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