AudioGen

Input—per 1M tokens

Output—per 1M tokens

Context—tokens

WeightsClosed

About

AudioGen is ranked #35 of 124 in AI music generators on Inferse. It runs on API, Linux, Self-hosted. There is a free plan.

Compared on AI music generators

Free plan: Yesgithub.com

Facts

Free plan: Yesgithub.com · 20 Sept 2026
What it does: AudioGen is a text-guided audio generation model that generates sounds from text.github.com · 3 Oct 2026
Intended users: The model card names audio, machine learning, and AI researchers, as well as amateurs learning about generative models, as primary users.github.com · 3 Oct 2026
Model architecture: The released model combines EnCodec audio tokenization with an autoregressive Transformer language model and has 1.5 billion parameters.github.com · 3 Oct 2026
Prompt generation: The documented API generates audio samples from text descriptions, with an example configured to generate five-second samples.github.com · 3 Oct 2026
Generation modes: The training documentation describes conditional and unconditional generation, audio continuation from a prompt, and greedy, temperature, top-K, and top-P sampling.github.com · 3 Oct 2026
Model availability: The AudioGen instructions list one pretrained model, facebook/audiogen-medium, and a local Jupyter notebook demo.github.com · 3 Oct 2026
Hardware requirement: The instructions say inference with the medium-sized models requires a GPU with at least 16 GB of memory.github.com · 3 Oct 2026
License: The repository says its code is MIT-licensed and its model weights use CC-BY-NC 4.0.github.com · 3 Oct 2026
Training data: The instructions say the datasets used to train AudioGen are not provided.github.com · 3 Oct 2026
Language limit: The model card says AudioGen was trained on English descriptions and performs less well in other languages.github.com · 3 Oct 2026
Output limit: The model card says AudioGen cannot generate realistic vocals and may require prompt engineering for satisfying results.github.com · 3 Oct 2026
Responsible use: The model card advises against downstream use without further risk evaluation and mitigation.github.com · 3 Oct 2026
Support: The model card directs questions and comments to the project’s GitHub repository or its issue tracker.github.com · 3 Oct 2026
Purpose: AudioGen is a text-to-sound generation model provided through AudioCraft.github.com · 3 Oct 2026
Model design: The provided reimplementation is a single-stage autoregressive Transformer trained over a 16 kHz EnCodec tokenizer with four codebooks sampled at 50 Hz.github.com · 3 Oct 2026
Model distinction: The provided models are not the original models used to report results in the AudioGen publication.github.com · 3 Oct 2026
Available model: The page lists one pretrained AudioGen model, facebook/audiogen-medium, with 1.5 billion parameters.github.com · 3 Oct 2026
Generation: The example API generates sound from text descriptions and shows saving output as WAV audio.github.com · 3 Oct 2026
Sampling controls: Generation supports greedy sampling, temperature sampling, top-K sampling, and top-P nucleus sampling.github.com · 3 Oct 2026
Audio continuation: The generation stage supports conditional or unconditional sample generation and audio continuation from a prompt.github.com · 3 Oct 2026
Installation: AudioCraft installation requires Python 3.9 and PyTorch 2.1.0; ffmpeg is also recommended by the repository instructions.github.com · 3 Oct 2026
Local demo: The maker provides a Jupyter notebook demo that can be run locally with a GPU.github.com · 3 Oct 2026
Training: AudioGenSolver implements the training pipeline, but the maker says it may not fully reproduce the paper results and does not provide the AudioGen training datasets.github.com · 3 Oct 2026