v0.4.2

What's New

Checkpoint Architecture Persistence

• AudioClassifier and Backbone now store a config dict capturing all constructor arguments (backbone name, pooling, num_classes, etc.)
• Checkpoints (.pt files) now contain both state_dict and config — fully self-describing, no need to remember how the model was built.
• New from_checkpoint(path) classmethod reconstructs model architecture + weights in one call:

  model = AudioClassifier.from_checkpoint("checkpoint.pt")

Public API

• Added AVAILABLE_BACKBONES and AVAILABLE_POOLING runtime tuple constants for programmatic inspection of supported options.
• Added PoolingName type alias (Literal["gap", "simpool", "ep"]) alongside the existing BackboneName.
• Removed internal names (BACKBONES, POOLING, AudioClassifierConstructor, BackboneConstructor) from the public namespace — users interact only via AudioClassifier and Backbone.

Documentation

• Fixed AudioClassifier and Backbone rendering as "alias of..." in ReadTheDocs API reference.
• Added uv-based environment setup instructions in the Installation page.
• Updated Contributing page with development setup (uv sync) and test execution (uv run pytest -v) instructions.
• Updated README training and evaluation examples to use from_checkpoint; added PyPI publish badge.

Tests

• Updated test_evaluation_loop and test_inference to use AudioClassifier.from_checkpoint(...) instead of manually calling torch.load + load_state_dict.