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
Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces

This repository contains source code for the paper Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces a

9 Nov 21, 2022
Human segmentation models, training/inference code, and trained weights, implemented in PyTorch

Human-Segmentation-PyTorch Human segmentation models, training/inference code, and trained weights, implemented in PyTorch. Supported networks UNet: b

Thuy Ng 474 Dec 19, 2022
Deep Learning with PyTorch made easy 🚀 !

Deep Learning with PyTorch made easy 🚀 ! Carefree? carefree-learn aims to provide CAREFREE usages for both users and developers. It also provides a c

381 Dec 22, 2022
TensorFlow implementation of "Attention is all you need (Transformer)"

[TensorFlow 2] Attention is all you need (Transformer) TensorFlow implementation of "Attention is all you need (Transformer)" Dataset The MNIST datase

YeongHyeon Park 4 Jan 05, 2022
A model which classifies reviews as positive or negative.

SentiMent Analysis In this project I built a model to classify movie reviews fromn the IMDB dataset of 50K reviews. WordtoVec : Neural networks only w

Rishabh Bali 2 Feb 09, 2022
This is an official implementation for "Exploiting Temporal Contexts with Strided Transformer for 3D Human Pose Estimation".

Exploiting Temporal Contexts with Strided Transformer for 3D Human Pose Estimation This repo is the official implementation of Exploiting Temporal Con

Vegetabird 241 Jan 07, 2023
A PyTorch implementation for Unsupervised Domain Adaptation by Backpropagation(DANN), support Office-31 and Office-Home dataset

DANN A PyTorch implementation for Unsupervised Domain Adaptation by Backpropagation Prerequisites Linux or OSX NVIDIA GPU + CUDA (may CuDNN) and corre

8 Apr 16, 2022
Python program that works as a contact list

Lista de Contatos Programa em Python que funciona como uma lista de contatos. Features Adicionar novo contato Remover contato Atualizar contato Pesqui

Victor B. Lino 3 Dec 16, 2021
Predict bus arrival time using VertexAI and Nvidia's Jetson Nano

bus_prediction predict bus arrival time using VertexAI and Nvidia's Jetson Nano imagenet the command for imagenet.py look like this python3 /path/to/i

10 Dec 22, 2022
Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search

CLIP-GLaSS Repository for the paper Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search An in-browser demo is

Federico Galatolo 172 Dec 22, 2022
ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation

ST++ This is the official PyTorch implementation of our paper: ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation. Lihe Ya

Lihe Yang 147 Jan 03, 2023
Pretraining Representations For Data-Efficient Reinforcement Learning

Pretraining Representations For Data-Efficient Reinforcement Learning Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Ch

Mila 40 Dec 11, 2022
The Face Mask recognition system uses AI technology to detect the person with or without a mask.

Face Mask Detection Face Mask Detection system built with OpenCV, Keras/TensorFlow using Deep Learning and Computer Vision concepts in order to detect

Rohan Kasabe 4 Apr 05, 2022
PN-Net a neural field-based framework for depth estimation from single-view RGB images.

PN-Net We present a neural field-based framework for depth estimation from single-view RGB images. Rather than representing a 2D depth map as a single

1 Oct 02, 2021
Internship Assessment Task for BaggageAI.

BaggageAI Internship Task Problem Statement: You are given two sets of images:- background and threat objects. Background images are the background x-

Arya Shah 10 Nov 14, 2022
GluonMM is a library of transformer models for computer vision and multi-modality research

GluonMM is a library of transformer models for computer vision and multi-modality research. It contains reference implementations of widely adopted baseline models and also research work from Amazon

42 Dec 02, 2022
GeDML is an easy-to-use generalized deep metric learning library

GeDML is an easy-to-use generalized deep metric learning library

Borui Zhang 32 Dec 05, 2022
nfelo: a power ranking, prediction, and betting model for the NFL

nfelo nfelo is a power ranking, prediction, and betting model for the NFL. Nfelo take's 538's Elo framework and further adapts it for the NFL, hence t

6 Nov 22, 2022
A pytorch implementation of the CVPR2021 paper "VSPW: A Large-scale Dataset for Video Scene Parsing in the Wild"

VSPW: A Large-scale Dataset for Video Scene Parsing in the Wild A pytorch implementation of the CVPR2021 paper "VSPW: A Large-scale Dataset for Video

45 Nov 29, 2022
Hierarchical User Intent Graph Network for Multimedia Recommendation

Hierarchical User Intent Graph Network for Multimedia Recommendation This is our Pytorch implementation for the paper: Hierarchical User Intent Graph

6 Jan 05, 2023