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README 2.md

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+---
+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
+---
+
+# 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("SAM-Companion/sam-wake-word")
+```
+
+## License
+
+MIT