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
GitHub Repository
some classic model used to segment the medical images like CT、X-ray and so on

github_project This is a project for medical image segmentation. This project includes common medical image segmentation models such as U-net, FCN, De

2 Mar 30, 2022
[WACV 2020] Reducing Footskate in Human Motion Reconstruction with Ground Contact Constraints

Reducing Footskate in Human Motion Reconstruction with Ground Contact Constraints Official implementation for Reducing Footskate in Human Motion Recon

Virginia Tech Vision and Learning Lab 38 Nov 01, 2022
WTTE-RNN a framework for churn and time to event prediction

WTTE-RNN Weibull Time To Event Recurrent Neural Network A less hacky machine-learning framework for churn- and time to event prediction. Forecasting p

Egil Martinsson 727 Dec 28, 2022
Official Pytorch implementation of MixMo framework

MixMo: Mixing Multiple Inputs for Multiple Outputs via Deep Subnetworks Official PyTorch implementation of the MixMo framework | paper | docs Alexandr

79 Nov 07, 2022
LEAP: Learning Articulated Occupancy of People

LEAP: Learning Articulated Occupancy of People Paper | Video | Project Page This is the official implementation of the CVPR 2021 submission LEAP: Lear

Neural Bodies 60 Nov 18, 2022
Source code for our paper "Do Not Trust Prediction Scores for Membership Inference Attacks"

Do Not Trust Prediction Scores for Membership Inference Attacks Abstract: Membership inference attacks (MIAs) aim to determine whether a specific samp

<a href=[email protected]"> 3 Oct 25, 2022
Bayesian Neural Networks in PyTorch

We present the new scheme to compute Monte Carlo estimator in Bayesian VI settings with almost no memory cost in GPU, regardles of the number of sampl

Jurijs Nazarovs 7 May 03, 2022
Code for Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights

Piggyback: https://arxiv.org/abs/1801.06519 Pretrained masks and backbones are available here: https://uofi.box.com/s/c5kixsvtrghu9yj51yb1oe853ltdfz4q

Arun Mallya 165 Nov 22, 2022
This repo holds the code of TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation

TransFuse This repo holds the code of TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation Requirements Pytorch=1.6.0, 1.9.0 (=1.

Rayicer 93 Dec 19, 2022
Source code for paper "Deep Superpixel-based Network for Blind Image Quality Assessment"

DSN-IQA Source code for paper "Deep Superpixel-based Network for Blind Image Quality Assessment" Requirements Python =3.8.0 Pytorch =1.7.1 Usage wit

7 Oct 13, 2022
MARS: Learning Modality-Agnostic Representation for Scalable Cross-media Retrieva

Introduction This is the source code of our TCSVT 2021 paper "MARS: Learning Modality-Agnostic Representation for Scalable Cross-media Retrieval". Ple

7 Aug 24, 2022
a delightful machine learning tool that allows you to train, test and use models without writing code

igel A delightful machine learning tool that allows you to train/fit, test and use models without writing code Note I'm also working on a GUI desktop

Nidhal Baccouri 3k Jan 05, 2023
Rocket-recycling with Reinforcement Learning

Rocket-recycling with Reinforcement Learning Developed by: Zhengxia Zou I have long been fascinated by the recovery process of SpaceX rockets. In this

Zhengxia Zou 202 Jan 03, 2023
Sound-guided Semantic Image Manipulation - Official Pytorch Code (CVPR 2022)

🔉 Sound-guided Semantic Image Manipulation (CVPR2022) Official Pytorch Implementation Sound-guided Semantic Image Manipulation IEEE/CVF Conference on

CVLAB 58 Dec 28, 2022
UNet model with VGG11 encoder pre-trained on Kaggle Carvana dataset

TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation By Vladimir Iglovikov and Alexey Shvets Introduction TernausNet is

Vladimir Iglovikov 1k Dec 28, 2022
Official implementation of the paper "Lightweight Deep CNN for Natural Image Matting via Similarity Preserving Knowledge Distillation"

Lightweight-Deep-CNN-for-Natural-Image-Matting-via-Similarity-Preserving-Knowledge-Distillation Introduction Accepted at IEEE Signal Processing Letter

DongGeun-Yoon 19 Jun 07, 2022
dualPC.R contains the R code for the main functions.

dualPC.R contains the R code for the main functions. dualPC_sim.R contains an example run with the different PC versions; it calls dualPC_algs.R whic

3 May 30, 2022
v objective diffusion inference code for PyTorch.

v-diffusion-pytorch v objective diffusion inference code for PyTorch, by Katherine Crowson (@RiversHaveWings) and Chainbreakers AI (@jd_pressman). The

Katherine Crowson 635 Dec 30, 2022
Robust Self-augmentation for NER with Meta-reweighting

Robust Self-augmentation for NER with Meta-reweighting

Lam chi 17 Nov 22, 2022
Official implementation of "SinIR: Efficient General Image Manipulation with Single Image Reconstruction" (ICML 2021)

SinIR (Official Implementation) Requirements To install requirements: pip install -r requirements.txt We used Python 3.7.4 and f-strings which are in

47 Oct 11, 2022