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README.md

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+# vocal-remover
+
+[![Release](https://img.shields.io/github/release/tsurumeso/vocal-remover.svg)](https://github.com/tsurumeso/vocal-remover/releases/latest)
+[![Release](https://img.shields.io/github/downloads/tsurumeso/vocal-remover/total.svg)](https://github.com/tsurumeso/vocal-remover/releases)
+
+This is a deep-learning-based tool to extract instrumental track from your songs.
+
+## Installation
+
+### Getting vocal-remover
+Download the latest version from [here](https://github.com/tsurumeso/vocal-remover/releases).
+
+### Install PyTorch
+**See**: [GET STARTED](https://pytorch.org/get-started/locally/)
+
+### 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 --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