Keyword Spotting
Last updated
Last updated
Task: Audio Classification
License: Apache 2.0
Have you ever wanted to make your own "Ok, Google" or "Alexa" keyword spotting model? The helloworld class has been collected by Edge Impulse teams, the added noise samples come from the Microsoft Scalable Noisy Speech Dataset and the unknown samples are based on a subset of data in the Google Speech Commands Dataset.
This dataset can be used to build an Edge AI project detecting the "Hello World" keyword phrase.
You can also follow our tutorial to guide you through building your keyword spotting model, from data collection to deployment on embedded devices.
Feature extraction: Audio (MFCC), Audio (MFE), Spectrogram, Raw Data
Learning block: Classification, Transfer Learning (Keyword Spotting)
Not sure what to choose? Try out this dataset with the EON Tuner.
Total Data Items: 2062
Total Data Length: 0h 34m 22s
Axis Summary: audio
Labeling Method: single label
Train/Test Split: 79.97% / 20.03%
Training Set
Testing Set
Total Data Items
1649
413
Labels
helloworld, noise, unknown
helloworld, noise, unknown
Total Data Length
0h 27m 29s
0h 6m 53s
Clone the public project.
To clone and use this project, visit the Edge Impulse Studio link, click on the Clone button on the top-right corner and follow the cloning instructions.
Download
HuggingFace - Soon
Kaggle - Soon
Import this dataset to your Edge Impulse project
This project uses the Edge Impulse Exporter Format (info.labels). See this documentation page for more info.
Edge Impulse also supports different data sample formats and dataset annotation formats that you can import into your project to build your edge AI models:
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If you use this dataset in your research paper, please cite it using the following BibTeX:
