license: apache-2.0
tags:
- Embeddings
- ACAV100M
- AE29H_float32
- nanowakeword
- noice
AE29H_float32
Audio Embeddings ~29 hours dataset contains precomputed audio embeddings designed for Nanowakeword framework. The embeddings are intended to be used as general-purpose negative training data, meaning the audio does not contain the target wake word or phrase.
Unlike raw audio datasets, the files in this dataset contain low-dimensional audio embeddings extracted from audio clips using a pre-trained speech embedding model. These embeddings can be directly used as input features when training wake-word detection models with NanoWakeWord.
The goal of this dataset is to provide diverse background audio representations (speech, environmental noise, music, etc.) that help wake-word models learn to avoid false activations.
Dataset Source
The embeddings were generated from a subset of the ACAV100M dataset.
ACAV100M is a very large automatically curated audio-visual dataset created from millions of internet videos and designed for large-scale audio-visual learning. It contains diverse real-world audio such as speech, environmental sounds, music, and background noise.
For this dataset:
- A 20K subset (~2 days of audio) from ACAV100M.
- Audio clips were processed and converted into embeddings suitable for wake-word training.
Dataset Statistics
Shape:
(21115, 16, 96)Total samples: 21,115
Feature dimensions:
- Temporal steps: 16
- Embedding size: 96
Each sample represents approximately 1.28 seconds of audio, where each temporal step corresponds to ~80 ms.
Data Type
dtype: float32
Value range
min: -77.23914
max: 95.59355
Intended Use
This dataset is intended for:
- Training NanoWakeWord wake-word detection models
- Providing negative training examples
- Improving false-positive robustness
- Training models that operate directly on audio embeddings