CAM++ - A speaker recognition model integrated with FunClip for efficient audio segment clipping.

## CAM++ Overview CAM++ is a speaker recognition model integrated with FunClip, designed for identifying and clipping audio segments based on speaker IDs. It is optimized for Chinese speech at a 16k Hz sampling rate and is lightweight, efficient, and easy to deploy. ## Key Features of CAM++ CAM++ offers several key features: - **Efficiency and Accuracy**: High accuracy in speaker verification with low computational complexity. - **Auto Registration**: Supports automatic speaker registration with a threshold. - **Lightweight Design**: The ONNX model size is 28M, with no dependencies on PyTorch or torchaudio. - **Architectural Enhancements**: Utilizes D-TDNN as the backbone with an enhanced Context-Aware Masking (CAM) module and multi-granularity pooling. ## CAM++ in FunClip CAM++ is integrated into FunClip to enable users to automatically recognize speaker IDs and clip audio segments belonging to specific speakers. This functionality is particularly useful for editing multi-speaker recordings in Chinese. ## Technical Specifications of CAM++ CAM++ has the following technical specifications: - **Language Support**: Designed for Chinese speech (zh-cn). - **Sampling Rate**: 16k Hz. - **Model Format**: ONNX. - **Model Size**: 28M. - **Dependencies**: None (no PyTorch or torchaudio). ## Installation and Usage of CAM++ To install and use CAM++: 1. **Installation**: - Clone the repository: `git clone https://github.com/lovemefan/campplus` - Navigate to the directory: `cd campplus` - Install the package: `python setup.py install` 2. **Usage**: - Initialize the model: `model = Campplus(threshold=0.7)` - Recognize speaker IDs: `index = model.recognize('test/a_cn_16k.wav')` - Print the output: `print(index)` ## Primary Function of CAM++ The primary function of CAM++ is speaker recognition, which involves identifying speaker IDs from audio inputs. This is essential for applications like voice authentication, speaker diarization, and audio editing in FunClip. ## Architectural Backbone of CAM++ CAM++ utilizes a densely connected time delay neural network (D-TDNN) as its backbone, enhanced with a Context-Aware Masking (CAM) module and multi-granularity pooling to capture both global and segment-level contextual information. ## Model Size of CAM++ The ONNX model size of CAM++ is 28M, making it lightweight and easy to deploy without dependencies on PyTorch or torchaudio. ## Sampling Rate of CAM++ CAM++ supports a sampling rate of 16k Hz, which is suitable for common audio processing standards, particularly for Chinese speech. ## Threshold in CAM++ The threshold in CAM++ is used to control the discrimination level for speaker registration and verification. Setting a threshold (e.g., 0.7) adjusts how strictly the model distinguishes between different speakers. ### Citation sources: - [CAM++](https://modelscope.cn/models/iic/speech_campplus_sv_zh-cn_16k-common/summary) - Official URL Updated: 2025-03-26