AudioSeal

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Introduce：

AudioSeal is a localized watermarking technology for AI-generated voice audio with state-of-the-art robustness and extremely fast detection speeds. It does this by jointly training an embedded watermark generator and a detector to detect watermarked fragments in longer audio, even in the case of audio editing. AudioSeal has designed a fast single-pass detector that detects two orders of magnitude faster than existing models, making it ideal for large-scale and real-time applications.

Stakeholders:
AudioSeal is for developers and businesses that need to copyright and validate AI-generated voice audio. It is particularly suitable for real-time monitoring and management of large-scale audio content, such as in the music industry, podcasts, audiobooks, etc.
Usage Scenario Examples:

• The music industry uses AudioSeal to protect original works from unauthorized copying and distribution.
• Podcast creators utilize AudioSeal to ensure the integrity and authenticity of their content.
• The audio book platform uses AudioSeal technology to secure the copyright and trace the source of audio content.

The features of the tool:

• Generator: Input audio signal, output the same size watermark, can be added to the input for watermarking.
• Detector: The probability that each sample in the input audio signal contains a watermark.
• Support 16-bit secret message encoding, optionally embedded in the watermark.
• The detector can output the secret message encoded in the watermark.
• Fast inspection for large-scale and real-time applications.
• Provides training code that allows users to build their own watermarking models.

Steps for Use:

• 1. Install the required Python environment and dependent libraries.
• 2. Clone the AudioSeal codebase from GitHub or install it through PyPI.
• 3. Load the AudioSeal generator and detector model.
• 4. Use generator to watermark audio signals.
• 5. Use the detector to detect the watermark audio and obtain the probability of the watermark.
• 6. If necessary, decode the secret message from the detector output.
• 7. Train your own watermark model or use the provided model as needed.

Tool’s Tabs: Speech watermark, generated by AI