---
license: cc-by-4.0
language:
- en
task_categories:
- automatic-speech-recognition
pretty_name: Hard ASR Sentences
tags:
- speech
- asr
- fine-tuning
- single-speaker
- hard-asr
- english
size_categories:
- n<1K
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train/**
  - split: test
    path: data/test/**
---

# Hard ASR Sentences

English sentences speech recognition finds hard, read by one speaker: Harvard sentences (IEEE 1969, public domain), tongue twisters, homographs, garden-path sentences, rare words, dense numbers and units, unusual proper nouns. `prompt_kind` names the kind and `prompt_source` the origin of each sentence.

**Website: [hebrish.app](https://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`](https://huggingface.co/datasets/danielrosehill/Read-Aloud-Voice-Sentences)**, the full, undifferentiated
dataset. It contains every phone recording there whose `categories` include `hard-asr` (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. **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.

## 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/Hard-ASR-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](https://huggingface.co/datasets/danielrosehill/Read-Aloud-Voice-Sentences#licence-and-attribution).