Code for unmixing audio signals in four different stems "drums, bass, vocals, others". The code is adapted from "Jukebox: A Generative Model for Music"

Related tags

Deep Learningunmix
Overview

Status: Archive (code is provided as-is, no updates expected)

Disclaimer

This code is a based on "Jukebox: A Generative Model for Music" Paper

We adjusted it for our application: Demixing an audio signal into four different stems: drums, basss, vocals, other.

Unmix

Install

Install the conda package manager from https://docs.conda.io/en/latest/miniconda.html

# Required: Sampling
conda create --name unmix python=3.7.5
conda activate unmix
conda install mpi4py=3.0.3 # if this fails, try: pip install mpi4py==3.0.3
conda install pytorch=1.4 torchvision=0.5 cudatoolkit=10.0 -c pytorch
git clone https://github.com/wzaiealmri/unmix.git
cd unmix
pip install -r requirements.txt
pip install -e .

# Required: Training
conda install av=7.0.01 -c conda-forge
pip install ./tensorboardX

# Optional: Apex for faster training with fused_adam
conda install pytorch=1.1 torchvision=0.3 cudatoolkit=10.0 -c pytorch
pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./apex

Training

Stage 1: VQVAE

To train the vqvae, run

mpiexec -n {ngpus} python unmix/train.py --hps=vqvae --name=vqvae_drums_b4 --sr=44100 --sample_length=393216 --bs=4 --audio_files_dir="Put the path to the specific stem audio folder" --labels=False --train --aug_shift --aug_blend

Here, {audio_files_dir} is the directory in which you can put the audio files for your stem , and {ngpus} is number of GPU's you want to use to train. The above trains a one-level VQ-VAE with downs_t = (3), and strides_t = (2) meaning we downsample the audio by 2**3 = 8 to get the first level of codes.
Checkpoints are stored in the logs folder. You can monitor the training by running Tensorboard

tensorboard --logdir logs

Stage 2: Encoder

Train encoder

Once the VQ-VAE is trained, we can restore it from its saved checkpoint and train encoder on the learnt codes. To train the encoder, we can run

mpiexec -n {ngpus} python unmix_encoder/train.py --hps=vqvae --name=encoder_drums__b4 --sr=44100 --sample_length=393216 --bs=4 --audio_files_dir="path to the mix dataset" --labels=False --train --aug_shift --aug_blend --encoder=True --channel=_1 --restore_vqvae="path to the specific checkpoint of the vq-vae"

License (Jukebox OpenAI)

Noncommercial Use License

It covers both released code and weights.

Owner
Wadhah Zai El Amri
Wadhah Zai El Amri
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