Hebrish: English-Hebrew code-switching
Collection
Every Hebrish model, dataset and Space, including those still on danielrosehill. hebrish.app • 22 items • Updated
Datasets:
Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/folder_based_builder/folder_based_builder.py", line 211, in _split_generators
raise ValueError(
f"Metadata files {downloaded_metadata_file} and {features_per_metadata_file[0][0]} have different features: {features_per_metadata_file[0]} != {metadata_features}"
)
ValueError: Metadata files hf://datasets/Hebrish/Yiddish-Sentences@de8ccc3c8d96f5a9c52970b77da4f769128572ca/data/test/metadata.jsonl and hf://datasets/Hebrish/Yiddish-Sentences@de8ccc3c8d96f5a9c52970b77da4f769128572ca/data/train/metadata.jsonl have different features: ('hf://datasets/Hebrish/Yiddish-Sentences@de8ccc3c8d96f5a9c52970b77da4f769128572ca/data/train/metadata.jsonl', {'file_name': Value('string'), 'id': Value('string'), 'text': Value('string'), 'duration_s': Value('float64'), 'categories': List(Value('string')), 'target_terms': List(Value('string')), 'prompt_id': Value('string'), 'prompt_source': Value('string'), 'mode': Value('string'), 'edited': Value('bool'), 'device': Value('string'), 'environment': Value('string'), 'conditions': List(Value('null')), 'sample_rate': Value('int64'), 'recorded_at': Value('string'), 'microphone': Value('string'), 'prompt_origin': Value('string'), 'hebrew_pronunciation': Value('string'), 'text_he': Value('string'), 'noise_level': Value('null'), 'background_speech_languages': List(Value('null')), 'notes': Value('null'), 'model_hypothesis': Value('null'), 'asr_model': Value('null'), 'speaker': Value('string'), 'speaker_gender': Value('string')}) != {'file_name': Value('string'), 'id': Value('string'), 'text': Value('string'), 'duration_s': Value('float64'), 'categories': List(Value('string')), 'target_terms': List(Value('string')), 'prompt_id': Value('string'), 'prompt_source': Value('string'), 'mode': Value('string'), 'edited': Value('bool'), 'device': Value('string'), 'environment': Value('string'), 'sample_rate': Value('int64'), 'recorded_at': Value('string'), 'microphone': Value('string'), 'prompt_origin': Value('string'), 'hebrew_pronunciation': Value('null'), 'conditions': List(Value('null')), 'text_he': Value('string'), 'noise_level': Value('null'), 'background_speech_languages': List(Value('null')), 'notes': Value('null'), 'model_hypothesis': Value('null'), 'asr_model': Value('null'), 'speaker': Value('string'), 'speaker_gender': Value('string')}
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Yiddish Sentences
English sentences with Yiddish words written in Latin letters (shul, daven, cholent, mensch, freier, tachles), the way English speakers in Israel use them, read aloud by one speaker. Many of these words are also used in modern Hebrew. Yiddish counts as code-switching here, so every row is also in English-Hebrew-Codeswitching-Sentences. The word list is data/yiddish-terms.json in the project repository.
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 yiddish. 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.
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("Hebrish/Yiddish-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.
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