fromrayimporttunedefobjective(step, alpha, beta):
return (0.1+alpha*step/100)**(-1) +beta*0.1deftraining_function(config):
# Hyperparametersalpha, beta=config["alpha"], config["beta"]
forstepinrange(10):
# Iterative training function - can be any arbitrary training procedure.intermediate_score=objective(step, alpha, beta)
# Feed the score back back to Tune.tune.report(mean_loss=intermediate_score)
analysis=tune.run(
training_function,
config={
"alpha": tune.grid_search([0.001, 0.01, 0.1]),
"beta": tune.choice([1, 2, 3])
})
print("Best config: ", analysis.get_best_config(metric="mean_loss", mode="min"))
# Get a dataframe for analyzing trial results.df=analysis.results_df
# install dependencies using conda
conda install -c conda-forge ffmpeg libsndfile
# install spleeter with pip
pip install spleeter
# download an example audio file (if you don't have wget, use another tool for downloading)
wget https://github.com/deezer/spleeter/raw/master/audio_example.mp3
# separate the example audio into two components
spleeter separate -p spleeter:2stems -o output audio_example.mp3
@article{spleeter2020,
doi = {10.21105/joss.02154},
url = {https://doi.org/10.21105/joss.02154},
year = {2020},
publisher = {The Open Journal},
volume = {5},
number = {50},
pages = {2154},
author = {Romain Hennequin and Anis Khlif and Felix Voituret and Manuel Moussallam},
title = {Spleeter: a fast and efficient music source separation tool with pre-trained models},
journal = {Journal of Open Source Software},
note = {Deezer Research}
}
