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audiomer-pytorch's Introduction

Audiomer

Audiomer: A Convolutional Transformer for Keyword Spotting
Accepted at AAAI 2022 DSTC Workshop

PWC

[ arXiv ] [ Previous SOTA ] [ Model Architecture ]

Pretrained Models

  • Links: Google Drive

  • Note: The pretrained models only work with commit 6270ca27de47fbfd0379c172bbc74e6a61f72176, after which there has been breaking changes.

Usage

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

  • To download the Speech Commands v2 dataset, run: python3 download_speechcommands.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

Results

Performer Conv-Attention

TLDR: We augment 1D ResNets With Performer Attention over Raw Audio waveform.


System requirements

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

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Contributors

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