| --- |
| 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). |
|
|
