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README.md
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@@ -9,7 +9,7 @@ We introduce AudioSeal, a method for speech localized watermarking, with state-o
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Audioseal achieves state-of-the-art detection performance of both natural and synthetic speech at the sample level (1/16k second resolution), it generates limited alteration of signal quality and is robust to many types of audio editing.
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Audioseal is designed with a fast, single-pass detector, that significantly surpasses existing models in speed — achieving detection up to two orders of magnitude faster, making it ideal for large-scale and real-time applications.
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AudioSeal requires Python >=3.8, Pytorch >= 1.13.0, [omegaconf](https://omegaconf.readthedocs.io/), [julius](https://pypi.org/project/julius/), and numpy. To install from PyPI:
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pip install -e .
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```
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We provide the checkpoints for the following models:
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**Note**: We are working to release the training code for anyone wants to build their own watermarker. Stay tuned !
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Audioseal provides a simple API to watermark and detect the watermarks from an audio sample. Example usage:
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Audioseal achieves state-of-the-art detection performance of both natural and synthetic speech at the sample level (1/16k second resolution), it generates limited alteration of signal quality and is robust to many types of audio editing.
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Audioseal is designed with a fast, single-pass detector, that significantly surpasses existing models in speed — achieving detection up to two orders of magnitude faster, making it ideal for large-scale and real-time applications.
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# 🧉 Installation
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AudioSeal requires Python >=3.8, Pytorch >= 1.13.0, [omegaconf](https://omegaconf.readthedocs.io/), [julius](https://pypi.org/project/julius/), and numpy. To install from PyPI:
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pip install -e .
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```
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# ⚙️ Models
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We provide the checkpoints for the following models:
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**Note**: We are working to release the training code for anyone wants to build their own watermarker. Stay tuned !
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# 🧮 Usage
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Audioseal provides a simple API to watermark and detect the watermarks from an audio sample. Example usage:
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