What does this PR do?

This PR implements the ECAPA2 model architecture and its corresponding training recipe for VoxCeleb.

Key Additions:

• speechbrain/lobes/models/ECAPA2.py: Implementation of the ECAPA2 architecture and SubCenterClassifier.
• speechbrain/nnet/losses.py: Added JeffreysLoss for embedding regularization.
• VoxCeleb Recipe (recipes/VoxCeleb/SpeakerRec/):
  • Added train_ecapa2.yaml and verification_ecapa2.yaml.
  • Updated train_speaker_embeddings.py and speaker_verification_cosine.py to support the new model and pipeline requirements.
  • Handled backward compatibility natively; existing models (X-Vector, ResNet, ECAPA-TDNN) run without modification.

Testing & Validation:

• Added ECAPA2 testing vectors to tests/recipes/VoxCeleb.csv.
• Ran pytest tests to ensure existing functionality remains intact.
• Passed all doctests.
• Ran pre-commit run -a to verify strict code formatting and linting.

Performance:

Trained on VoxCeleb 1 + VoxCeleb 2:

• VoxCeleb1-O: 0.60% EER (with s-norm) / 0.70% EER (without s-norm)

Trained on VoxCeleb 2 only (tested without s-norm):

• VoxCeleb1-O: 0.79% EER
• VoxCeleb1-E: 1.00% EER
• VoxCeleb1-H: 1.76% EER

Fixes N/A

Breaking changes: None. Backward compatibility is maintained for existing VoxCeleb scripts.

PR review