yeyupiaoling/VoiceprintRecognition-Pytorch

Health v2 · maintenance only

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1312 stars · 171 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A PyTorch-based voiceprint recognition (speaker recognition) framework implementing models such as ECAPA-TDNN, ResNetSE, ERes2Net, and CAM++, along with multiple pooling layers, loss functions (including ArcFace), and audio preprocessing methods like MelSpectrogram, MFCC, and Fbank. It provides end-to-end tooling for dataset preparation, training, evaluation, and inference, supporting voiceprint comparison, speaker identification, and speaker diarization.

Use cases

• build a speaker recognition system in pytorch
• extract speaker embeddings from audio files
• check if two voice recordings are from the same person
• train a speaker verification model with arcface loss
• identify who is speaking in an audio clip
• perform speaker diarization to separate speakers in audio
• voice biometrics for user authentication
• compare voiceprints of two speakers

When to choose

• You need a full training and inference pipeline for speaker recognition rather than just a pretrained model
• You want to experiment with multiple state-of-the-art speaker models (ECAPA-TDNN, ERes2Net, CAM++, ResNetSE) under one framework
• You need audio data augmentation (speed, volume, noise, reverb, SpecAugment) for robust speaker models
• You work in Python with PyTorch and want voiceprint comparison, recognition, and diarization capabilities

When to avoid

• You need speech-to-text transcription rather than speaker identification - this project recognizes who speaks, not what is said
• You need a production-scale managed voice biometrics service rather than a self-run training framework
• You cannot use Python or PyTorch in your stack
• You need real-time low-latency streaming speaker verification without adaptation work

Facets

framework · maturity active

audio-processing machine-learning deep-learning speech-recognition sdk speech-processing machine-learning deep-learning artificial-intelligence security python cross-platform windows speaker-recognition voiceprint speaker-verification speaker-diarization pytorch ecapa-tdnn res2net cam++ arcface-loss voice-embeddings audio-augmentation spectrogram audio linux macos gpu

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markdown · JSON · MCP: product_card(name="yeyupiaoling/VoiceprintRecognition-Pytorch")