Unofficial PyTorch implementation of Keyword Transformer: A Self-Attention Model for Keyword Spotting.

pip install -r requirements.txt

To download the Google Speech Commands V2 dataset, you may run the provided bash script as below. This would download and extract the dataset to the "destination path" provided.

sh ./download_gspeech_v2.sh <destination_path>

The Speech Commands V2 dataset provides a "validation_list.txt" file and a "testing_list.txt" file. Run:

python make_data_list.py -v <path/to/validation_list.txt> -t <path/to/testing_list.txt> -d <path/to/dataset/root> -o <output dir>

This will create the files training_list.txt, validation_list.txt, testing_list.txt and label_map.json at the specified output dir.

Running train.py is fairly straightforward. Only a path to a config file is required. Inside the config file, you'll need to add the paths to the .txt files and the label_map.json file created above.

python train.py --conf path/to/config.yaml

Refer to the example config to see how the config file looks like, and see the config explanation for a complete rundown of the various config parameters.

You can use the pre-trained model (or a model you trained) for inference, using the two scripts:

• inference.py: For short ~1s clips, like the audios in the Speech Commands dataset
• window_inference.py: For running inference on longer audio clips, where multiple keywords may be present. Runs inference on the audio in a sliding window manner.

python inference.py --conf sample_configs/base_config.yaml \
                    --ckpt <path to pretrained_model.ckpt> \
                    --inp <path to audio.wav / path to audio folder> \
                    --out <output directory> \
                    --lmap label_map.json \
                    --device cpu \
                    --batch_size 8   # should be possible to use much larger batches if necessary, like 128, 256, 512 etc.

!python window_inference.py --conf sample_configs/base_config.yaml \
                    --ckpt <path to pretrained_model.ckpt> \
                    --inp <path to audio.wav / path to audio folder> \
                    --out <output directory> \
                    --lmap label_map.json \
                    --device cpu \
                    --wlen 1 \
                    --stride 0.5 \
                    --thresh 0.85 \
                    --mode multi

For detailed usage example, check the colab tutorial.

• Colab Tutorial: [Using pretrained model | Inference scripts | Training]

You can optionally log your training runs with wandb. You may provide a path to a file containing your API key, or simply provide it manually from the login prompt when your start your training.

*The example config file provided contains the exact settings used to train the KWT-1 checkpoint, and should be reproducible.