Perth is a comprehensive Python library for audio watermarking and detection.
Perth enables you to embed imperceptible watermarks in audio files and later detect them, even after the audio has undergone various transformations or manipulations. The library implements multiple watermarking techniques including neural network-based approaches.
- Multiple Watermarking Techniques: Including the Perth-Net Implicit neural network approach
- Robust Watermarks: Watermarks can survive common audio transformations like compression, resampling, and more
- Command-Line Interface: Easy to use CLI for quick watermarking tasks
- Python API: Comprehensive API for integration into your applications
- Quality Metrics: Tools to evaluate the perceptual quality of watermarked audio
pip install resemble-perthgit clone https://github.com/resemble-ai/Perth cd Perth pip install -e .# Apply a watermark to an audio file perth input.wav -o output.wav # Extract a watermark from an audio file perth input.wav --extractimport perth import librosa import soundfile as sf # Load audio file wav, sr = librosa.load("input.wav", sr=None) # Initialize watermarker watermarker = perth.PerthImplicitWatermarker() # Apply watermark watermarked_audio = watermarker.apply_watermark(wav, watermark=None, sample_rate=sr) # Save watermarked audio sf.write("output.wav", watermarked_audio, sr)import perth import librosa # Load the watermarked audio watermarked_audio, sr = librosa.load("output.wav", sr=None) # Initialize watermarker (same as used for embedding) watermarker = perth.PerthImplicitWatermarker() # Extract watermark watermark = watermarker.get_watermark(watermarked_audio, sample_rate=sr) print(f"Extracted watermark: {watermark}")The Perth-Net Implicit watermarker uses a neural network-based approach for embedding and extracting watermarks. It's designed to be robust against various audio manipulations while maintaining high audio quality.
from perth.perth_net.perth_net_implicit.perth_watermarker import PerthImplicitWatermarker watermarker = PerthImplicitWatermarker(device="cuda") # Use GPU for faster processingA simple placeholder watermarker for testing and demonstration purposes.
from perth import DummyWatermarker watermarker = DummyWatermarker()The library includes utilities for evaluating the quality and robustness of watermarked audio:
import librosa from perth.utils import calculate_audio_metrics, plot_audio_comparison # Load original and watermarked audio original, sr = librosa.load("input.wav", sr=None) watermarked, _ = librosa.load("output.wav", sr=None) # Calculate quality metrics metrics = calculate_audio_metrics(original, watermarked) print(f"SNR: {metrics['snr']:.2f} dB") print(f"PSNR: {metrics['psnr']:.2f} dB") # Visualize differences plot_audio_comparison(original, watermarked, sr, output_path="comparison.png")Contributions are welcome! Please feel free to submit a Pull Request.
This project is licensed under the MIT License - see the LICENSE file for details.
