Official implementation of the RAVE model: a Realtime Audio Variational autoEncoder

Overview

rave_logo

RAVE: Realtime Audio Variational autoEncoder

Official implementation of RAVE: A variational autoencoder for fast and high-quality neural audio synthesis (article link) by Antoine Caillon and Philippe Esling.

If you use RAVE as a part of a music performance or installation, be sure to cite either this repository or the article !

Installation

RAVE needs python 3.9. Install the dependencies using

pip install -r requirements.txt

Detailed instructions to setup a training station for this project are available here.

Preprocessing

RAVE comes with two command line utilities, resample and duration. resample allows to pre-process (silence removal, loudness normalization) and augment (compression) an entire directory of audio files (.mp3, .aiff, .opus, .wav, .aac). duration prints out the total duration of a .wav folder.

Training

Both RAVE and the prior model are available in this repo. For most users we recommand to use the cli_helper.py script, since it will generate a set of instructions allowing the training and export of both RAVE and the prior model on a specific dataset.

python cli_helper.py

However, if you want to customize even more your training, you can use the provided train_{rave, prior}.py and export_{rave, prior}.py scripts manually.

Reconstructing audio

Once trained, you can reconstruct an entire folder containing wav files using

python reconstruct.py --ckpt /path/to/checkpoint --wav-folder /path/to/wav/folder

You can also export RAVE to a torchscript file using export_rave.py and use the encode and decode methods on tensors.

Realtime usage

UPDATE

If you want to use the realtime mode, you should update your dependencies !

pip install -r requirements.txt

RAVE and the prior model can be used in realtime on live audio streams, allowing creative interactions with both models.

nn~

RAVE is compatible with the nn~ max/msp and PureData external.

max_msp_screenshot

An audio example of the prior sampling patch is available in the docs/ folder.

RAVE vst

You can also use RAVE as a VST audio plugin using the RAVE vst !

plugin_screenshot

Discussion

If you have questions, want to share your experience with RAVE or share musical pieces done with the model, you can use the Discussion tab !

Owner
ACIDS
Artificial Creative Intelligence and Data Science
ACIDS
RCDNet: A Model-driven Deep Neural Network for Single Image Rain Removal (CVPR2020)

RCDNet: A Model-driven Deep Neural Network for Single Image Rain Removal (CVPR2020) Hong Wang, Qi Xie, Qian Zhao, and Deyu Meng [PDF] [Supplementary M

Hong Wang 6 Sep 27, 2022
Federated_learning codes used for the the paper "Evaluation of Federated Learning Aggregation Algorithms" and "A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparison"

Federated Distance (FedDist) This is the code accompanying the Percom2021 paper "A Federated Learning Aggregation Algorithm for Pervasive Computing: E

GETALP 8 Jan 03, 2023
Repository for benchmarking graph neural networks

Benchmarking Graph Neural Networks Updates Nov 2, 2020 Project based on DGL 0.4.2. See the relevant dependencies defined in the environment yml files

NTU Graph Deep Learning Lab 2k Jan 03, 2023
Biomarker identification for COVID-19 Severity in BALF cells Single-cell RNA-seq data

scBALF Covid-19 dataset Analysis Here is the Github page that has the codes for the bioinformatics pipeline described in the paper COVID-Datathon: Bio

Nami Niyakan 2 May 21, 2022
Single Image Random Dot Stereogram for Tensorflow

TensorFlow-SIRDS Single Image Random Dot Stereogram for Tensorflow SIRDS is a means to present 3D data in a 2D image. It allows for scientific data di

Greg Peatfield 5 Aug 10, 2022
Code for the ECCV2020 paper "A Differentiable Recurrent Surface for Asynchronous Event-Based Data"

A Differentiable Recurrent Surface for Asynchronous Event-Based Data Code for the ECCV2020 paper "A Differentiable Recurrent Surface for Asynchronous

Marco Cannici 21 Oct 05, 2022
Pytorch implementations of the paper Value Functions Factorization with Latent State Information Sharing in Decentralized Multi-Agent Policy Gradients

LSF-SAC Pytorch implementations of the paper Value Functions Factorization with Latent State Information Sharing in Decentralized Multi-Agent Policy G

Hanhan 2 Aug 14, 2022
Code and models for ICCV2021 paper "Robust Object Detection via Instance-Level Temporal Cycle Confusion".

Robust Object Detection via Instance-Level Temporal Cycle Confusion This repo contains the implementation of the ICCV 2021 paper, Robust Object Detect

Xin Wang 69 Oct 13, 2022
A embed able annotation tool for end to end cross document co-reference

CoRefi CoRefi is an emebedable web component and stand alone suite for exaughstive Within Document and Cross Document Coreference Anntoation. For a de

PythicCoder 39 Dec 12, 2022
Learning to Stylize Novel Views

Learning to Stylize Novel Views [Project] [Paper] Contact: Hsin-Ping Huang ([ema

34 Nov 27, 2022
The mini-AlphaStar (mini-AS, or mAS) - mini-scale version (non-official) of the AlphaStar (AS)

A mini-scale reproduction code of the AlphaStar program. Note: the original AlphaStar is the AI proposed by DeepMind to play StarCraft II.

Ruo-Ze Liu 216 Jan 04, 2023
Keras implementation of PersonLab for Multi-Person Pose Estimation and Instance Segmentation.

PersonLab This is a Keras implementation of PersonLab for Multi-Person Pose Estimation and Instance Segmentation. The model predicts heatmaps and vari

OCTI 160 Dec 21, 2022
Code release to accompany paper "Geometry-Aware Gradient Algorithms for Neural Architecture Search."

Geometry-Aware Gradient Algorithms for Neural Architecture Search This repository contains the code required to run the experiments for the DARTS sear

18 May 27, 2022
The implementation our EMNLP 2021 paper "Enhanced Language Representation with Label Knowledge for Span Extraction".

LEAR The implementation our EMNLP 2021 paper "Enhanced Language Representation with Label Knowledge for Span Extraction". See below for an overview of

杨攀 93 Jan 07, 2023
Implementation of H-UCRL Algorithm

Implementation of H-UCRL Algorithm This repository is an implementation of the H-UCRL algorithm introduced in Curi, S., Berkenkamp, F., & Krause, A. (

Sebastian Curi 25 May 20, 2022
An Unpaired Sketch-to-Photo Translation Model

Unpaired-Sketch-to-Photo-Translation We have released our code at https://github.com/rt219/Unsupervised-Sketch-to-Photo-Synthesis This project is the

38 Oct 28, 2022
maximal update parametrization (µP)

Maximal Update Parametrization (μP) and Hyperparameter Transfer (μTransfer) Paper link | Blog link In Tensor Programs V: Tuning Large Neural Networks

Microsoft 694 Jan 03, 2023
[Preprint] "Bag of Tricks for Training Deeper Graph Neural Networks A Comprehensive Benchmark Study" by Tianlong Chen*, Kaixiong Zhou*, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang

Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study Codes for [Preprint] Bag of Tricks for Training Deeper Graph

VITA 101 Dec 29, 2022
Convolutional Neural Network for 3D meshes in PyTorch

MeshCNN in PyTorch SIGGRAPH 2019 [Paper] [Project Page] MeshCNN is a general-purpose deep neural network for 3D triangular meshes, which can be used f

Rana Hanocka 1.4k Jan 04, 2023
DeepFaceLive - Live Deep Fake in python, Real-time face swap for PC streaming or video calls

DeepFaceLive - Live Deep Fake in python, Real-time face swap for PC streaming or video calls

8.3k Dec 31, 2022