Awesome
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