A Convolutional Transformer for Keyword Spotting

Last update: Jan 27, 2022

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Overview

☢️ Audiomer ☢️

Audiomer: A Convolutional Transformer for Keyword Spotting

[ `arXiv` ]	[ `Previous SOTA` ]	[ `Model Architecture` ]

Results on SpeechCommands

Model Architecture

Performer Conv-Attention

Usage

To reproduce the results in the paper, follow the instructions:

To download the Speech Commands v2 dataset, run: python3 datamodules/SpeechCommands12.py
To train Audiomer-S and Audiomer-L on all three datasets thrice, run: python3 run_expts.py
To evaluate a model on a dataset, run: python3 evaluate.py --checkpoint_path /path/to/checkpoint.ckpt --model <model type> --dataset <name of dataset>.
For example: python3 evaluate.py --checkpoint_path ./epoch=300.ckpt --model S --dataset SC20

System requirements

NVIDIA GPU with CUDA
Python 3.6 or higher.
pytorch_lightning
torchaudio
performer_pytorch

Owner

GitHub Repository

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A Convolutional Transformer for Keyword Spotting

Related tags

Overview

☢️ Audiomer ☢️

Results on SpeechCommands

Model Architecture

Performer Conv-Attention

Usage

System requirements

Owner

Temporally Coherent GAN SIGGRAPH project.

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