| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| - he |
| task_categories: |
| - automatic-speech-recognition |
| pretty_name: Pronunciation Variants |
| tags: |
| - speech |
| - asr |
| - fine-tuning |
| - single-speaker |
| - pronunciation-variants |
| - robustness |
| - english |
| - code-switching |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-*.parquet |
| - split: test |
| path: data/test-*.parquet |
| --- |
| # Pronunciation Variants |
|
|
| Takes in which a word was **deliberately said one particular way**, from the main dataset, stratified by `variant_category`. Two kinds: a term from the project's pronunciation registry said in a recorded variant (`pronunciation_variants`, a list of `{term, variant, category}`; first case: **Parquet**, said par-KET like the English floor word rather than the usual tech reading par-KAY), and a Hebrew word read in a non-Israeli way (`hebrew_pronunciation` of `ashkenazi`, `anglicized` or `mispronounced`). **The transcript is always the canonical spelling** (`Parquet`, `Shabbat`) whichever way it was said: the point is a model that writes the same word for every common pronunciation of it. These takes stay in training in the main dataset too. |
|
|
| `variant_category`: `cognate` (a word shared with another language, read the other language's way), `loanword-stress`, `brand`, `acronym` (letters vs a word), `ashkenazi` (Hebrew word, Ashkenazi reading) or `hebrew` (Hebrew word, anglicized or mispronounced). |
|
|
| **Website: [hebrish.app](https://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`](https://huggingface.co/datasets/danielrosehill/Read-Aloud-Voice-Sentences)**, the full, undifferentiated |
| dataset. It contains every phone recording there with a non-empty `pronunciation_variants`, or `hebrew_pronunciation` of `ashkenazi`, `anglicized` or `mispronounced`; each row adds `variant_category`. 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. |
|
|
| ## 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. |
|
|
| **Pairs.** Since 2026-10-20 each row also has `variant_groups`, the ids from |
| `data/pronunciation-variants.json` that link the Israeli and Ashkenazi forms of one word (`shabbat` |
| for Shabbat / Shabbos, `tosafot` for Tosafot / Tosfos), and `variant_spelling`: `israeli`, |
| `ashkenazi`, `mixed`, or null when the transcript has no word that is spelled two ways. Sentences |
| written for an Ashkenazi form come pre-tagged `ashkenazi` in the capture app, so pairs are labelled |
| as they are recorded. Words spelled the same in both traditions (Torah, Pesach, Kiddush) differ only |
| in `hebrew_pronunciation`. |
|
|
| ## Layout |
|
|
| ``` |
| data/train-*.parquet one row per take: `audio` (the FLAC or WAV file, embedded) and every column |
| meta/train.jsonl index: the same rows without audio, plus `shard`, `audio_path` and the audio's byte range |
| meta/schema.json column types |
| data/test-*.parquet, meta/test.jsonl held-out set, never trained on |
| ``` |
|
|
| Audio is 48 kHz mono 24-bit FLAC for rows from 2026-10-22 and 16 kHz mono 16-bit WAV before. |
|
|
| The `test` split is **term-disjoint**: a held-out term never appears in `train`. |
| `load_dataset("Hebrish/Pronunciation-Variants")` 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](https://huggingface.co/datasets/danielrosehill/Read-Aloud-Voice-Sentences#licence-and-attribution). |
|
|
