wuw_testset1 / README.md
Yougen's picture
Add files using upload-large-folder tool
3cacbbe verified
---
license: other
task_categories:
- automatic-speech-recognition
pretty_name: WUW Dataset
configs:
- config_name: default
data_files:
- split: train
path: "data/train/audio/*.tar"
tags:
- audio
- speech
- wake-up-word
- wuw
- webdataset
---
# Yougen/wuw_testset1
Wake-Up-Word (WUW) speech dataset, packed as **WebDataset tar shards**.
The input is a Kaldi-style data directory
(`wav.scp`, `text`, `utt2spk`, `utt2dur`, `segments`), where multiple
utterances share a long recording via the `segments` file.
To avoid duplicating audio, **each tar sample corresponds to one full
recording**. The utterance-level metadata (`id / start / end / text /
spk / duration`) is stored in a JSON list inside that sample.
Downstream consumers slice the decoded waveform by `[start*sr : end*sr]`
themselves.
## Layout
```
data/
<split>/
metadata.csv # flattened utterance-level table
audio/
<split>-000.tar
<split>-001.tar
...
```
Shard counts:
- `train`: 3746 tar shard(s)
Inside each tar, every sample is a pair sharing a unique key:
```
<key>.wav # raw recording bytes (original format preserved)
<key>.json # {"rec_id":..., "rel_path":..., "wav_format":"wav",
# "segments": [
# {"id":..., "start":..., "end":...,
# "text":..., "spk":..., "duration":...},
# ...
# ]}
```
`metadata.csv` columns (one row per utterance):
`key, shard, rec_id, rel_path, wav_format, id, start, end, duration, text, spk`
## Loading
```python
from datasets import load_dataset
ds = load_dataset("Yougen/wuw_testset1")
ex = ds["train"][0]
wav_array = ex["wav"]["array"]
sr = ex["wav"]["sampling_rate"]
for seg in ex["json"]["segments"]:
s = int(seg["start"] * sr)
e = int(seg["end"] * sr)
print(seg["id"], seg["text"], wav_array[s:e].shape)
```
Streaming:
```python
ds = load_dataset("Yougen/wuw_testset1", streaming=True)
for example in ds["train"]:
print(example["__key__"], len(example["json"]["segments"]))
break
```