danielrosehill/Read-Aloud-Voice-Sentences

Read-Aloud Voice Sentences

Read-aloud sentences from one speaker, recorded on a phone (OnePlus Nord 3 5G, internal microphone).

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, including the imported ehms_* rows, is his voice. They are intended for fine-tuning nvidia/parakeet-tdt-0.6b-v2 on a single speaker's voice, for use with on-device dictation.

The dataset grows over time. Batches are pushed straight from the capture app on the phone.

Recording device. Every phone take was recorded on a OnePlus Nord 3 5G using its internal microphone, with browser echo cancellation, noise suppression and auto gain turned off. The exception is the ehms_* rows (mode: "import", device: null). These were imported from danielrosehill/English-Hebrew-Mixed-Sentences, recorded 2025-11-17 on another device, and their transcripts were updated to this dataset's spelling and casing (the original wording is in original_text). Filter on mode != "import" for phone-only data.

Subsets. This is the full, undifferentiated dataset. Two derived datasets carry its phone takes for one kind of data, unchanged (same ids, columns and split), and are updated on every push:

• danielrosehill/English-Hebrew-Codeswitching-Sentences: rows tagged code_switch
• danielrosehill/Tech-Sentences: rows tagged tech

Contents

Category	What the sentences contain
code_switch	English with everyday Hebrew words written in Latin letters. The spellings follow a single canonical convention.
names	Jewish and Israeli first names, surnames and Israeli place names, each with one canonical spelling
email / message	Sentences in the style of dictated emails, WhatsApp messages and notes to self. This is the main use case: email dictation on Android.
tech	Software, DevOps, cloud and AI vocabulary
homophone	Sentences whose context settles which spelling of a homophone is meant
general	Everyday spoken English
target_word / typed	Sentences generated on demand around one word, or typed in by the speaker

Layout

The dataset uses the standard audiofolder layout, so load_dataset("danielrosehill/Read-Aloud-Voice-Sentences") works directly.

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 for code_switch and tech. A held-out Hebrew or technical term never appears in train, so the evaluation measures generalisation to unseen vocabulary.

Columns

Column	Meaning
text	Ground-truth transcript: punctuated and cased, to match Parakeet v2's output style
categories, target_terms	What the sentence exercises, and which term the evaluation scores
prompt_source	Where the sentence text came from (see Licence)
edited	true when the speaker corrected the prompt before or after reading it
duration_s, environment, device, recorded_at	Recording metadata
conditions	Recording conditions noted on the phone (background-noise, recorded-outdoors, in-car, ...); missing on the earliest rows

Filtering by type

A row can have several categories (code_switch, tech, names, long, email, message, homophone, general), so one kind of data can be used on its own:

from datasets import load_dataset
ds = load_dataset("danielrosehill/Read-Aloud-Voice-Sentences")
code_switch = ds["train"].filter(lambda r: "code_switch" in r["categories"])

Licence and attribution

• Audio and transcripts: CC-BY-4.0.
• Sentence text whose prompt_source starts with tatoeba:<id> comes from Tatoeba under CC-BY 2.0 FR. The id identifies the source sentence: https://tatoeba.org/en/sentences/show/<id>.
• Sentence text whose prompt_source starts with generated: was written for this dataset by the named model. A second model from a different family then reviewed it for correct Hebrew usage and natural phrasing. It is released under CC-BY-4.0 with the rest of the dataset.
• Sentence text whose prompt_source starts with enron:<message-id number> is a single sentence taken from the Enron email corpus, which the US Federal Energy Regulatory Commission made public and CMU redistributes. Its licence status is inferred (public record); it has never been formally licensed. Sentences were filtered and reviewed to exclude names, personal content and identifying details.
• Sentence text from english-hebrew-mixed-sentences and tech-sentences-for-asr-training comes from the author's earlier datasets, danielrosehill/English-Hebrew-Mixed-Sentences and danielrosehill/Tech-Sentences-For-ASR-Training, both MIT.