Parakeet V2 Fine-Tune - Training Data
Collection
Audio the Parakeet v2 fine-tunes blend from: master dataset, category and task subsets, imports, one blend dataset per model. • 27 items • Updated
Datasets:
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Currency Sentences
One speaker saying amounts of money the natural way ("50 shekels", "a dollar fifty", "6 pounds 50"). spoken_text is the script that was read; text is the written form a dictation model should give: 50 NIS, $1.50, €18, £6.50 (NIS rather than ₪, which the Parakeet v2 tokenizer cannot emit). prompt_kind is the currency: ILS, USD, EUR, GBP, mixed, other (yen, francs: written as words), or word (the currency named without an amount, which stays a word).
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 currency (the capture app's task of that name). 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.
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/Currency-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, 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.
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