| | --- |
| | 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** |
