
Zymergen
Zymergen was a bio/tech company that engineered microbes for various industrial purposes.

Realtime Audio Variational autoEncoder for fast and high-quality neural audio synthesis.

RAVE (Realtime Audio Variational autoEncoder) is a variational autoencoder designed for fast and high-quality neural audio synthesis. Developed by Antoine Caillon and Philippe Esling, RAVE provides an official implementation for realtime audio applications. It supports dataset preparation using regular and lazy preprocessing methods, allowing training directly on raw audio files. The tool facilitates training with various configurations, including v1, v2, discrete, and causal models. Data augmentation techniques are also available to improve model generalization. RAVE is built with non-causal convolutions by default but can be configured for causal mode to lower latency. The models can be exported to torchscript files for realtime processing. RAVE finds utility in music performance, installations, and research, requiring citation when used.
RAVE (Realtime Audio Variational autoEncoder) is a variational autoencoder designed for fast and high-quality neural audio synthesis.
Explore all tools that specialize in neural audio encoding. This domain focus ensures RAVE delivers optimized results for this specific requirement.
RAVE uses a variational autoencoder architecture to encode and decode audio signals efficiently.
Allows training directly on raw audio files (mp3, ogg) without prior conversion.
Supports causal convolutions for lowering the overall latency of the model.
Provides data augmentation techniques (mute, compress, gain) to improve model generalization.
Exports trained models to torchscript files for realtime processing in environments like Max/MSP or PureData.
Install RAVE using pip: `pip install acids-rave`
Install torch and torchaudio before installing RAVE to choose appropriate versions.
Install ffmpeg on your computer using conda: `conda install ffmpeg`
Prepare your dataset using `rave preprocess --input_path /audio/folder --output_path /dataset/path --channels X (--lazy)`
Train a RAVE model using `rave train --config v2 --db_path /dataset/path --out_path /model/out --name give_a_name --channels X`
Export your trained model to a torchscript file using `rave export --run /path/to/your/run (--streaming)`
All Set
Ready to go
Verified feedback from other users.
"RAVE is praised for its realtime capabilities and high-quality audio synthesis, although some users find the setup process challenging."
Post questions, share tips, and help other users.

Zymergen was a bio/tech company that engineered microbes for various industrial purposes.

Uncover and optimize your SaaS investment.

A powerful shell designed for interactive use and scripting.

Zopto was a LinkedIn automation tool designed to generate leads, but it is now defunct.

AI-powered collaboration platform that reimagines teamwork through unified communication and workspace automation.

Maximize your Amazon sales and grow your business with powerful, accurate data and AI-driven listing optimization.

Your one-stop static site engine.

A customer engagement platform for marketing, sales, and support teams to enhance communication with website visitors at every stage of the customer lifecycle.