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---
title: README
emoji: 🦀
colorFrom: pink
colorTo: gray
sdk: static
pinned: false
---
# [TTSDS Benchmark](https://ttsdsbenchmark.com)
As many recent Text-to-Speech (TTS) models have shown, synthetic audio can be close to real human speech.
However, traditional evaluation methods for TTS systems need an update to keep pace with these new developments.
Our TTSDS benchmark assesses the quality of synthetic speech by considering factors like prosody, speaker identity, and intelligibility.
By comparing these factors with both real speech and noise datasets, we can better understand how synthetic speech stacks up.
## More information
More details can be found in our paper [*TTSDS -- Text-to-Speech Distribution Score*](https://arxiv.org/abs/2407.12707).
## Reproducibility
To reproduce our results, check out our repository [here](https://github.com/ttsds/ttsds).
## Citation
```
@misc{minixhofer2024ttsds,
title={TTSDS -- Text-to-Speech Distribution Score},
author={Christoph Minixhofer and Ondřej Klejch and Peter Bell},
year={2024},
eprint={2407.12707},
archivePrefix={arXiv},
primaryClass={eess.AS},
url={https://arxiv.org/abs/2407.12707},
}
```
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