README.md

---
license: other
task_categories:
  - audio-classification
  - automatic-speech-recognition
pretty_name: KWS Dataset
configs:
  - config_name: default
    data_files:
      - split: train
        path: "data/train/audio/*.tar"
tags:
  - audio
  - speech
  - keyword-spotting
  - kws
  - webdataset
---

# ygyuan/wuw_accent

Keyword-Spotting (KWS) speech dataset, packed as **WebDataset tar shards**.
The input is a Kaldi-style data directory
(`wav.scp`, `text`, `utt2spk`, `utt2dur`, `segments`), where each
utterance is packed as a single tar sample.

## Layout

```
data/
  <split>/
    metadata.csv
    audio/
      <split>-000.tar
      <split>-001.tar
      ...
```

Shard counts:

- `train`: 509 tar shard(s)

Inside each tar, every sample is a pair sharing a unique key:
```
<key>.wav       # raw audio bytes (original format preserved)
<key>.json      # {"id":..., "rel_path":..., "wav_format":"wav",
                #   "duration":..., "text":"<keyword>", "spk":...,
                #   "rec_id":..., "start":..., "end":...}
```

`metadata.csv` columns:
`key, shard, id, rel_path, wav_format, duration, text, spk, rec_id, start, end`

## Loading

```python
from datasets import load_dataset

ds = load_dataset("ygyuan/wuw_accent")
print(ds)
print(ds["train"][0])
# sample keys: 'wav' (decoded audio), 'json' (metadata), '__key__', '__url__'
```

For streaming (no full download needed):

```python
ds = load_dataset("ygyuan/wuw_accent", streaming=True)
for example in ds["train"]:
    print(example["__key__"], example["json"]["text"])
    break
```