Search Results for "music learning"

AudioCraft is a PyTorch library for text-to-audio and text-to-music generation, packaging research models and tooling for training and inference. It includes MusicGen for music generation conditioned on text (and optionally melody) and AudioGen for text-conditioned sound effects and environmental audio. Both models operate over discrete audio tokens produced by a neural codec (EnCodec), which acts like a tokenizer for waveforms and enables efficient sequence modeling. The repo provides...

The Gemini Cookbook is an official repository of examples and guides for using Google’s Gemini API. It provides a structured learning path with quick-start tutorials for beginners and practical examples for advanced users. The repository covers a wide range of Gemini capabilities, including text, images, video, speech, robotics, and multimodal interactions. It highlights newly introduced features such as Gemini 2.5 models (Flash and Pro), Gemini’s native image generation, Veo for video generation, robotics-focused reasoning models, and Lyria for TTS and music generation. ...

A community-maintained directory of real-world, open-source iOS applications. It showcases a variety of apps (mostly written in Swift or Objective-C) covering utilities, UI examples, games, and more—as a learning reference or starting point for iOS developers. Help analyse individuals’ travel and trace their movements when cases of infection arise. Helps developers learn architecture patterns like MVVM, Combine, and SwiftUI. Regularly updated with projects reflecting current iOS development trends.

Marsyas (Music Analysis, Retrieval and Synthesis for Audio Signals) is a framework for developing systems for audio processing. It provides an general architecture for connecting audio, soundfiles, signal processing blocks and machine learning. Source code at SF is outdated! Marsyas is now hosted at GitHub: https://github.com/marsyas/marsyas Downloads are now provided at Bintray: https://bintray.com/marsyas

musicinformationretrieval.com is a collection of instructional materials for music information retrieval (MIR). These materials contain a mix of casual conversation, technical discussion, and Python code. These pages, including the one you're reading, are authored using Colab notebooks.