

Nes2net
Overview :
Nes2Net is a lightweight nested architecture designed for foundation model-driven speech anti-fraud tasks, featuring a low error rate and suitability for audio deepfake detection. This model performs excellently 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 primarily aims to improve the efficiency and accuracy of speech recognition and anti-fraud.
Target Users :
Nes2Net is suitable for researchers, developers, and enterprise users, especially professionals engaged in audio processing and speech recognition. Its ease of use and efficiency make it ideal for deepfake detection.
Use Cases
Use Nes2Net to detect deepfake audio files and ensure audio authenticity.
Use pre-trained models in academic research to improve the accuracy of speech recognition.
Enterprises use Nes2Net for security review of audio content to prevent the spread of fake audio.
Features
Provides multiple pre-trained models for quick implementation of anti-fraud tasks.
Supports simple inference on audio; users can directly use existing models for testing.
Easy to install and use, supporting Conda and Pip installation environments.
Allows for custom model training to adapt to specific datasets.
Implements specific functional support for the CTR-SVDD dataset, suitable for research in this field.
Provides evaluation tools to calculate EER and minDCF, helping users evaluate model effectiveness.
Includes detailed instructions and example commands to reduce learning costs.
How to Use
Clone the Nes2Net repository to your local machine.
Install the required dependencies using the command: conda env create -f SVDD.yml or pip install -r requirements.txt.
Download the required pre-trained models and place them in the specified path.
Run the easy_inference_demo.py script, specifying the model path and the audio file to be tested.
Train the model as needed using the train.py script and adjust parameters.
Evaluate the model using the eval.py script to view model performance and evaluation results.
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