tsurumeso / Vocal Remover
Licence: mit
Vocal Remover using Deep Neural Networks
Stars: ✭ 178
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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.
Experimental Warning: 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 --reduction_rate 0.5 --mixup_rate 0.5 --gpu 0
References
- [1] Jansson et al., "Singing Voice Separation with Deep U-Net Convolutional Networks", https://ismir2017.smcnus.org/wp-content/uploads/2017/10/171_Paper.pdf
- [2] Takahashi et al., "Multi-scale Multi-band DenseNets for Audio Source Separation", https://arxiv.org/pdf/1706.09588.pdf
- [3] Liutkus et al., "The 2016 Signal Separation Evaluation Campaign", Latent Variable Analysis and Signal Separation - 12th International Conference
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