feat: Add comprehensive Python testing infrastructure with Poetry

[llbbl]

Add Comprehensive Python Testing Infrastructure

Summary

This PR establishes a complete testing infrastructure for the SpecAugment project using Poetry as the package manager and pytest as the testing framework.

Changes Made

• Package Management: Set up Poetry configuration in pyproject.toml with all existing dependencies (numpy, tensorflow, librosa, matplotlib)
• Testing Framework: Configured pytest with comprehensive settings including:
  • Test discovery patterns
  • Coverage reporting (80% threshold)
  • Custom markers for unit, integration, and slow tests
  • HTML and XML coverage reports
• Testing Dependencies: Added development dependencies:
  • pytest (main testing framework)
  • pytest-cov (coverage reporting)
  • pytest-mock (mocking utilities)
• Directory Structure: Created organized test directories:
  • tests/ (root test directory)
  • tests/unit/ (unit tests)
  • tests/integration/ (integration tests)
  • tests/conftest.py (shared fixtures)
• Shared Fixtures: Comprehensive fixtures in conftest.py including:
  • Sample mel spectrograms and audio data
  • Temporary directories for testing
  • Mock configurations and parameters
  • Random seed management for reproducible tests
• Validation Tests: Added test_setup_validation.py to verify infrastructure works correctly
• Configuration: Updated .gitignore with testing-related entries and Claude settings

Running Tests

# Install dependencies
poetry install

# Run all tests with coverage
poetry run pytest

# Run specific test categories
poetry run pytest -m unit
poetry run pytest -m integration

# Run with verbose output
poetry run pytest -v

# Generate coverage report
poetry run pytest --cov-report=html

Coverage Configuration

• Threshold: 80% minimum coverage required
• Reports: HTML (htmlcov/), XML (coverage.xml), and terminal output
• Exclusions: Properly configured to exclude test files, demo code, and virtual environments

Notes

• Poetry is now the primary package manager for dependency management
• All existing functionality remains unchanged - only testing infrastructure added
• The tensorflow-addons dependency has a known compatibility issue on some platforms but doesn't affect core functionality
• Ready-to-use environment for developers to immediately start writing tests

Testing Infrastructure Validation

The setup includes comprehensive validation tests that verify:

• All dependencies are properly installed and importable
• Custom fixtures work correctly
• Coverage configuration is proper
• Project structure is as expected
• pytest markers function correctly

This infrastructure provides a solid foundation for maintaining code quality and enabling confident refactoring and feature development.