehill, the dataset's author, who was born in Ireland and speaks English with an Irish accent, and who lives in Jerusalem. Every recording is his voice.

All audio was recorded on a OnePlus Nord 3 5G phone using its internal microphone, with browser echo cancellation, noise suppression and auto gain turned off.

Browse and listen by term: English-Hebrew Code-Switching Browser. Every clip, by id: hebrish.app/archive, a listenable catalogue where each clip's id links to it (https://hebrish.app/archive#<id>).

Used in

This dataset trains danielrosehill/Parakeet-TDT-0.6B-v2-English-Hebrew-Codeswitching. That model runs on-device in Parlance, the project's dictation app, which offers it alongside stock Parakeet: an Android APK (voice keyboard), an Ubuntu / Debian .deb and a Windows .zip.

Models trained on this dataset, and their results

Parakeet v1.0 (Jachnun) and a streaming Nemotron fine-tune were trained on this data and benchmarked on 2026-10-19. On the public English-Hebrew-Codeswitching-ASR-Benchmark (test set v1, clean subset: 84 clips, 172 term occurrences), v1.0 writes 73.3% of the Hebrew terms correctly, against 32.6% for stock Parakeet TDT 0.6B v2, at 3.3% WER against 9.5%.

One speaker's voice: every training and test clip is the same speaker, so these numbers rank models on that speaker only. Full results: hebrish.app/benchmarks; all the datasets: hebrish.app/datasets.

Pronunciation handling

One of the less obvious facets of this kind of code-switching is that the words switched into English do not come from one language with one pronunciation. Some are Modern Hebrew, some are Yiddish, and even within Hebrew there is more than one pronunciation tradition. Modern Israeli Hebrew uses a Sephardi-based pronunciation (Shabbat, Sukkot, brit); many English-speaking Jews use the Ashkenazi pronunciation of the same words, especially religious ones (Shabbos, Sukkos, bris), and one speaker can move between the two in a single conversation. On top of that is the speaker's own accent in English. That makes this a harder target than code-switching with a single donor language.

Methodology. Variants are recorded deliberately: the speaker reads some Hebrew words in more than one pronunciation, so the same term appears in the dataset said in different ways. The transcript follows the sentence's spelling: "Shabbat" read the Ashkenazi way is still written "Shabbat", and only a sentence that spells the Ashkenazi form ("Shabbos") is transcribed that way.

Tagging. For the best results the variants should be tagged, so models can be scored, and the data balanced, per pronunciation. Since 2026-10-19 each take has a hebrew_pronunciation column:

Value	Meaning
israeli	Modern Israeli (Sephardi-based) pronunciation
ashkenazi	Ashkenazi pronunciation
mixed	Both within the one take
null	Not tagged

Rows recorded before then are null, except those whose transcript already spells an Ashkenazi form, which were filled as ashkenazi. Earlier takes that were read in a deliberate variant are still untagged and will be retro-tagged by listening, so a null does not mean Israeli pronunciation.

Layout

data/train/audio/<id>.wav    16 kHz mono 16-bit PCM
data/train/metadata.jsonl
data/test/...                held-out set, never trained on

The test split is term-disjoint: a held-out term never appears in train. load_dataset("danielrosehill/English-Hebrew-Codeswitching-Sentences") works directly.

Columns

The columns are the same as in the main dataset: text (ground truth, punctuated and cased like Parakeet v2 output), text_he (the same transcript with its Hebrew terms in Hebrew script, for Hebrew-capable models), categories, target_terms, prompt_source, prompt_origin (manual / dataset / generated), edited, duration_s, environment, conditions, device, speaker, speaker_gender, hebrew_pronunciation and recorded_at. The main dataset's card describes them.

Licence

CC-BY-4.0, with sentence-text attribution per prompt_source as described in the main dataset's licence section.