feat: remove BAR token; bump spec to v2.3; fix max_seq_len

Bar boundaries are now implicit — the detokenizer counts positions per bar
using TIME × SUB, and the generator gates EOS to bar boundaries only.
Removing the deterministic BAR token reduces vocab size from 85 to 84 and
lets the model focus on meaningful predictions.

- src/tokenizer.py: drop BAR from VOCAB (85→84); replace BAR-based
  detokenize_to_period with position-counting logic; add write_chord_file;
  fix _tokens_to_symbol for add9/m(add9) qualities
- tests/test_tokenizer.py: update vocab-size assertions to 84, structural
  token test, remove bar-count test, add test_no_bar_token_in_vocab
- docs/chord_format_spec.md: bump to v2.3; document BAR removal in §5.2,
  §5.3, §5.4, §5.5, §5.6, §6.2, and changelog
- CLAUDE.md: remove stale BAR reference, update vocab size to 84
- scripts/pretrain.py: raise max_seq_len 256→320 to cover regenerated
  McGill data (mean=83, max=283 tokens with BAR-free tokenizer)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

scripts/pretrain.py

@@ -56,9 +56,9 @@ TRAIN_CFG = TrainConfig(
     warmup_steps=200,
     seed=42,
     device="auto",
-    # Real McGill sequences are ≤ 195 tokens (p95 = 146, mean = 92).
-    # Using 256 instead of the 512 default cuts attention cost ~4x.
-    max_seq_len=256,
+    # Regenerated McGill sequences: mean=83, max=283 (BAR-free tokenizer).
+    # 320 covers the full distribution with headroom; still ~2.5x cheaper than 512.
+    max_seq_len=320,
 )