fix(lab): make the speech fixture actually classify as speech

The speech fixture classified as LIVE_HUMAN on its first 3s window and drifted
to MUSIC for every window after. I had validated only the first window and
reported the fixture as verified, which overstated it: any lab result resting
on that fixture — the hold-slayer scenarios above all — was proving less than
it appeared to.

The cause was one modelling error, not a tuning problem. `_detect_tonality`
looks for an autocorrelation peak above 0.5 in the 50-1000 Hz lag range, and
each syllable used a *constant* f0, which is perfectly periodic there. That
scored is_tonal=True, handing the music score a free 0.3 that speech could not
outrun — and the decision requires speech_score to strictly exceed
music_score, so ties went to music.

Real voices glide and jitter, so the periodicity never locks. The fundamental
now follows a per-syllable pitch contour (rise or fall, plus ~2% cycle-to-cycle
jitter), with the frequency integrated to phase rather than multiplied by t —
`2*pi*f*t` is only a chirp when f is the instantaneous rate, which it is not
once f0 itself moves. is_tonal is now False in every window.

Two smaller fixes fell out of that:

- Aspiration noise is high-passed rather than broadband. Flat noise puts
  energy in every Goertzel bin, so the strongest DTMF row and column both
  clear the detector's `total_power * 0.1` threshold and each syllable reads
  as a keypress. A first-difference filter leaves the 697-1633 Hz bands
  comparatively empty. The level is set for margin — spectral flatness lands
  at ~0.46, mid-way through the 0.1-0.5 band, not on an edge.

- The music fixture gained two more harmonics and a recording-style noise
  floor. Windows straddling a chord change had a momentarily sparse spectrum
  and fell *below* the music score's 0.05 flatness floor, scoring as speech.

All three fixtures now classify correctly in 100% of windows (music 27/27,
speech 5/5, silence 2/2), and remain correct when the window is stepped by
half a window — a fixture that only works on aligned boundaries would still
be a trap in a live call, where the analysis window has no relationship to
where the audio began.

Confirmed on a real call through the lab: scenario 1003 now shows the whole
hold-slayer arc, speech -> sustained music -> speech, matching the dialplan.

tests/test_lab_fixtures.py guards this: it sweeps every window rather than
sampling the first, which is exactly what the original validation missed, and
checks the generator is byte-for-byte deterministic. It skips when the
fixtures have not been generated, since they are gitignored.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

tests/test_lab_fixtures.py

@@ -0,0 +1,109 @@
+"""
+Lab audio fixtures — the classifier must agree with what each one claims to be.
+
+These guard the *fixtures*, not the classifier. `tests/lab/sounds/generate.py`
+synthesises music/speech/silence that the Asterisk lab plays down a real call;
+if a fixture drifts into the wrong class, every lab result built on it is
+quietly meaningless — a hold-music scenario that never classifies as music
+proves nothing about the hold slayer.
+
+The first version of these fixtures passed on the opening 3s window and drifted
+to MUSIC after, which a single-window check would not have caught. Hence the
+sweep across every window.
+
+Skipped when the fixtures have not been generated: they are gitignored (~680K,
+reproducible from a fixed seed), so a fresh checkout has none until
+`python tests/lab/sounds/generate.py` runs.
+"""
+
+import subprocess
+import sys
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+from config import Settings
+from models.call import AudioClassification
+from services.audio_classifier import SAMPLE_RATE, AudioClassifier
+
+SOUNDS_DIR = Path(__file__).parent / "lab" / "sounds"
+GENERATOR = SOUNDS_DIR / "generate.py"
+
+# The lab writes 8 kHz .sln; the classifier works at 16 kHz.
+LAB_RATE = 8000
+WINDOW_SAMPLES = SAMPLE_RATE * 3  # classifier's 3s analysis window
+
+FIXTURES = [
+    ("lab-music.sln", AudioClassification.MUSIC),
+    ("lab-speech.sln", AudioClassification.LIVE_HUMAN),
+    ("lab-silence.sln", AudioClassification.SILENCE),
+]
+
+
+def _load_16k(path: Path) -> np.ndarray:
+    """Load an 8 kHz .sln and upsample to the classifier's 16 kHz."""
+    return np.repeat(np.fromfile(path, dtype="<i2"), SAMPLE_RATE // LAB_RATE)
+
+
+def _windows(samples: np.ndarray, step: int):
+    """Yield successive analysis windows; at least one, even for short files."""
+    end = max(1, len(samples) - WINDOW_SAMPLES)
+    for offset in range(0, end, step):
+        yield samples[offset : offset + WINDOW_SAMPLES].astype("<i2").tobytes()
+
+
+@pytest.fixture(scope="module")
+def classifier():
+    return AudioClassifier(settings=Settings().classifier)
+
+
+@pytest.mark.parametrize("filename,expected", FIXTURES)
+def test_fixture_classifies_correctly_in_every_window(filename, expected, classifier):
+    """Every window must classify correctly — not just the first.
+
+    Stepped at half the window length so windows overlap: a fixture that only
+    works on aligned boundaries would still be a trap in a live call, where
+    the window has no relationship to where the audio started.
+    """
+    path = SOUNDS_DIR / filename
+    if not path.exists():
+        pytest.skip(f"{filename} not generated — run {GENERATOR}")
+
+    samples = _load_16k(path)
+    results = [
+        classifier.classify_chunk(w).audio_type
+        for w in _windows(samples, step=WINDOW_SAMPLES // 2)
+    ]
+
+    wrong = [(i, r.value) for i, r in enumerate(results) if r is not expected]
+    assert not wrong, (
+        f"{filename} must classify as {expected.value} in all "
+        f"{len(results)} windows; wrong: {wrong}"
+    )
+
+
+def test_generator_is_deterministic(tmp_path):
+    """Same bytes on every run — the whole point of synthesising them.
+
+    Real hold music varies per call, so a classifier regression on the PSTN is
+    indistinguishable from noise. Fixed-seed audio makes the answer binary.
+    """
+    if not GENERATOR.exists():
+        pytest.skip("generator not present")
+
+    def run(target: Path) -> dict[str, bytes]:
+        subprocess.run(
+            [sys.executable, str(GENERATOR), str(target)],
+            check=True,
+            capture_output=True,
+        )
+        return {p.name: p.read_bytes() for p in sorted(target.glob("*.sln"))}
+
+    first = run(tmp_path / "a")
+    second = run(tmp_path / "b")
+
+    assert first, "generator produced no .sln files"
+    assert first.keys() == second.keys()
+    for name in first:
+        assert first[name] == second[name], f"{name} differs between runs"