---
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)
```