Relay: log upstream events + forward error envelopes to device for diagnostics
Plan 3 smoke surfaced 'session_ended reason=error' on every wake with no context. The relay now logs every upstream event type at INFO and forwards any upstream 'error' event to the device as 'error code=upstream_<code>' so it appears in the Pi's journal alongside the wake/session_ended pair. Also extends the client's wakeword.predict with per-frame RMS + score diagnostics and the bridge loop with a 5 s heartbeat for triage.
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@@ -65,11 +65,15 @@ public class RealtimeSession
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try
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{
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var enabled = _registry.EnabledFor(_cfg.EnabledTools).ToList();
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_logger.LogInformation(
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"relay opening session: model={Model} voice={Voice} tools=[{Tools}]",
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_cfg.Model, _cfg.Voice, string.Join(",", enabled.Select(t => t.Name)));
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await _upstream.SendJsonAsync(RealtimeEvents.SessionUpdate(_cfg, enabled), ct);
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var ack = await WaitForUpstreamTypeAsync("session.updated", ct);
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if (ack is null)
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{
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_logger.LogWarning("upstream closed before session.updated");
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_endReason = EndReason.Error;
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return;
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}
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@@ -274,7 +278,25 @@ public class RealtimeSession
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{
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var evt = await _upstream.ReceiveJsonAsync(ct);
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if (evt is null) return null;
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if ((string?)evt["type"] == type) return evt;
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var evtType = (string?)evt["type"];
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_logger.LogInformation("upstream evt={EvtType}", evtType);
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if (evtType == "error")
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{
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var err = evt["error"] as JsonObject;
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var code = (string?)err?["code"] ?? "unknown";
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var msg = (string?)err?["message"] ?? evt.ToJsonString();
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_logger.LogWarning("upstream error code={Code} message={Message}", code, msg);
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await _output.WriteEnvelopeAsync(new JsonObject
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{
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["type"] = "error",
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["code"] = "upstream_" + code,
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["message"] = msg,
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["fatal"] = false,
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}, ct);
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_endReason = EndReason.Error;
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return null;
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}
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if (evtType == type) return evt;
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}
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}
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}
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@@ -43,11 +43,28 @@ def _build_bridge_thread(
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stop_event: threading.Event,
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) -> threading.Thread:
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def run():
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frame_counter = 0
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recent_rms_max = 0.0
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while not stop_event.is_set():
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try:
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pcm16_bytes = mic_queue.get(timeout=0.25)
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except queue.Empty:
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continue
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frame_counter += 1
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raw = np.frombuffer(pcm16_bytes, dtype=np.int16)
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rms = float(np.sqrt(np.mean(raw.astype(np.float32) ** 2)))
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if rms > recent_rms_max:
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recent_rms_max = rms
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# Every ~5 s emit a heartbeat so we can see if the InputStream is
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# actually producing frames and what state we're routing to.
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if frame_counter % 62 == 0:
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_log.info(
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"bridge frames=%d state=%s wake_en=%s up_en=%s qsize=%d rms_max_5s=%.0f",
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frame_counter, sm.state.name,
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sm.wakeword_enabled, sm.uplink_enabled, mic_queue.qsize(),
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recent_rms_max,
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)
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recent_rms_max = 0.0
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if sm.wakeword_enabled:
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frame = np.frombuffer(pcm16_bytes, dtype=np.int16)
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if frame.size != FRAME_SAMPLES:
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+6
-1
@@ -39,9 +39,14 @@ class WakewordDetector:
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def predict(self, frame_16k_int16: np.ndarray) -> bool:
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scores = self._model.predict(frame_16k_int16)
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score = float(scores.get(WAKEWORD, 0.0))
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rms = float(np.sqrt(np.mean(frame_16k_int16.astype(np.float32) ** 2)))
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now = time.monotonic()
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# Diagnostic: log any non-trivial score so we can see what's getting
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# close to firing.
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if score >= 0.1:
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_log.info("score=%.3f rms=%.0f", score, rms)
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if score >= self._threshold and (now - self._last_fired) >= self._cooldown_s:
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self._last_fired = now
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_log.info("wakeword fired score=%.3f", score)
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_log.info("wakeword fired score=%.3f rms=%.0f", score, rms)
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return True
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return False
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