AudioSeal: Proactive Localized Watermarking
AudioSeal is a cutting-edge tool designed for embedding invisible watermarks into speech audio, which enhances the traceability and authenticity of audio files. Through the collaboration of its two core components—the generator and the detector—AudioSeal achieves exceptional speed and robustness in identifying watermarks even in modified audio files.
Key Features
- Localized Watermarking: Watermarks are inserted at an incredibly fine level of detail, specifically at 1/16,000th of a second. This allows for precise embedding without affecting audio quality.
- Robustness: AudioSeal maintains watermark integrity across a variety of audio modifications such as compression, re-encoding, and adding noise, ensuring the original content's traceability.
- Fast Detection: The detection process is designed to be extremely fast, up to one hundred times faster than existing models. This makes AudioSeal ideal for large-scale and real-time applications.
Installation
To start using AudioSeal, users need Python 3.8 or higher and Pytorch 1.13.0 or above. Required packages such as Omegaconf, Julius, and Numpy should be installed. The software can be easily installed via PyPI with the command:
pip install audioseal
For those interested in modifying or directly accessing the source code, cloning the repository from GitHub is an option:
git clone https://github.com/facebookresearch/audioseal
cd audioseal
pip install -e .
Models
AudioSeal offers both a generator and a detector. The generator embeds a watermark within the audio signal, and users can include a secret 16-bit message in the watermark. The detector identifies these watermarks and can retrieve the secret message, all while operating at impressive speeds.
- AudioSeal Generator: Produces a watermark that matches the input audio's size and can encode additional messages.
- AudioSeal Detector: Evaluates and confirms the presence of a watermark at every small time-sample and retrieves any embedded message.
Usage
Using AudioSeal involves loading the appropriate models and applying them to audio data:
from audioseal import AudioSeal
model = AudioSeal.load_generator("audioseal_wm_16bits")
wav, sr = ..., 16000
watermark = model.get_watermark(wav, sr)
watermarked_audio = wav + watermark
detector = AudioSeal.load_detector("audioseal_detector_16bits")
result, message = detector.detect_watermark(watermarked_audio, sr)
This snippet offers a glance into how one can integrate watermarking and detection into their audio processing tasks.
Contributing and Support
AudioSeal welcomes contributions and feedback from the community. Interested developers can submit pull requests or propose improvements through GitHub issues. For any difficulties during usage, the project offers troubleshooting guidance related to common errors.
Licensing and Credits
AudioSeal is released under the MIT license, promoting free software use and innovation. The project is maintained by a team of developers, including Tuan Tran, Hady Elsahar, Pierre Fernandez, and Robin San Roman, who ensure its continuous improvement and upkeep.
For those working or interested in the field of audio watermarking, AudioSeal represents a significant advancement, providing a reliable and efficient solution for modern audio authentication needs.