File size: 2,187 Bytes
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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](https://github.com/arcosoph/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](https://www.kaggle.com/models/google/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](https://acav100m.github.io/)** 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** |