ecapa-tdnn

Research and Production Oriented Speaker Verification, Recognition and Diarization Toolkit

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

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).

Wespeaker implementations for speaker recognition and verification: U3-xi,Uncertainty aware AAM Softmax, Score normalization, calibration, and unofficial RecXi (NeurIPS 2023) source code.

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

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

A multimodal SER project combining BERT and ECAPA-TDNN with cross-attention-based fusion on the IEMOCAP dataset.

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

Target speaker extraction — isolate any voice from a noisy recording using a short reference clip. Conv-TasNet separator and ECAPA-TDNN encoder, both trained from scratch.

📈 Model training, Pre-Training and Fine-Tuning workspace, scaling from a single RTX 3090 (24GB).

Multi-modal biometric authentication system combining face recognition (FaceNet), speaker verification (ECAPA-TDNN), and speech-to-text with liveness detection. Built with PyTorch. 99.5% test accuracy (on personal Data Set).

Full-stack AI speaker verification system using SpeechBrain's ECAPA-TDNN model to generate voice embeddings and verify speaker identity via cosine similarity. Built with FastAPI backend and React frontend.

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).

Production-ready AI voice biometric authentication platform using FastAPI, SpeechBrain ECAPA-TDNN, and Tensorflow. Generate offline Python SDKs (.whl) for secure speaker verification with zero cloud dependency.

🎙️ High-precision, privacy-first speech intelligence platform powered by Faster-Whisper Turbo & SpeechBrain ECAPA-TDNN on CUDA. Features acoustic- first │ VAD segmentation, mutual k-NN graph Laplacian diarization, and multi-sample biometric voiceprint enrollment.