Hebrish/Noisy-Condition-Sentences

Noisy Condition Sentences

Every take from the main dataset recorded in noise, whatever its category: outdoors, in a noisy room, with traffic, other people talking, background noise or a crying baby. A robustness and evaluation set for dictation in real conditions. environment is where the take was recorded, conditions the sticky conditions set in the app, noise_flags the per-clip flags (crying-baby, significant-noise, …).

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 with any noise_flags or conditions, or environment of outdoor or noisy-indoor; a take later labelled by listening counts as labelled (noise sources, background speech or music, or a noisy environment: in; judged clean: out). 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.

Listen: what Hebrish is and how it's made

A spoken introduction to the project by the person who makes it: why Hebrish exists, why stock speech-to-text gets Hebrew words wrong, how the fine-tunes are trained, the model lines (Sabich, Jachnun, Shakshuka, Meorav), how the training data is recorded, and the plans for the apps.

Listen	hebrish.app/updates/2026-10-22: player, chapters, edited transcript, MP3 download
Author	Daniel Rosehill
Recorded	2026-10-22
Runtime	1:07:03

Chapters (each opens the player at that point): 0:00 Why this voice note · 1:36 Coming to speech to text · 6:36 Why Hebrew words go wrong · 9:23 How the fine-tune works · 12:27 Why Parakeet, and the model plan · 17:44 Disfluencies, shekels, paragraphs · 25:40 Tech vocabulary and Meorav · 29:07 The kitchen sink: Sabich and Jachnun · 33:35 Licensing, accents, noisy recordings · 39:10 One recorder for ASR and TTS · 41:04 Desktop shortcuts and the macro pad · 43:34 Android: Bluetooth and one button · 45:35 Streaming and Shakshuka · 50:28 Long-form transcription · 56:00 Output modes · 63:01 Report a word, and more voices

Layout

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/Noisy-Condition-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.