Datasets:
Tasks:
Audio Classification
Modalities:
Audio
Formats:
soundfolder
Languages:
English
Size:
1K - 10K
License:
File size: 1,769 Bytes
85cd112 068c353 85cd112 068c353 85cd112 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | ---
language:
- en
task_categories:
- audio-classification
tags:
- wake-word
- keyword-spotting
- voice-assistant
pretty_name: SAM Wake Word Dataset
size_categories:
- 1K<n<10K
license: mit
configs:
- config_name: default
data_files:
- split: train
path: "**/*.wav"
default: true
---
# SAM Wake Word Dataset
Audio dataset for training a wake-word detection model to recognise the keyword **"Sam"**.
## Dataset Description
Each sample is a short audio clip labelled as either **positive** (contains the wake word) or **negative** (does not).
| Split | Description |
|-------|-------------|
| `positive/` | Clips of the word "Sam" spoken in varied styles, speeds, and intonations |
| `negative/` | Clips of phonetically similar or common words that are **not** "Sam" |
## Generation
All audio is synthesised using **OpenAI `gpt-4o-mini-tts`** with:
- **10 voices**: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer
- **Variable speed**: 0.85× – 1.30×
- **Diverse speaking styles**: whispering, shouting, questioning, commanding, accented, etc.
Positive prompts include: *"Sam", "sam", "SAM", "Sam!", "Hey Sam", "Yo Sam"*
Negative prompts include phonetically close words (*ham, jam, slam, spam, dam, same, samuel, sample*) and common short words (*hello, hey, stop, play, yes, no, …*).
### Audio Format
- **Sample rate**: 16 kHz
- **Channels**: mono
- **Format**: WAV
## Manifest
A `manifest.json` file is included with metadata for each clip:
```json
{
"file": "positive/positive_00042.wav",
"label": "positive",
"text": "Sam",
"voice": "nova",
"speed": 1.12
}
```
## Usage
```python
from datasets import load_dataset
ds = load_dataset("quo-labs/sam-wake-word")
```
## License
MIT
|