vocal remover

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

See: GET STARTED

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

--postprocess option masks instrumental part based on the vocals volume to improve the separation quality.

Warning

This is an experimental feature. If you get any problems with this option, please disable it.

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