Ops hardening: lead re-import, SMS health-watch, ambient office murmur

- scripts/reimport_leads.py drains appointment_requests.jsonl into Odoo
  (fallback leads were captured but invisible to staff forever); server.py
  now warns at startup when leads are pending.
- scripts/healthwatch.sh (cron, every minute): after 3 consecutive /health
  failures sends a Twilio SMS to ALERT_SMS_TO (30-min cooldown) + a recovery
  SMS. systemd handles restarts; this makes a human aware when that isn't
  enough. Tested end-to-end (DOWN + RECOVERED SMS delivered).
- Ambient office murmur (assets/office_ambience.wav, generated by
  scripts/make_ambience.py from low-passed unintelligible Kokoro babble +
  room tone) mixed continuously into outbound audio via pipecat
  SoundfileMixer, so callers hear a live office instead of dead silence
  while the agent thinks. AMBIENT_VOLUME/AMBIENT_ENABLED to tune/disable.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

scripts/make_ambience.py

@@ -0,0 +1,86 @@
+#!/usr/bin/env python3
+"""Generate assets/office_ambience.wav — low, unintelligible office murmur.
+
+Mixed continuously into the outbound call audio (SoundfileMixer) so the caller hears a
+live-office room tone instead of dead digital silence while the agent thinks. Built from
+Kokoro voices speaking mundane phrases, overlapped and heavily low-pass filtered so no
+words are intelligible (nothing that could be mistaken for real conversation/PHI), plus
+a touch of room noise. Output: 60s mono 16 kHz PCM16 loop, quiet by design (the mixer
+volume scales it further).
+
+Run inside the pipecat venv: python scripts/make_ambience.py
+"""
+import os
+
+import numpy as np
+import soundfile as sf
+from kokoro_onnx import Kokoro
+
+HERE = os.path.dirname(os.path.abspath(__file__))
+PROJ = os.path.dirname(HERE)
+MODEL_DIR = os.environ.get("KOKORO_MODEL_DIR", "/home/tocmo0nlord/pipecat-run/models")
+OUT = os.path.join(PROJ, "assets", "office_ambience.wav")
+
+SR = 16000          # match PIPELINE_SAMPLE_RATE (transport output rate)
+DUR = 60.0          # loop length in seconds
+RNG = np.random.default_rng(42)
+
+PHRASES = [
+    "Okay, so I'll move that over to Thursday and send the reminder out this afternoon.",
+    "Could you pull the file for the three o'clock? I think it's already up front.",
+    "Yes, we got the shipment in this morning, it's in the back on the second shelf.",
+    "Let me transfer you over, one moment please, thank you so much for holding.",
+    "The afternoon looks pretty full but the morning still has a couple of openings.",
+    "I'll leave a note for the doctor and we'll follow up first thing tomorrow.",
+    "They said the delivery should arrive before noon so we should be all set.",
+    "Can you double check the calendar for next Tuesday? I think there's a conflict.",
+]
+VOICES = ["af_bella", "am_michael", "bf_emma", "am_adam", "af_nicole", "bm_george"]
+
+
+def lowpass(x, cutoff_hz, sr):
+    """Simple FFT brick-wall low-pass — fine for ambience shaping."""
+    X = np.fft.rfft(x)
+    freqs = np.fft.rfftfreq(len(x), 1 / sr)
+    X[freqs > cutoff_hz] = 0
+    return np.fft.irfft(X, n=len(x))
+
+
+def main():
+    k = Kokoro(os.path.join(MODEL_DIR, "kokoro-v1.0.onnx"),
+               os.path.join(MODEL_DIR, "voices-v1.0.bin"))
+    bed = np.zeros(int(SR * DUR), dtype=np.float64)
+
+    # ~18 murmured utterances scattered through the minute, different voices/speeds.
+    for i in range(18):
+        text = PHRASES[i % len(PHRASES)]
+        voice = VOICES[i % len(VOICES)]
+        samples, sr0 = k.create(text, voice=voice, speed=float(RNG.uniform(0.9, 1.1)))
+        # Resample to SR by linear interpolation (quality is irrelevant post-filter).
+        t_src = np.arange(len(samples)) / sr0
+        t_dst = np.arange(int(len(samples) * SR / sr0)) / SR
+        s = np.interp(t_dst, t_src, samples.astype(np.float64))
+        s = lowpass(s, 900, SR)                # muffle: through-the-wall murmur
+        s *= RNG.uniform(0.25, 0.5)            # each talker is quiet and uneven
+        start = int(RNG.uniform(0, DUR - len(s) / SR) * SR)
+        bed[start:start + len(s)] += s
+
+    # Gentle room tone under the voices (shaped noise), so pauses aren't pure silence.
+    noise = lowpass(RNG.standard_normal(len(bed)), 400, SR) * 0.02
+    bed += noise
+
+    # Loop seamlessly: crossfade the last second into the first.
+    xf = SR
+    ramp = np.linspace(0, 1, xf)
+    bed[:xf] = bed[:xf] * ramp + bed[-xf:] * (1 - ramp)
+    bed = bed[:-xf]
+
+    # Normalize conservatively — this is a QUIET bed; mixer volume scales it down further.
+    bed = bed / (np.abs(bed).max() + 1e-9) * 0.35
+    os.makedirs(os.path.dirname(OUT), exist_ok=True)
+    sf.write(OUT, bed.astype(np.float32), SR, subtype="PCM_16")
+    print(f"wrote {OUT}: {len(bed)/SR:.1f}s @ {SR}Hz, peak {np.abs(bed).max():.2f}")
+
+
+if __name__ == "__main__":
+    main()