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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}
}
```
|