A Convolutional Transformer for Keyword Spotting

Related tags

Deep LearningAudiomer-PyTorch
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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