1e7b7844a4
- 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>
87 lines
3.7 KiB
Python
87 lines
3.7 KiB
Python
#!/usr/bin/env python3
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"""Generate assets/office_ambience.wav — low, unintelligible office murmur.
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Mixed continuously into the outbound call audio (SoundfileMixer) so the caller hears a
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live-office room tone instead of dead digital silence while the agent thinks. Built from
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Kokoro voices speaking mundane phrases, overlapped and heavily low-pass filtered so no
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words are intelligible (nothing that could be mistaken for real conversation/PHI), plus
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a touch of room noise. Output: 60s mono 16 kHz PCM16 loop, quiet by design (the mixer
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volume scales it further).
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Run inside the pipecat venv: python scripts/make_ambience.py
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"""
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import os
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import numpy as np
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import soundfile as sf
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from kokoro_onnx import Kokoro
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HERE = os.path.dirname(os.path.abspath(__file__))
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PROJ = os.path.dirname(HERE)
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MODEL_DIR = os.environ.get("KOKORO_MODEL_DIR", "/home/tocmo0nlord/pipecat-run/models")
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OUT = os.path.join(PROJ, "assets", "office_ambience.wav")
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SR = 16000 # match PIPELINE_SAMPLE_RATE (transport output rate)
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DUR = 60.0 # loop length in seconds
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RNG = np.random.default_rng(42)
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PHRASES = [
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"Okay, so I'll move that over to Thursday and send the reminder out this afternoon.",
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"Could you pull the file for the three o'clock? I think it's already up front.",
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"Yes, we got the shipment in this morning, it's in the back on the second shelf.",
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"Let me transfer you over, one moment please, thank you so much for holding.",
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"The afternoon looks pretty full but the morning still has a couple of openings.",
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"I'll leave a note for the doctor and we'll follow up first thing tomorrow.",
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"They said the delivery should arrive before noon so we should be all set.",
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"Can you double check the calendar for next Tuesday? I think there's a conflict.",
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]
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VOICES = ["af_bella", "am_michael", "bf_emma", "am_adam", "af_nicole", "bm_george"]
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def lowpass(x, cutoff_hz, sr):
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"""Simple FFT brick-wall low-pass — fine for ambience shaping."""
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X = np.fft.rfft(x)
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freqs = np.fft.rfftfreq(len(x), 1 / sr)
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X[freqs > cutoff_hz] = 0
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return np.fft.irfft(X, n=len(x))
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def main():
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k = Kokoro(os.path.join(MODEL_DIR, "kokoro-v1.0.onnx"),
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os.path.join(MODEL_DIR, "voices-v1.0.bin"))
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bed = np.zeros(int(SR * DUR), dtype=np.float64)
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# ~18 murmured utterances scattered through the minute, different voices/speeds.
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for i in range(18):
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text = PHRASES[i % len(PHRASES)]
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voice = VOICES[i % len(VOICES)]
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samples, sr0 = k.create(text, voice=voice, speed=float(RNG.uniform(0.9, 1.1)))
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# Resample to SR by linear interpolation (quality is irrelevant post-filter).
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t_src = np.arange(len(samples)) / sr0
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t_dst = np.arange(int(len(samples) * SR / sr0)) / SR
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s = np.interp(t_dst, t_src, samples.astype(np.float64))
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s = lowpass(s, 900, SR) # muffle: through-the-wall murmur
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s *= RNG.uniform(0.25, 0.5) # each talker is quiet and uneven
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start = int(RNG.uniform(0, DUR - len(s) / SR) * SR)
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bed[start:start + len(s)] += s
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# Gentle room tone under the voices (shaped noise), so pauses aren't pure silence.
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noise = lowpass(RNG.standard_normal(len(bed)), 400, SR) * 0.02
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bed += noise
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# Loop seamlessly: crossfade the last second into the first.
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xf = SR
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ramp = np.linspace(0, 1, xf)
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bed[:xf] = bed[:xf] * ramp + bed[-xf:] * (1 - ramp)
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bed = bed[:-xf]
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# Normalize conservatively — this is a QUIET bed; mixer volume scales it down further.
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bed = bed / (np.abs(bed).max() + 1e-9) * 0.35
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os.makedirs(os.path.dirname(OUT), exist_ok=True)
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sf.write(OUT, bed.astype(np.float32), SR, subtype="PCM_16")
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print(f"wrote {OUT}: {len(bed)/SR:.1f}s @ {SR}Hz, peak {np.abs(bed).max():.2f}")
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if __name__ == "__main__":
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main()
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