Datasets:

Hebrish
/

Israeli-Name-Sentences

license: cc-by-4.0
language:
  - en
  - he
task_categories:
  - automatic-speech-recognition
pretty_name: Israeli Name Sentences
tags:
  - speech
  - asr
  - fine-tuning
  - single-speaker
  - code-switching
  - hebrew
  - english
  - names
  - israeli-names
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test-*.parquet
      - split: train
        path: data/train-*.parquet

Israeli Name Sentences

English messages and emails that mention an Israeli or Jewish person's name, a first name (Naftali, Yotam, Tzlil, Bat-El) or a surname (Buskila, Ifergan, Weinberg), written in its common Israeli-English spelling, read aloud by one speaker. Each sentence also has one everyday Hebrew word in Latin letters. target_terms gives the name. Rows start with the prompts added on 2026-10-29; name sentences recorded before then are in English-Hebrew-Codeswitching-Sentences only. Every row is also there.

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 person_names. 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. Deliberate variation: he sometimes changes his speaking speed on purpose (fast and rushed, or slow) and sometimes says a word the way people commonly mispronounce it, to make the models robust to real speech. The transcript is always the sentence as written, so a take whose speed or pronunciation sounds off against its card is usually intentional, not a recording error.

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.

Layout

data/train/audio/<YYYY-MM-DD>/<id>.flac   48 kHz mono 24-bit FLAC (rows from 2026-10-29)
data/train/audio/<id>.flac   48 kHz mono 24-bit FLAC (rows from 2026-10-22)
data/train/audio/<id>.wav    16 kHz mono 16-bit PCM (earlier rows)
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("Hebrish/Israeli-Name-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, contributor, hebrew_pronunciation, variant_groups, variant_spelling 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.