Nes2Net
Lightweight nested architecture for voice anti-fraud.
Product Details
Nes2Net is a lightweight nested architecture designed for basic model-driven speech anti-fraud tasks, with low error rates and suitable for audio deepfakes detection. The model has performed well on multiple datasets, and the pre-trained model and code have been released on GitHub for easy use by researchers and developers. Suitable for audio processing and security fields, it is mainly positioned to improve the efficiency and accuracy of speech recognition and anti-fraud.
Main Features
How to Use
Target Users
Nes2Net is suitable for researchers, developers and enterprise users, especially professionals working in audio processing and speech recognition. Its ease of use and efficiency make it ideal for deepfakes detection.
Examples
Use Nes2Net to detect deepfake audio files and ensure the authenticity of the audio.
Using pretrained models in academic research to improve speech recognition accuracy.
Enterprises use Nes2Net to conduct security reviews of audio content to prevent the spread of fake audio.
Quick Access
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