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A Vocoder Based Method For Singing Voice Extraction

Pritish Chandna, Merlijn Blaauw, Jordi Bonada, Emilia Gómez

Music Technology Group, Universitat Pompeu Fabra, Barcelona

This repository contains the source code for the paper with the same title.

Installation

To install, clone the repository and use
pip install requirements.txt 
to install the packages required.

The main code is in the train_tf.py file. To use the file, you will have to download the model weights and place it in the log_dir_m1 directory, defined in config.py. Wave files to be tested should be placed in the wav_dir, as defined in config.py. You will also require TensorFlow to be installed on the machine.

Data pre-processing

Once the iKala files have been put in the wav_dir, you can run

python prep_data_ikala.py
to carry out the data pre-processing step.

Training and inference

Once setup, you can run the command

python train_tf.py -t
to train or
python train_tf.py -s <filename> -p (optional, for plots)
to synthesize the output.The output will be saved in the val_dir specified in the config.py file. Note that plots are only supported for iKala songs as the ground truth is available for these songs.

Evaluation

Once the file has been synthesized, you can add examples to be evaluated to the sep_eval folder. Then to evaluate, please run

python sep_eval.py
to run the evaluation script. The results will be save in csv format in the file eval.csv.

We are currently working on future applications for the methodology and the rest of the files in the repository are for this purpose, please ignore. We will further update the repository in the coming months.

Acknowledgments

The TITANX used for this research was donated by the NVIDIA Corporation. This work is partially supported by the Towards Richer Online Music Public-domain Archives (TROMPA) project.

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