clovaai/voxceleb_trainer

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1175 stars · 290 forks observed · 2026-08-28

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

A PyTorch framework for training and evaluating speaker recognition and verification models on the VoxCeleb datasets. It implements multiple network architectures (ResNet, RawNet3, VGGVox) and metric learning loss functions (AM-Softmax, AAM-Softmax, Angular Prototypical, GE2E, Triplet), with pretrained models provided.

Use cases

• train a speaker verification model on VoxCeleb
• evaluate speaker recognition models with EER metrics
• compare metric learning loss functions for speaker embeddings
• download and run pretrained speaker recognition models
• prepare and augment VoxCeleb audio data for training
• extract speaker embeddings from raw waveforms

When to choose

• you need to train or benchmark speaker verification models on VoxCeleb
• you want to experiment with metric learning losses like AAM-Softmax or Angular Prototypical
• you need reproducible baselines with pretrained models and published EER scores

When to avoid

• you need production-ready speaker recognition as a service rather than a research training framework
• your task is general speech recognition or transcription rather than speaker identity
• you work outside Python/PyTorch environments

Facets

library · maturity maintenance

machine-learning deep-learning audio-processing speech-recognition benchmarking machine-learning deep-learning speech-processing artificial-intelligence python speaker-verification speaker-recognition metric-learning voxceleb pytorch research-code pretrained-models audio linux gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="clovaai/voxceleb_trainer")