feat: add --bars arg to control output length

generate_period() now accepts n_bars=N to stop after exactly N complete
bars. bars_completed is seeded from the prefix length so --bars counts
the full output, not just the generated tail.

scripts/generate.py exposes this as --bars (default: None = model decides).

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

scripts/generate.py

@@ -89,6 +89,8 @@ def main() -> None:
                     help="Nucleus sampling cutoff (default: 0.9).")
     ap.add_argument("--max-tokens", type=int, default=300, dest="max_tokens",
                     help="Hard cap on generated tokens (default: 300).")
+    ap.add_argument("--bars", type=int, default=None,
+                    help="Stop after this many complete bars (default: let the model decide).")
     ap.add_argument("--seed", type=int, default=None,
                     help="Random seed for reproducibility.")
     ap.add_argument("--tempo", type=int, default=90,
@@ -136,6 +138,7 @@ def main() -> None:
         temperature=args.temperature,
         top_p=args.top_p,
         max_tokens=args.max_tokens,
+        n_bars=args.bars,
         seed=args.seed,
     )
 

src/generate.py

@@ -190,6 +190,7 @@ def generate_period(
     temperature: float = 1.0,
     top_p: float = 0.9,
     max_tokens: int = 300,
+    n_bars: Optional[int] = None,
     seed: Optional[int] = None,
 ) -> ChordPeriod:
     """Generate one harmonic period autoregressively.
@@ -210,6 +211,8 @@ def generate_period(
         temperature: Sampling temperature (> 1 = more random).
         top_p:       Nucleus cutoff probability (0 < top_p <= 1).
         max_tokens:  Hard cap on generated tokens.
+        n_bars:      Stop after this many complete bars in the output.
+                     Counts bars from the prefix too. None = let the model decide.
         seed:        RNG seed for reproducibility.
 
     Returns:
@@ -245,10 +248,15 @@ def generate_period(
     positions_per_bar = _expected_positions(time, subdivision)
 
     pos_in_bar = 0
+    bars_completed = 0
     if prefix:
         encoded_prefix, n_prefix_positions = _encode_prefix(prefix, shift_to_canonical)
         ids.extend(encoded_prefix)
         pos_in_bar = n_prefix_positions % positions_per_bar
+        bars_completed = n_prefix_positions // positions_per_bar
+
+    if n_bars is not None and bars_completed >= n_bars:
+        log.warning("prefix already spans %d bars (>= requested %d)", bars_completed, n_bars)
 
     last_id = ids[-1]
     context_limit = model.max_seq_len - 1  # leave one slot so seq_len never hits max
@@ -268,6 +276,10 @@ def generate_period(
             # Advance position counter when a body position is completed
             if (_BASS_START <= token_id <= _BASS_END) or token_id in (_HOLD, _NC):
                 pos_in_bar = (pos_in_bar + 1) % positions_per_bar
+                if pos_in_bar == 0:
+                    bars_completed += 1
+                    if n_bars is not None and bars_completed >= n_bars:
+                        break
 
             if token_id == _EOS:
                 break

tests/test_generate.py

@@ -1,9 +1,31 @@
 """Tests for src/generate.py — prefix encoding and position tracking."""
 
 import pytest
+import torch
+import torch.nn as nn
 
-from src.generate import _encode_prefix, _HOLD, _NC, _ROOT_START, _BASS_START, _BASS_END
+from src.generate import _encode_prefix, _EOS, _HOLD, _NC, _ROOT_START, _BASS_START, _BASS_END, generate_period
-from src.tokenizer import TOKEN_TO_ID
+from src.tokenizer import TOKEN_TO_ID, VOCAB
+
+
+# ---------------------------------------------------------------------------
+# Mock model that outputs uniform logits (EOS suppressed so generation runs
+# until the bar-count limit or max_tokens).
+# ---------------------------------------------------------------------------
+
+class _UniformModel(nn.Module):
+    """Always returns zero logits except EOS=-1000, forcing non-EOS sampling."""
+    def __init__(self, vocab_size: int = len(VOCAB), max_seq_len: int = 512):
+        super().__init__()
+        self.max_seq_len = max_seq_len
+        self._vocab_size = vocab_size
+        self._dummy = nn.Parameter(torch.zeros(1))  # gives .parameters() something
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        b, s = x.shape
+        logits = torch.zeros(b, s, self._vocab_size, device=x.device)
+        logits[:, :, _EOS] = -1000.0
+        return logits
 
 
 def test_encode_prefix_chord_only():
@@ -56,3 +78,51 @@ def test_encode_prefix_position_count_with_holds():
     ids, n_pos = _encode_prefix(["Am", ".", "G", "."], shift=0)
     assert n_pos == 4
     assert len(ids) == 2 * 4 + 2 * 1   # 10 tokens
+
+
+# ---------------------------------------------------------------------------
+# n_bars tests
+# ---------------------------------------------------------------------------
+
+def test_generate_exact_bars():
+    model = _UniformModel()
+    period = generate_period(
+        model=model, mode="major", time="4/4", subdivision=4,
+        style="H1K0", function="verse", key="C_major",
+        n_bars=4, seed=0,
+    )
+    assert len(period.bars) == 4
+
+
+def test_generate_exact_bars_various():
+    model = _UniformModel()
+    for n in (1, 2, 8, 16):
+        period = generate_period(
+            model=model, mode="major", time="4/4", subdivision=4,
+            style="H1K0", function="verse", key="C_major",
+            n_bars=n, seed=0,
+        )
+        assert len(period.bars) == n, f"expected {n} bars, got {len(period.bars)}"
+
+
+def test_generate_bars_with_prefix():
+    # 4-position prefix = 1 bar; n_bars=4 → 3 more bars generated → 4 total
+    model = _UniformModel()
+    period = generate_period(
+        model=model, mode="major", time="4/4", subdivision=4,
+        style="H1K0", function="verse", key="C_major",
+        prefix=["C", ".", ".", "."],
+        n_bars=4, seed=0,
+    )
+    assert len(period.bars) == 4
+
+
+def test_generate_no_bars_arg_still_works():
+    # Without n_bars the model generates until EOS or max_tokens
+    model = _UniformModel()
+    period = generate_period(
+        model=model, mode="major", time="4/4", subdivision=4,
+        style="H1K0", function="verse", key="C_major",
+        max_tokens=64, seed=0,
+    )
+    assert len(period.bars) >= 1