AudioCraft Review, Pricing & Alternatives (2026)

AudioCraft is a PyTorch library from Meta's Fundamental AI Research team for audio generation and processing. Meta announced the framework in August 2023, after releasing MusicGen in June of that year. The repository contains inference and training code for MusicGen, AudioGen, EnCodec, Multi Band Diffusion, MAGNeT, AudioSeal, MusicGen Style, and JASCO. The GitHub repository lists more than 23,000 stars and requires Python 3.9 with PyTorch 2.1.0. AudioCraft is software rather than a hosted creator service, meaning users install dependencies, download checkpoints, and supply their own compute. Meta says the project was released to help researchers reproduce results, train models on their own datasets, and build new audio systems from shared components.

A typical AudioCraft workflow starts with installing PyTorch, the AudioCraft package, and ffmpeg, then loading a named pretrained model through a Python interface. MusicGen accepts text descriptions and can also condition generation on a melody reference. Its documented models range from the 300 million parameter small model to 1.5 billion parameter medium and melody models, and 3.3 billion parameter large models. AudioGen targets environmental sounds and effects from text. JASCO adds local musical controls, including chords, drums, and melody, while EnCodec handles neural audio tokenization and compression. Users set generation parameters such as duration, sampling strategy, temperature, top K, or top P, generate tensors, and write the output to WAV. Local Gradio demos and notebooks are also included.

MusicGen uses an autoregressive Transformer over a 32 kHz EnCodec representation with four codebooks sampled at 50 Hz. Meta trained it on about 20,000 hours of licensed music, including the Meta Music Initiative Sound Collection, Shutterstock, and Pond5 data. JASCO is the most notable release covered by the project's 2025 changelog: version 1.4.0a2 added training and inference code plus Hugging Face checkpoints. JASCO uses flow matching with an EnCodec tokenizer and offers 400 million and 1 billion parameter variants for text with chords and drums, or text with chords, drums, and melody. The published JASCO checkpoints generate 10 second samples. AudioCraft also includes MAGNeT, a non autoregressive generator, and AudioSeal for audio watermarking.

AudioCraft has no hosted subscription, metered endpoint, or official paid plan listed in the project documentation. The software itself is free, and the repository code is MIT licensed. Users pay indirectly through a suitable local machine, cloud GPU, storage, and model downloads. MusicGen documentation recommends a GPU with at least 16 GB of memory for medium models, while smaller models can run with less memory for shorter sequences. The released pretrained weights are CC BY NC 4.0, so free access does not mean unrestricted commercial use. Training datasets are not included, and custom training requires users to prepare audio and metadata. The practical cost therefore varies by hardware and generation volume rather than by AudioCraft credits or minutes.

AudioCraft's main weakness is operational: it is a research codebase that expects Python, PyTorch, model management, and GPU troubleshooting. The model cards also report weak realistic vocals, reduced performance outside English descriptions, uneven results across musical cultures, and occasional collapse to silence. Suno and Udio offer polished hosted creation workflows with no local installation, while Stability AI's Stable Audio provides a more service oriented route for text to audio generation. AudioCraft is more attractive when code access, local inference, fine tuning, or inspectable model components matter. Pick it for research, prototyping, and controlled experiments, not for a turnkey commercial music studio or a guaranteed commercial license.