quo-labs/sam-wake-word

--- 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