Pytorch port of Google Research's VGGish model used for extracting audio features.

Audio search using Azure Cognitive Search

Visualizations of music semantics calculus using Spotify and deep embeddings.

SERVER: Multi-modal Speech Emotion Recognition using Transformer-based and Vision-based Embeddings

Audio encoding authentication and validation library for verifying audio as being from a trusted source

Extract audio embeddings from an audio file using Python

A python library to train Deep Neural Networks on various audio tasks using Self-Supervised backbones.

Offline AI music recommender & recogniser. Local song ID + smart playlists (CLAP + FAISS).

Directly from voice, recognise speaker emotion, intensity, & sentiment in speaker utterances.

Generate audio embedding out of pruned L3

AudioMuse-AI-DCLAP is a lightweight, high-speed distilled version of LAION CLAP, designed for fast and efficient text-to-music search

Audio Deep Learning Project in Java

Re-Implementation of Google Research's VGGish model used for extracting audio features using Pytorch with GPU support.

Audio Embeddings using VGGish

Competition-winning SED (Sound Event Detection) system that identifies audio events using ML, optimized for real-world error cost reduction.

Group together similar music