| --- |
| 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} |
| } |
| ``` |
|
|
