On-device wake word detection powered by deep learning
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Updated
May 2, 2024 - Python
On-device wake word detection powered by deep learning
A lightweight, simple-to-use, RNN wake word listener
ICASSP 2023-2024 Papers: A complete collection of influential and exciting research papers from the ICASSP 2023-24 conferences. Explore the latest advancements in acoustics, speech and signal processing. Code included. Star the repository to support the advancement of audio and signal processing!
Code for Temporal Convolution for Real-time Keyword Spotting on Mobile Devices
Spokestack is a library that allows a user to easily incorporate a voice interface into any Python application with a focus on embedded systems.
wake word engine benchmark framework
Wav2Keyword is keyword spotting(KWS) based on Wav2Vec 2.0. This model shows state-of-the-art in Speech commands dataset V1 and V2.
Learning Efficient Representations for Keyword Spotting with Triplet Loss
In this repository, we explore using a hybrid system consisting of a Convolutional Neural Network and a Support Vector Machine for Keyword Spotting task.
Classify audio with neural nets on embedded systems like the Raspberry Pi
Official code for Long Expressive Memory (ICLR 2022, Spotlight)
Train a 4-layer Convolutional Neural Network to detect trigger word
BC-ResNet for Keyword Spotting
Tensorflow implementation of "Small-Footprint Keyword Spotting with Multi-Scale Temporal Convolution"(INTERSPEECH 2020)
Tensorflow2 implementation of Data-driven Harmonic Filters for Audio Representation Learning
Speech command recognition with capsule network & various NNs / KWS on Google Speech Command Dataset.
Pytorch implementation of BiFSMNv2, TNNLS 2023
EfficientNet-Absolute Zero for Continuous Speech Keyword Spotting
An official implementation for SSAMBA: Self-Supervised Audio Mamba
Attention-based model for keywords spotting
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