Add Conformer audio encoder as default architecture

[asfilion]

Summary

• Add Conformer encoder (Gulati et al., 2020) combining self-attention with depthwise convolutions to capture both global and local temporal audio patterns
• Three new classes: ConformerFeedForward, ConformerConvModule, ConformerBlock in transformer.py
• AudioEncoder branches on use_conformer config flag (default True), transparent to decoder
• Tune medium configs: LR 1e-4 → 3e-5, warmup 500 → 1000 (fixes val loss divergence with larger models)
• New configs/medium_conformer.json (9.4M params, fits 8GB VRAM at batch_size=8)
• 9 new tests in TestConformer class (178 total tests passing)
• Updated all project docs (CLAUDE.md, README.md, RESEARCH.md, CODEBASE_REFERENCE.md)

Training results (medium conformer, 7 epochs so far)

Epoch	Val Loss	Val Acc
1	3.277	37.8%
4	2.530	52.1%
7	2.396	53.6%

Stable training with no divergence. On track to surpass the small model baseline (60.6% at epoch 16).

Test plan

• All 178 tests pass (python -m pytest tests/ -v)
• Conformer output shape, padding, sinusoidal PE, backward pass, onset features, conv module, param count tested
• Existing encoder/decoder tests unchanged and passing
• Medium conformer config instantiates and reports 9,422,371 params
• Training completes and surpasses baseline accuracy

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