feat(voice): improve Romanian STT — hallucination gate + finetuned model

Gemma 4 cloud audio was infeasible (31b-cloud has no audio; E4B broken
upstream, no deploy host), so improve faster-whisper instead.

- Pin temperature=0.0 to disable the fallback ladder that re-decoded unclear
  audio up to 6x (source of the 16-24s latency outliers); reject hallucinated
  segments via avg_logprob/compression_ratio in the new pure _filter_segments.
- Adopt mikr/whisper-small-ro-cv11 (CT2 int8) via configurable voice.stt_model:
  spike showed WER 24%->10%, numbers fixed at source, +0.33s p50 (in budget).
- Add tools/voice_stt_mine.py (log mining) + tools/voice_stt_spike.py (model
  eval with diacritic scoring) + tests for the gate and miner.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

tools/voice_stt_spike.py

@@ -0,0 +1,180 @@
+#!/usr/bin/env python3
+"""STT model spike — compare faster-whisper models on Romanian diacritic accuracy.
+
+Answers the autoplan D1 question: does a Romanian-finetuned whisper-small beat the
+generic `small` on diacritics WITHOUT regressing latency? Synthesizes clean RO
+audio via Supertonic (ground-truth text known, with diacritics), runs each model,
+and scores diacritic preservation + word error rate + latency.
+
+This is an evaluation harness, not production code. Synthetic TTS audio is a clean
+probe for diacritic behaviour specifically — it is NOT a proxy for real-mic acoustic
+robustness, so weight latency + diacritics, not absolute WER.
+
+Usage:
+    python3 tools/voice_stt_spike.py --models small,/home/.../whisper-small-ro-cv11-int8
+    python3 tools/voice_stt_spike.py --models small,<path> --threads 4 --trials 2
+"""
+from __future__ import annotations
+
+import argparse
+import statistics
+import sys
+import tempfile
+import time
+import unicodedata
+from pathlib import Path
+
+import httpx
+
+SUPERTONIC_URL = "http://127.0.0.1:7788"
+
+# Ground truth with correct Romanian diacritics (mirrors tools/voice_bench.py).
+UTTERANCES_RO: list[tuple[str, str]] = [
+    ("short", "Salut, ce mai faci?"),
+    ("conversational", "Stai puțin să mă gândesc la asta."),
+    ("medium", "Am verificat în calendar și avem ședință cu echipa la trei după-amiază."),
+    ("numbers", "Costul total este o sută douăzeci și trei de lei și cincizeci de bani."),
+    ("question", "Marius, vrei să-ți pun pe agenda de mâine să suni la NOAA?"),
+    ("longer", "Vreau să-mi reamintești diseară să verific dacă scriptul de backup a rulat corect."),
+]
+
+_DIACRITICS = set("ăâîșțĂÂÎȘȚ")
+
+
+def _strip_punct_lower(text: str) -> list[str]:
+    out = []
+    for raw in text.split():
+        w = "".join(c for c in raw if c.isalnum() or c in _DIACRITICS)
+        if w:
+            out.append(w.lower())
+    return out
+
+
+def _deaccent(s: str) -> str:
+    # Map RO diacritics to base letters for "ignoring diacritics" comparison.
+    table = str.maketrans("ăâîșțĂÂÎȘȚ", "aaistAAIST")
+    s = s.translate(table)
+    return "".join(c for c in unicodedata.normalize("NFD", s)
+                    if unicodedata.category(c) != "Mn")
+
+
+def wer(ref: list[str], hyp: list[str]) -> float:
+    """Word error rate via Levenshtein on token lists."""
+    n, m = len(ref), len(hyp)
+    if n == 0:
+        return 0.0 if m == 0 else 1.0
+    dp = list(range(m + 1))
+    for i in range(1, n + 1):
+        prev = dp[0]
+        dp[0] = i
+        for j in range(1, m + 1):
+            cur = dp[j]
+            cost = 0 if ref[i - 1] == hyp[j - 1] else 1
+            dp[j] = min(dp[j] + 1, dp[j - 1] + 1, prev + cost)
+            prev = cur
+    return dp[m] / n
+
+
+def diacritic_score(ref: str, hyp: str) -> tuple[int, int]:
+    """For each ground-truth word that carries a diacritic, did the hypothesis
+    contain that exact diacritized word? Returns (correct, total)."""
+    ref_words = _strip_punct_lower(ref)
+    hyp_words = set(_strip_punct_lower(hyp))
+    hyp_deaccent = {_deaccent(w) for w in hyp_words}
+    correct = total = 0
+    for w in ref_words:
+        if set(w) & _DIACRITICS:
+            total += 1
+            if w in hyp_words:
+                correct += 1
+    return correct, total
+
+
+def synthesize(text: str, out_path: Path) -> float:
+    import wave
+    r = httpx.post(f"{SUPERTONIC_URL}/v1/audio/speech",
+                   json={"model": "supertonic-3", "input": text, "voice": "M2",
+                         "response_format": "wav", "lang": "ro"}, timeout=60.0)
+    r.raise_for_status()
+    out_path.write_bytes(r.content)
+    with wave.open(str(out_path), "rb") as wf:
+        return wf.getnframes() / float(wf.getframerate())
+
+
+def run_model(model_ref: str, wavs: list[tuple[str, str, str, float]],
+              threads: int, trials: int) -> dict:
+    from faster_whisper import WhisperModel
+    t0 = time.perf_counter()
+    model = WhisperModel(model_ref, device="cpu", compute_type="int8", cpu_threads=threads)
+    load_s = time.perf_counter() - t0
+    rows, lats = [], []
+    dia_c = dia_t = 0
+    wers = []
+    for name, ref_text, wav_path, _dur in wavs:
+        best_text = ""
+        for trial in range(trials):
+            t1 = time.perf_counter()
+            segments, _ = model.transcribe(wav_path, language="ro", beam_size=5,
+                                           temperature=0.0, condition_on_previous_text=False)
+            text = " ".join(s.text.strip() for s in segments).strip()
+            lats.append(time.perf_counter() - t1)
+            if trial == 0:
+                best_text = text
+        c, tt = diacritic_score(ref_text, best_text)
+        dia_c += c
+        dia_t += tt
+        w = wer(_strip_punct_lower(ref_text), _strip_punct_lower(best_text))
+        wers.append(w)
+        rows.append((name, ref_text, best_text, c, tt, w))
+    return {
+        "model": model_ref, "load_s": load_s, "rows": rows,
+        "p50": statistics.median(lats), "p95": sorted(lats)[max(0, int(0.95*(len(lats)-1)))],
+        "dia_correct": dia_c, "dia_total": dia_t,
+        "wer": statistics.mean(wers) if wers else 1.0,
+    }
+
+
+def main(argv=None) -> int:
+    ap = argparse.ArgumentParser(description=__doc__,
+                                 formatter_class=argparse.RawDescriptionHelpFormatter)
+    ap.add_argument("--models", required=True, help="CSV of model names or CT2 dirs")
+    ap.add_argument("--threads", type=int, default=4)
+    ap.add_argument("--trials", type=int, default=2)
+    args = ap.parse_args(argv)
+
+    work = Path(tempfile.mkdtemp(prefix="stt_spike_"))
+    print(f"[spike] synth dir {work}", flush=True)
+    wavs = []
+    for name, text in UTTERANCES_RO:
+        p = work / f"{name}.wav"
+        dur = synthesize(text, p)
+        wavs.append((name, text, str(p), dur))
+        print(f"[spike] TTS {name}: {dur:.2f}s", flush=True)
+
+    results = []
+    for ref in args.models.split(","):
+        ref = ref.strip()
+        if not ref:
+            continue
+        print(f"[spike] running {ref} (threads={args.threads})…", flush=True)
+        results.append(run_model(ref, wavs, args.threads, args.trials))
+
+    print("\n" + "=" * 72)
+    print(f"{'model':<42} {'p50':>6} {'p95':>6} {'WER':>6} {'diacr':>8}")
+    print("-" * 72)
+    for r in results:
+        dia = f"{r['dia_correct']}/{r['dia_total']}"
+        label = Path(r["model"]).name if "/" in r["model"] else r["model"]
+        print(f"{label:<42} {r['p50']:>5.2f}s {r['p95']:>5.2f}s {r['wer']*100:>5.1f}% {dia:>8}")
+    print("=" * 72)
+    for r in results:
+        label = Path(r["model"]).name if "/" in r["model"] else r["model"]
+        print(f"\n### {label}")
+        for name, ref_text, hyp, c, tt, w in r["rows"]:
+            print(f"  [{name}] ref: {ref_text}")
+            print(f"  [{name}] hyp: {hyp}   (diacr {c}/{tt}, wer {w*100:.0f}%)")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())