This is a deep-learning-based tool for extracting the instrumental track from your songs.
Download the latest version from here.
See: GET STARTED
cd vocal-remover
pip install -r requirements.txt
The following command separates the input into instrumental and vocal tracks. They are saved as *_Instruments.wav and *_Vocals.wav.
python inference.py --input path/to/an/audio/file
python inference.py --input path/to/an/audio/file --gpu 0
The --tta option performs Test-Time Augmentation to improve separation quality.
python inference.py --input path/to/an/audio/file --tta --gpu 0
The --postprocess option masks the instrumental track based on the vocal volume to improve separation quality.
Warning
This is an experimental feature. If you encounter any problems with this option, please disable it.
python inference.py --input path/to/an/audio/file --postprocess --gpu 0
path/to/dataset/
+- instruments/
| +- 01_foo_inst.wav
| +- 02_bar_inst.mp3
| +- ...
+- mixtures/
+- 01_foo_mix.wav
+- 02_bar_mix.mp3
+- ...
python train.py --dataset path/to/dataset --mixup_rate 0.5 --reduction_rate 0.5 --gpu 0
- [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
