AudioGen vs VTS
AudioGen
github.comVTS
github.comSide by side
| Figure | AudioGen | VTS |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Linux | Yes | — |
| API | Yes | — |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 0 |
| Spec score · AI Sound Effect Generators | 8.8 (#2) | 6.6 (#29) |
| Generation | The example API generates sound from text descriptions and shows saving output as WAV audio. | Generated WAV files are written to a chosen output directory, and the default output duration follows the input audio duration unless a duration is specified. |
| Intended users | The model card names audio, machine learning, and AI researchers, as well as amateurs learning about generative models, as primary users. | The checkpoint is intended for research and creative sound-effect generation from vocal sketches or short audio sketches plus text prompts. |
| License | The repository says its code is MIT-licensed and its model weights use CC-BY-NC 4.0. | The project and model checkpoint are listed under the MIT License. |
| Purpose | AudioGen is a text-to-sound generation model provided through AudioCraft. | VTS generates sound effects from a short vocal or audio sketch combined with a text prompt. |
| Support | The model card directs questions and comments to the project’s GitHub repository or its issue tracker. | The repository says to contact the maker at [email protected] with questions. |
| 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. | Training code and dataset manifests are not included in the inference package. |
Plans
In their own words
AudioGen
AudioGen is a text-to-sound model provided through AudioCraft. It generates audio samples from text descriptions and can continue from an audio prompt, with conditional and unconditional generation modes. Sampling controls include greedy, temperature, top-K, and top-P methods. The listed pretrained model, facebook/audiogen-medium, has 1.5 billion parameters; the example API generates sound and saves it as WAV. The implementation uses an autoregressive Transformer and a 16 kHz EnCodec tokenizer. The repository provides API access and a local Jupyter notebook demo. Running the medium model locally requires a GPU with at least 16 GB of memory; installation requires Python 3.9 and PyTorch 2.1.0, with ffmpeg recommended. The model was trained on English descriptions and performs less well in other languages. It cannot generate realistic vocals, and prompts may need refinement. Code is MIT-licensed, while weights use CC-BY-NC 4.0; training datasets are not provided. The listed self-hosted plan is free.