htqin/BiFSMNv2

Pytorch implementation of BiFSMNv2, TNNLS 2023

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This project helps developers integrate efficient keyword spotting capabilities into edge devices like smart home speakers or wearables. It takes audio inputs and identifies specific keywords, producing highly accurate results comparable to larger models, but with significantly reduced computational demands and storage requirements. This is ideal for embedded systems engineers and machine learning engineers working on resource-constrained hardware.

No commits in the last 6 months.

Use this if you need to deploy highly accurate keyword spotting models on devices with limited memory and processing power, such as microcontrollers or small IoT devices.

Not ideal if you are working with powerful servers or cloud environments where computational resources and storage are not a constraint.

keyword-spotting edge-ai embedded-systems voice-interfaces on-device-machine-learning
No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 7 / 25
Maturity 8 / 25
Community 16 / 25

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35

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7

Language

Python

License

Last pushed

Feb 10, 2023

Commits (30d)

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