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>

app/inference/transcribe_cli.py

@@ -0,0 +1,66 @@
+#!/opt/aime-venv/bin/python
+"""CallRec NPU transcriber: wav -> JSON words on stdout.
+large-v3-turbo decoder on CPU (int8), encoder on RK3588 NPU (rknn fp16).
+Output matches what recorder.js consumes: {"text","words":[{word,t_start_ms,t_end_ms,probability}]}.
+Usage: transcribe_cli.py <wav> [--cpu]   (--cpu = no NPU, plain CPU encoder for A/B)
+"""
+import sys, json, time
+import numpy as np
+
+WAV = sys.argv[1]
+USE_NPU = "--cpu" not in sys.argv[2:]
+import os
+BATCH = int(os.environ.get("WHISPER_BATCH", "8"))
+MODEL = "large-v3-turbo"
+ROOT = "/opt/callrec/models/whisper"
+RKNN = "/opt/callrec/models/turbo-encoder-fp16.rknn"
+sys.path.insert(0, "/opt/callrec")
+
+from faster_whisper import WhisperModel, BatchedInferencePipeline
+
+t0 = time.time()
+model = WhisperModel(MODEL, device="cpu", compute_type="int8",
+                     download_root=ROOT, cpu_threads=4)
+
+if USE_NPU:
+    from faster_whisper.transcribe import get_ctranslate2_storage
+    from inference.rknpu_whisper_encoder import RknnWhisperEncoder
+    npu = RknnWhisperEncoder(RKNN, lib_path="/usr/lib/librknnrt.so")
+    def npu_encode(features):
+        feats = np.asarray(features, dtype=np.float32)
+        out = npu.run(feats)
+        return get_ctranslate2_storage(np.ascontiguousarray(out))
+    model.encode = npu_encode
+
+engine = BatchedInferencePipeline(model=model) if BATCH > 1 else model
+t_load = time.time() - t0
+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),
+        })
+t_trans = time.time() - t0
+out = {
+    "text": " ".join(texts).strip(),
+    "words": words,
+    "duration": round(float(info.duration or 0), 1),
+    "load_s": round(t_load, 1),
+    "transcribe_s": round(t_trans, 1),
+    "rtf": round(t_trans / max(float(info.duration or 0.1), 0.1), 3),
+    "compute": "npu" if USE_NPU else "cpu",
+    "batch": BATCH,
+}
+print(json.dumps(out, ensure_ascii=False))