This project uses a variety of advanced voiceprint recognition models such as EcapaTdnn, ResNetSE, ERes2Net, CAM++, etc. It is not excluded that more models will be supported in the future. At the same time, this project also supports MelSpectrogram, Spectrogram data preprocessing methods

Research and Production Oriented Speaker Verification, Recognition and Diarization Toolkit

Unofficial reimplementation of ECAPA-TDNN for speaker recognition (EER=0.86 for Vox1_O when train only in Vox2)

The Pytorch implementation of sound classification supports EcapaTdnn, PANNS, TDNN, Res2Net, ResNetSE and other models, as well as a variety of preprocessing methods.

本项目使用了EcapaTdnn、ResNetSE、ERes2Net、CAM++等多种先进的声纹识别模型，同时本项目也支持了MelSpectrogram、Spectrogram、MFCC、Fbank等多种数据预处理方法

基于PaddlePaddle实现的音频分类，支持EcapaTdnn、PANNS、TDNN、Res2Net、ResNetSE等各种模型，还有多种预处理方法

Verifying the identity of a person from characteristics of the voice independent from language via NVIDIA NeMo models (ECAPA-TDNN, SpeakerNet, TitaNet-L).

Speaker verification task with ECAPA-TDNN model (trained on Persian dataset)

针对CN-Celeb数据集的基于ECAPA-TDNN的说话人识别的pytorch实现

This repository contain the code of the main part of my master thesis degree at Politecnico di Torino in Data science & Engineering

Speaker verification of virtual assistants using ECAPA-TDNN model from SpeechBrain toolkit and transfer learning approach emphasizing on inter and intra comparision (text independent and dependent).

CryCeleb2023 experiments