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
🌊 Online machine learning in Python

In a nutshell River is a Python library for online machine learning. It is the result of a merger between creme and scikit-multiflow. River's ambition

OnlineML 4k Jan 02, 2023
Official Code Release for "CLIP-Adapter: Better Vision-Language Models with Feature Adapters"

Official Code Release for "CLIP-Adapter: Better Vision-Language Models with Feature Adapters" Pipeline of CLIP-Adapter CLIP-Adapter is a drop-in modul

peng gao 157 Dec 26, 2022
Highly comparative time-series analysis

〰️ hctsa 〰️ : highly comparative time-series analysis hctsa is a software package for running highly comparative time-series analysis using Matlab (fu

Ben Fulcher 569 Dec 21, 2022
Save-restricted-v-3 - Save restricted content Bot For telegram

Save restricted content Bot Contact: Telegram A stable telegram bot to get restr

DEVANSH 11 Dec 21, 2022
[Preprint] ConvMLP: Hierarchical Convolutional MLPs for Vision, 2021

Convolutional MLP ConvMLP: Hierarchical Convolutional MLPs for Vision Preprint link: ConvMLP: Hierarchical Convolutional MLPs for Vision By Jiachen Li

SHI Lab 143 Jan 03, 2023
YOLOv7 - Framework Beyond Detection

🔥🔥🔥🔥 YOLO with Transformers and Instance Segmentation, with TensorRT acceleration! 🔥🔥🔥

JinTian 3k Jan 01, 2023
Code and project page for ICCV 2021 paper "DisUnknown: Distilling Unknown Factors for Disentanglement Learning"

DisUnknown: Distilling Unknown Factors for Disentanglement Learning See introduction on our project page Requirements PyTorch = 1.8.0 torch.linalg.ei

Sitao Xiang 24 May 16, 2022
Running Google MoveNet Multipose Tracking models on OpenVINO.

MoveNet MultiPose Tracking on OpenVINO

60 Nov 17, 2022
A Pythonic library for Nvidia Codec.

A Pythonic library for Nvidia Codec. The project is still in active development; expect breaking changes. Why another Python library for Nvidia Codec?

Zesen Qian 12 Dec 27, 2022
Pneumonia Detection using machine learning - with PyTorch

Pneumonia Detection Pneumonia Detection using machine learning. Training was done in colab: DEMO: Result (Confusion Matrix): Data I uploaded my datase

Wilhelm Berghammer 12 Jul 07, 2022
CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP

CLIP-GEN [简体中文][English] 本项目在萤火二号集群上用 PyTorch 实现了论文 《CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP》。 CLIP-GEN 是一个 Language-F

75 Dec 29, 2022
Distilled coarse part of LoFTR adapted for compatibility with TensorRT and embedded divices

Coarse LoFTR TRT Google Colab demo notebook This project provides a deep learning model for the Local Feature Matching for two images that can be used

Kirill 46 Dec 24, 2022
Compositional Sketch Search

Compositional Sketch Search Official repository for ICIP 2021 Paper: Compositional Sketch Search Requirements Install and activate conda environment c

Alexander Black 8 Sep 06, 2021
Memory Defense: More Robust Classificationvia a Memory-Masking Autoencoder

Memory Defense: More Robust Classificationvia a Memory-Masking Autoencoder Authors: - Eashan Adhikarla - Dan Luo - Dr. Brian D. Davison Abstract Many

Eashan Adhikarla 4 Dec 25, 2022
This repository contains the code used to quantitatively evaluate counterfactual examples in the associated paper.

On Quantitative Evaluations of Counterfactuals Install To install required packages with conda, run the following command: conda env create -f requi

Frederik Hvilshøj 1 Jan 16, 2022
A Closer Look at Structured Pruning for Neural Network Compression

A Closer Look at Structured Pruning for Neural Network Compression Code used to reproduce experiments in https://arxiv.org/abs/1810.04622. To prune, w

Bayesian and Neural Systems Group 140 Dec 05, 2022
A smart Chat bot that can help to know about corona virus and Make prediction of corona using X-ray.

TRINIT_Hum_kuchh_nahi_karenge_ML01 Document Link https://github.com/Jatin-Goyal-552/TRINIT_Hum_kuchh_nahi_karenge_ML01/blob/main/hum_kuchh_nahi_kareng

JatinGoyal 1 Feb 03, 2022
This is the code for "HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields".

HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields This is the code for "HyperNeRF: A Higher-Dimensional

Google 702 Jan 02, 2023
CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection

CIFS This repository provides codes for CIFS (ICML 2021). CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Sel

Hanshu YAN 19 Nov 12, 2022
Multi-Scale Geometric Consistency Guided Multi-View Stereo

ACMM [News] The code for ACMH is released!!! [News] The code for ACMP is released!!! About ACMM is a multi-scale geometric consistency guided multi-vi

Qingshan Xu 118 Jan 04, 2023