Implementation of the paper https://arxiv.org/pdf/1802.04208.pdf
- Max Holmberg
- Joel Lidin
SC09 (0-9 digits) which was trained for ~320k update steps.
Kitten meows which was trained for ~100k update steps.
Dependencies
tensorflow=2.1.0
numpy=1.18.4
matplotlib=3.2.1
scipy=1.4.1
librosa=0.7.2
tqdm
In order to generate the dataset files required for training run
python dataset.py -create_piano_wav -path "dataset/piano/train" -output_path "piano.wav"
python dataset.py -create_piano_npy -path "piano.wav" -output_path "piano.npy"
python dataset.py -create_sc09_npy -path "dataset/sc09-spoken-numbers/sc09/train" -output_path "sc09.npy"
To train the model (on for example the piano dataset)
python run.py -train -dataset piano.npy -epochs 100
To continue the training and specify which logging step it should start from in tensorboard (logs to tensorboard every 10th update step, can be changed in hyperparams)
python run.py -train -continue -initial_log_step 5 -dataset piano.npy -epochs 100
To generate samples with weights, run
python run.py -generate -weights piano -n 1000 -output_path "..."
| Real (Kittens) | WaveGAN (Kittens) |
|---|---|
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| Real (Piano) | WaveGAN (Piano) |
|---|---|
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| Real (sc09) | WaveGAN (sc09) |
|---|---|
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