--- license: mit pipeline_tag: text-to-audio library_name: pytorch language: - en tags: - audio - text-to-audio - audio-generation - flow-matching - meanflow - onnx --- # TinyAudio Checkpoints for **TinyAudio: Compact and Efficient Text-to-Audio Generation for Low-Resource Deployment**. [Paper](https://arxiv.org/abs/2609.31525) · [Code](https://github.com/junxi25liu/TinyAudio) · [Demo](https://tinyaudio-project.github.io/) TinyAudio is a complete text-to-audio system with only 87M parameters, covering the text encoder, the generator and the decoder. It generates 44.1 kHz audio with 0.48 GB of peak GPU memory. Its one-step variant, TinyAudio-MF, generates audio in real time on a four-core CPU quota. ## Files | Path | Description | |---|---| | `weights/tinyaudio.pth` | TinyAudio generator, flow matching, 25 steps | | `weights/tinyaudio_mf.pth` | TinyAudio-MF generator, MeanFlow, 1 step | | `weights/ta_clap_ettin32m.pt` | TA-CLAP text encoder (32M) | | `weights/ta_vae/` | TA-VAE autoencoder | | `cpu/standard/` | ONNX export of TinyAudio-MF for CPU inference with ONNX Runtime | ## Usage Checkpoints are downloaded automatically by the code repository. ```bash git clone https://github.com/junxi25liu/TinyAudio.git cd TinyAudio pip install -e . python infer.py --prompt "A dog is barking while a car passes by" ``` ```python from tinyaudio import TinyAudioPipeline pipe = TinyAudioPipeline.from_pretrained("tinyaudio_mf") audio = pipe("A dog is barking while a car passes by") pipe.save(audio[0], "dog.wav") ``` For CPU inference without PyTorch, run `python cpu/tinyaudio_onnx.py "your prompt"`. See the [code repository](https://github.com/junxi25liu/TinyAudio) for training and evaluation. ## Citation ```bibtex @article{liu2026tinyaudio, title = {TinyAudio: Compact and Efficient Text-to-Audio Generation for Low-Resource Deployment}, author = {Liu, Junxi and Li, Xiquan and Guan, Wenhao and Duan, Yifan and Niu, Zhikang and Huo, Yanru and Ma, Ziyang and Chen, Xie}, journal = {arXiv preprint arXiv:2609.31525}, year = {2026} } ```