Lab Materials for MIT 6.S191: Introduction to Deep Learning

Amphion (/æmˈfaɪən/) is a toolkit for Audio, Music, and Speech Generation. Its purpose is to support reproducible research and help junior researchers and engineers get started in the field of audio, music, and speech generation research and development.

An AI for Music Generation

🧠+🎧 Build your music algorithms and AI models with the next-gen DAW 🔥

Resources on Music Generation with Deep Learning

Apply diffusion models using the new Hugging Face diffusers package to synthesize music instead of images.

MIDI / symbolic music tokenizers for Deep Learning models 🎶

Train an LSTM to generate piano or violin/piano music.

a list of demo websites for automatic music generation research

"Pop Music Transformer: Beat-based Modeling and Generation of Expressive Pop Piano Compositions", ACM Multimedia 2020

Generate music from the entropy of Linux 🐧🎵

Implementation of MusicLM, a text to music model published by Google Research, with a few modifications.

A toolkit for symbolic music generation

A paper and project list about the cutting edge Speech Synthesis, Text-to-Speech (TTS), Singing Voice Synthesis (SVS), Voice Conversion (VC), Singing Voice Conversion (SVC), and related interesting works (such as Music Synthesis, Automatic Music Transcription, Automatic MOS Prediction, SSL-based ASR...etc).

A complete and open application for automatic backing tracks generation.

Experiment diverse Deep learning models for music generation with TensorFlow

AI Audio Datasets 🎵. A list of datasets consisting of speech, music, and sound effects, which can provide training data for Generative AI, AIGC, AI model training, intelligent audio tool development, and audio applications.

This is the dataset repository for the paper: POP909: A Pop-song Dataset for Music Arrangement Generation

music generation with masked transformers!

State of the Art of Music Generation with Deep Learning and AI