M6 + M0: hardening, deploy docs, PoC scripts

- Pipeline-down fallback: dialplan plays gogo-fallback after a failed
  AudioSocket (§15) + script to synthesize the Bosnian prompt via TTS
- Voice heartbeat file → /health/voice endpoint + live status in admin panel
- Retention job also purges stale call registrations; chat sessions metered;
  100%-usage super-admin alert email (GOGO_ADMIN_ALERT_EMAIL)
- .env.example, docs/DEPLOY.md (compose stack, GPU node, backups pg_dump,
  update procedure, security notes)
- §16 Definition of Done as an automated test: softphone-style call → fake
  partner availability → request pushed → webhook confirm → console SMS
- M0 PoC scripts: STT accuracy harness (CER/WER + spoken-digit check; dry-run
  verified with espeak-ng samples + faster-whisper small on CPU — phone number
  extracted exactly), TTS bake-off (Azure vs ElevenLabs, latency+cost), and
  end-to-end latency smoke test speaking real AudioSocket to the live pipeline

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

scripts/poc/stt_test.py

@@ -0,0 +1,112 @@
+"""M0 PoC #1 — STT accuracy on local speech samples (§14 Phase 0).
+
+Kill-the-risk test: can faster-whisper (language hint 'sr') reliably transcribe
+service names, personal names and PHONE NUMBERS SPOKEN ALOUD over phone-quality
+audio?
+
+Prepare a directory of WAV/MP3/OGG samples recorded by the founder + a
+manifest.csv with two columns:  filename,expected_text
+
+Run:
+    python scripts/poc/stt_test.py <samples_dir> [--model large-v3] [--device cuda]
+
+Outputs per-file transcript vs expected, character error rate (CER), word error
+rate (WER), digit accuracy (for phone numbers) and timing.
+GATE (§14): if phone numbers/names are unusable → stop and rethink providers.
+"""
+
+from __future__ import annotations
+
+import csv
+import re
+import sys
+import time
+from pathlib import Path
+
+
+def levenshtein(a: list, b: list) -> int:
+    prev = list(range(len(b) + 1))
+    for i, ca in enumerate(a, 1):
+        cur = [i]
+        for j, cb in enumerate(b, 1):
+            cur.append(min(prev[j] + 1, cur[j - 1] + 1, prev[j - 1] + (ca != cb)))
+        prev = cur
+    return prev[-1]
+
+
+def normalize(s: str) -> str:
+    return re.sub(r"[^\wšđčćž ]", "", s.lower()).strip()
+
+
+def digits(s: str) -> str:
+    words = {
+        "nula": "0", "jedan": "1", "dva": "2", "tri": "3", "četiri": "4",
+        "pet": "5", "šest": "6", "sedam": "7", "osam": "8", "devet": "9",
+    }
+    out = []
+    for token in normalize(s).split():
+        if token.isdigit():
+            out.append(token)
+        elif token in words:
+            out.append(words[token])
+    return "".join(out)
+
+
+def main() -> None:
+    if len(sys.argv) < 2:
+        print(__doc__)
+        sys.exit(1)
+    samples_dir = Path(sys.argv[1])
+    model_name = sys.argv[sys.argv.index("--model") + 1] if "--model" in sys.argv else "large-v3"
+    device = sys.argv[sys.argv.index("--device") + 1] if "--device" in sys.argv else "auto"
+
+    manifest = samples_dir / "manifest.csv"
+    if not manifest.exists():
+        print(f"missing {manifest} (columns: filename,expected_text)")
+        sys.exit(1)
+
+    from faster_whisper import WhisperModel
+
+    print(f"loading {model_name} on {device}…")
+    t0 = time.monotonic()
+    model = WhisperModel(model_name, device=device,
+                         compute_type="float16" if device == "cuda" else "int8")
+    print(f"model loaded in {time.monotonic() - t0:.1f}s\n")
+
+    rows = list(csv.DictReader(manifest.open()))
+    total_cer = total_wer = 0.0
+    digit_ok = digit_total = 0
+    for row in rows:
+        path = samples_dir / row["filename"]
+        expected = row["expected_text"]
+        t0 = time.monotonic()
+        segments, info = model.transcribe(str(path), language="sr", beam_size=5, vad_filter=True)
+        got = " ".join(s.text.strip() for s in segments).strip()
+        elapsed = time.monotonic() - t0
+
+        exp_n, got_n = normalize(expected), normalize(got)
+        cer = levenshtein(list(exp_n), list(got_n)) / max(1, len(exp_n))
+        wer = levenshtein(exp_n.split(), got_n.split()) / max(1, len(exp_n.split()))
+        total_cer += cer
+        total_wer += wer
+
+        exp_digits = digits(expected)
+        if exp_digits:
+            digit_total += 1
+            digit_ok += exp_digits == digits(got)
+
+        flag = "✓" if wer < 0.2 else ("~" if wer < 0.5 else "✗")
+        print(f"{flag} {row['filename']}  ({elapsed:.1f}s, audio {info.duration:.1f}s)")
+        print(f"   očekivano: {expected}")
+        print(f"   dobijeno : {got}\n")
+
+    n = max(1, len(rows))
+    print("=" * 60)
+    print(f"samples: {len(rows)}  avg CER: {total_cer / n:.1%}  avg WER: {total_wer / n:.1%}")
+    if digit_total:
+        print(f"phone-number samples exactly right: {digit_ok}/{digit_total}")
+    print("GATE: WER > 50% on names or wrong digits on most numbers → rethink STT (§14)")
+
+
+if __name__ == "__main__":
+    main()