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Re-creation of model from Real-Time Guitar Amplifier Emulation with Deep Learning

See my blog post for a more in depth description along with song demos.


data/in.wav - Concatenation of a few samples from the IDMT-SMT-Guitar dataset
data/ts9_out.wav - Recorded output of in.wav after being passed through an Ibanez TS9 Tube Screamer (all knobs at 12 o'clock).
models/pedalnet.ckpt - Pretrained model weights


Run effect on .wav file: Must be single channel, 44.1 kHz

# must be same data used to train
python data/in.wav data/out_ts9.wav 

# specify input file and desired output file
python my_input_guitar.wav my_output.wav 

# if you trained you own model you can pass --model flag
# with path to .ckpt


python data/in.wav data/out_ts9.wav # or use your own!
python --gpus "0,1"  # for multiple gpus
python -h  # help (see for other hyperparameters)


python # test pretrained model
python --model lightning_logs/version_{X}/epoch={EPOCH}.ckpt  # test trained model

Creates files y_test.wav, y_pred.wav, and x_test.wav, for the ground truth output, predicted output, and input signal respectively.