speaker-recognition

FluidAudio

+60

Frontier CoreML audio models in your apps — text-to-speech, speech-to-text, voice activity detection, and speaker diarization. In Swift, powered by SOTA open source.

Speech

+41

A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)

pyannote-audio

+41

Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding

speechbrain

+14

A PyTorch-based Speech Toolkit

wespeaker

+4

Research and Production Oriented Speaker Verification, Recognition and Diarization Toolkit

VoiceprintRecognition-Pytorch

+3

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