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0:00 Introduction to Audiocraft full tutorial with example AI generated music
0:30 Sample several songs made by me via Audiocraft
1:27 How to save generated music files on your computer
3:53 How to install Audiocraft
6:49 How to do automatic installation with my special scripts
8:52 How to start Audiocraft / MusicGen application and use it
11:57 Where the Audiocraft / MusicGen model files are saved
12:31 How to use condition melody to generate a song
14:25 How to use Audiocraft / MusicGen on Google Colab
15:50 How to use automatic run script that I have shared
18:12 Very long text prompt experimentation
19:25 Very epic music generated by Audiocraft
20:44 How to close Google Colab runtime – turn it off
22:34 Amazing music generated by MusicGen
23:50 How to use pip freeze to see versions of all installed libraries
24:22 How to install specific version of a library in Python
Audiocraft is a library for audio processing and generation with deep learning. It features the state-of-the-art EnCodec audio compressor / tokenizer, along with MusicGen, a simple and controllable music generation LM with textual and melodic conditioning.
Audiocraft is a PyTorch library for deep learning research on audio generation. At the moment, it contains the code for MusicGen, a state-of-the-art controllable text-to-music model.
MusicGen
Audiocraft provides the code and models for MusicGen, a simple and controllable model for music generation. MusicGen is a single stage auto-regressive Transformer model trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz. Unlike existing methods like MusicLM, MusicGen doesn’t require a self-supervised semantic representation, and it generates all 4 codebooks in one pass. By introducing a small delay between the codebooks, we show we can predict them in parallel, thus having only 50 auto-regressive steps per second of audio. Check out our sample page or test the available demo!
MusicGen Model Card
Model details
Organization developing the model: The FAIR team of Meta AI.
Model date: MusicGen was trained between April 2023 and May 2023.
Model version: This is the version 1 of the model.
Model type: MusicGen consists of an EnCodec model for audio tokenization, an auto-regressive language model based on the transformer architecture for music modeling. The model comes in different sizes: 300M, 1.5B and 3.3B parameters ; and two variants: a model trained for text-to-music generation task and a model trained for melody-guided music generation.
Where to send questions or comments about the model: Questions and comments about MusicGen can be sent via the Github repository of the project, or by opening an issue.
Intended use
Primary intended use: The primary use of MusicGen is research on AI-based music generation, including:
Research efforts, such as probing and better understanding the limitations of generative models to further improve the state of science
Generation of music guided by text or melody to understand current abilities of generative AI models by machine learning amateurs
Primary intended users: The primary intended users of the model are researchers in audio, machine learning and artificial intelligence, as well as amateur seeking to better understand those models.
Out-of-scope use cases The model should not be used on downstream applications without further risk evaluation and mitigation. The model should not be used to intentionally create or disseminate music pieces that create hostile or alienating environments for people. This includes generating music that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
Adventure by Alexander Nakarada | https://www.serpentsoundstudios.com
Music promoted by https://www.free-stock-music.com
Attribution 4.0 International (CC BY 4.0)
