vocal-remover

This is a deep-learning-based tool to extract instrumental track from your songs.

Installation

Getting vocal-remover

Download the latest version from here.

Install PyTorch

Install the other packages

cd vocal-remover
pip install -r requirements.txt

Usage

The following command separates the input into instrumental and vocal tracks. They are saved as *_Instruments.wav and *_Vocals.wav.

Run on CPU

python inference.py --input path/to/an/audio/file

Run on GPU

python inference.py --input path/to/an/audio/file --gpu 0

Advanced options

--tta option performs Test-Time-Augmentation to improve the separation quality.

python inference.py --input path/to/an/audio/file --tta --gpu 0

Train your own model

Place your dataset

path/to/dataset/
  +- instruments/
  |    +- 01_foo_inst.wav
  |    +- 02_bar_inst.mp3
  |    +- ...
  +- mixtures/
       +- 01_foo_mix.wav
       +- 02_bar_mix.mp3
       +- ...

Train a model

python train.py --dataset path/to/dataset --mixup_rate 0.5 --gpu 0

References

[1] Jansson et al., "Singing Voice Separation with Deep U-Net Convolutional Networks", https://ejhumphrey.com/assets/pdf/jansson2017singing.pdf
[2] Takahashi et al., "Multi-scale Multi-band DenseNets for Audio Source Separation", https://arxiv.org/pdf/1706.09588.pdf
[3] Takahashi et al., "MMDENSELSTM: AN EFFICIENT COMBINATION OF CONVOLUTIONAL AND RECURRENT NEURAL NETWORKS FOR AUDIO SOURCE SEPARATION", https://arxiv.org/pdf/1805.02410.pdf
[4] Choi et al., "PHASE-AWARE SPEECH ENHANCEMENT WITH DEEP COMPLEX U-NET", https://openreview.net/pdf?id=SkeRTsAcYm
[5] Jansson et al., "Learned complex masks for multi-instrument source separation", https://arxiv.org/pdf/2103.12864.pdf
[6] Liutkus et al., "The 2016 Signal Separation Evaluation Campaign", Latent Variable Analysis and Signal Separation - 12th International Conference