☢️ Audiomer ☢️

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

[ arXiv ]	[ Previous SOTA ]	[ Model Architecture ]

Pretrained Models: Google Drive

NOTE: This is a pre-print release, the code might have bugs.

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

Results

Performer Conv-Attention

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

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System requirements

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