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:
Tasks:
Automatic Speech Recognition
Formats:
soundfolder
Languages:
English
Size:
< 1K
DOI:
License:
audio audio | id string | text string | text_he string | duration_s float64 | categories list | target_terms list | prompt_id string | prompt_source string | prompt_origin string | mode string | edited bool | device string | microphone string | environment string | conditions list | noise_level null | background_speech_languages list | noise_flags list | clipped bool | notes null | hebrew_pronunciation null | variant_groups list | variant_spelling null | task string | spoken_text null | aside_text null | aside_position null | verbatim_text null | prompt_categories list | source_prompt_id string | prompt_kind null | prompt_note null | term_language null | paragraph_text null | model_hypothesis null | asr_model null | speaker string | speaker_gender string | sample_rate int64 | recorded_at string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
20261020T134127Z_6a0j8h | Just following up on my last email, since I haven't heard back and I want to make sure it didn't get lost. | Just following up on my last email, since I haven't heard back and I want to make sure it didn't get lost. | 7.266 | [
"weird-voices"
] | [] | weird-voices_p_32ea17e14e | generated:anthropic/claude-sonnet-5.5 | generated | bank | false | OnePlus Nord 3 5G | internal | quiet-indoor | [] | null | [] | [] | false | null | null | [] | null | weird-voices | null | null | null | null | [
"email"
] | p_32ea17e14e | null | null | null | null | null | null | Daniel Rosehill | male | 16,000 | 2026-10-20T13:41:27.941Z |
Weird Voice Sentences
Experimental. Plain English sentences read by one speaker in a put-on voice (very deep, squeaky, robot, cartoon, mock accent, mumbly). text is the sentence as written.
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 weird-voices (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/Weird-Voice-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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