# yeyupiaoling/VoiceprintRecognition-Pytorch

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

Repository: https://github.com/yeyupiaoling/VoiceprintRecognition-Pytorch
Canonical: https://ross.abutalabs.com/products/voiceprintrecognition-pytorch
Language: Python
License: Apache-2.0
License Family: permissive
Topics: pytorch, voice-recognition, arcface, speaker-recognition, ecapa-tdnn
Last push: 2025-12-17T13:07:59+00:00

## Health v2 (maintenance only)
Score: 58/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 57, release rhythm 35, longevity 100
- inputs: {"age_days": 1892, "days_push": 259, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1312, forks 171 (observed 2026-08-28T04:04:20.187664+00:00)

## What it is
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
- artifact type: framework
- maturity: active
- function: audio-processing, machine-learning, deep-learning, speech-recognition, sdk
- domain: speech-processing, machine-learning, deep-learning, artificial-intelligence, security
- platform: python, cross-platform, windows
- tags: speaker-recognition, voiceprint, speaker-verification, speaker-diarization, pytorch, ecapa-tdnn, res2net, cam++, arcface-loss, voice-embeddings, audio-augmentation, spectrogram, audio, linux, macos, gpu

## Member repositories
- yeyupiaoling/VoiceprintRecognition-Pytorch (main) score 58

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:20.187664+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:49:48.609094+00:00, confidence not recorded.
  - readme: https://github.com/yeyupiaoling/VoiceprintRecognition-Pytorch (fetched 2026-08-28T04:04:20.187664+00:00, sha 011c8211534a)
- Data as of 2026-08-30T08:39:29.467469+00:00.