| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| - he |
| task_categories: |
| - automatic-speech-recognition |
| pretty_name: Model Checks |
| tags: |
| - speech |
| - asr |
| - fine-tuning |
| - single-speaker |
| - asr-evaluation |
| - human-feedback |
| - error-analysis |
| - english |
| - code-switching |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-*.parquet |
| --- |
| # Model Checks |
|
|
| Takes **checked by a speech recognition model while they were recorded**, with the model's transcript, the speaker's verdict on it and, once judged, a second, machine verdict. Three kinds of row, all from the capture app: |
|
|
| - **Read-sentence checks** (`mode` `bank`): the speaker read a sentence (from the sentence banks, or a queue of new sentences written from current web topics, `prompt_source` `queue:<site>`), the newest fine-tune transcribed it, and the speaker marked it `pass` or `fail`. `text` is the sentence as written (ground truth). |
| - **Free-speech corrections** (`mode` `correction`, category `correction`): the speaker said anything, the model got something wrong, and `text` is the speaker's corrected transcript. |
| - **Free-speech passes** (`mode` `correction`, category `correction_accepted`, `check_verdict` `pass`): the speaker accepted the model's transcript, so `text` equals `model_hypothesis`. **Not training data**: an accepted transcript may still contain a mistake that is already being trained on. These rows are in no other subset. |
|
|
| Columns beyond the main dataset's: `model_hypothesis` (what the model wrote), `asr_model` (the model and its Hugging Face revision, e.g. `finetune:Hebrish/Sabich-Rolling@<sha7>`), `check_verdict` (`pass` | `fail`; null on older corrections, which were all errors), `check_errors` (the words of `text` the model got wrong), `check_error_types` (the speaker's failure types: `wrong-word`, `hebrew-to-english`, `hebrew-spelling`, `name-brand`, `number-format`, `dropped-word`, `added-word`, `split-merge`), and the machine judgement: `ai_verdict` (`pass` | `fail`), `ai_error_types` (same keys), `ai_notes` (one line), `ai_model` and `ai_judged_at` (ISO time), all empty until a judging pass fills them. `source_url` and `topic` give the web page a queued sentence was written from. |
|
|
| **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 with a `check_verdict` (checked while recording) or recorded in Correct mode (`mode` `correction`); each row adds the empty `ai_*` judgement columns. 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-*.parquet one row per take: `audio` (the FLAC or WAV file, embedded) and every column |
| meta/train.jsonl index: the same rows without audio, plus `shard`, `audio_path` and the audio's byte range |
| meta/schema.json column types |
| data/test-*.parquet, meta/test.jsonl held-out set, never trained on |
| ``` |
|
|
| Audio is 48 kHz mono 24-bit FLAC for rows from 2026-10-22 and 16 kHz mono 16-bit WAV before. |
|
|
| The `test` split is **term-disjoint**: a held-out term never appears in `train`. |
| `load_dataset("Hebrish/Model-Checks")` 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). |
|
|
