feat: komplette Codebase vom Board ins Repo gespiegelt

app/ = /opt/callrec (Node-Backend, Vite-Frontend, Python-Inferenz, Audio-Skripte, rkllm_server.cpp), system/ = systemd-Units, WirePlumber/PipeWire-Drop-ins, sudoers. Modelle, Binaries, Aufnahmen und node_modules bleiben draussen (.gitignore). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-07-16 11:18:29 +02:00
parent 560622e125
commit f9e1eb82c4
61 changed files with 5546 additions and 0 deletions
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+#!/opt/aime-venv/bin/python
+"""CallRec CPU transcriber daemon (SHORT clips).
+
+Loads faster-whisper large-v3-turbo (CT2, CPU int8) ONCE and keeps it warm for
+the whole callrec lifetime. Used for SHORT recordings where per-call model load
+(~28-40s) dwarfs the transcription itself. CPU-ONLY — it never touches the NPU,
+so it coexists with a resident rkllm_server (Qwen-3B): short clips need NO
+NPU-encoder swap AND no model reload → a 10s clip drops from ~120s to ~20s.
+
+(The NPU turbo-encoder path stays in transcribe_cli.py for LONG clips, where the
+2.4x encoder speedup beats the swap overhead.)
+
+Protocol (line based, like the NPU daemon / spk_embed / rkllm_server):
+  stdin : one absolute wav path per line
+  stdout: "READY" once, then per request "OK <base64(json)>" or "ERR <base64(msg)>"
+"""
+import sys
+import json
+import base64
+import time
+import os
+
+sys.path.insert(0, "/opt/callrec")
+
+MODEL = "large-v3-turbo"
+ROOT = "/opt/callrec/models/whisper"
+BATCH = int(os.environ.get("WHISPER_BATCH", "8"))
+
+from faster_whisper import WhisperModel, BatchedInferencePipeline
+
+# CT2 model loaded ONCE (CPU), stays warm. No NPU encoder override -> ct2 runs
+# its own encoder on the CPU, so nothing here competes with rkllm on the NPU.
+_model = WhisperModel(MODEL, device="cpu", compute_type="int8",
+                      download_root=ROOT, cpu_threads=4)
+_engine = BatchedInferencePipeline(model=_model) if BATCH > 1 else _model
+
+
+def transcribe(wav):
+    t0 = time.time()
+    if BATCH > 1:
+        segments, info = _engine.transcribe(
+            wav, language="de", beam_size=1, batch_size=BATCH, word_timestamps=True)
+    else:
+        segments, info = _engine.transcribe(
+            wav, language="de", beam_size=1, word_timestamps=True, vad_filter=True)
+    words = []
+    texts = []
+    for seg in segments:
+        texts.append(seg.text.strip())
+        for w in (seg.words or []):
+            words.append({
+                "word": w.word.strip(),
+                "t_start_ms": int(round((w.start or 0) * 1000)),
+                "t_end_ms": int(round((w.end or 0) * 1000)),
+                "probability": round(w.probability, 3),
+            })
+    dur = float(info.duration or 0)
+    trans_s = time.time() - t0
+    return {
+        "text": " ".join(texts).strip(),
+        "words": words,
+        "duration": round(dur, 1),
+        "load_s": 0.0,
+        "transcribe_s": round(trans_s, 1),
+        "rtf": round(trans_s / max(dur, 0.1), 3),
+        "compute": "cpu-daemon",
+    }
+
+
+def main():
+    print("READY", flush=True)
+    for line in sys.stdin:
+        wav = line.strip()
+        if not wav:
+            continue
+        try:
+            out = transcribe(wav)
+            payload = base64.b64encode(
+                json.dumps(out, ensure_ascii=False).encode("utf-8")).decode("ascii")
+            print("OK " + payload, flush=True)
+        except Exception as e:  # noqa: BLE001
+            msg = base64.b64encode(str(e).encode("utf-8")).decode("ascii")
+            print("ERR " + msg, flush=True)
+
+
+if __name__ == "__main__":
+    main()