| --- |
| license: cc-by-4.0 |
| task_categories: |
| - automatic-speech-recognition |
| - question-answering |
| - feature-extraction |
| language: |
| - en |
| tags: |
| - speechgr |
| - slue-sqa5 |
| - hubert |
| - discrete-units |
| - kmeans |
| --- |
| |
| # SLUE-SQA-5 HuBERT Layer-22 K=500 Discrete Units |
|
|
| Packed discrete-unit files for SpeechGR experiments on SLUE-SQA-5. |
|
|
| The units were produced with HuBERT layer 22 and a K=500 k-means model, then deduplicated with consecutive counts retained. The packed format avoids one `.code` and `.cnt` file per utterance. |
|
|
| ## Files |
|
|
| - `documents.npz`: packed document/passage units |
| - `train.npz`: packed train question units |
| - `validation.npz`: packed validation question units |
| - `test.npz`: packed test question units |
| - `verified_test.npz`: packed verified test question units |
|
|
| Each archive contains: |
|
|
| - `ids`: record ids |
| - `codes`: all unit sequences concatenated |
| - `code_offsets`, `code_lengths`: offsets into `codes` |
| - `counts`: all consecutive-run counts concatenated |
| - `count_offsets`, `count_lengths`: offsets into `counts` |
| - `text`: optional transcript/passage metadata when available |
| - `doc_ids`: question-to-document ids for question splits when available |
|
|
| ## Loading |
|
|
| Use `unit_store.PackedUnitStore` from the SpeechGR repository: |
|
|
| ```python |
| from unit_store import PackedUnitStore |
| |
| store = PackedUnitStore("train.npz") |
| record_id = store.ids[0] |
| codes = store.get_code(record_id) |
| counts = store.get_counts(record_id) |
| ``` |
|
|
