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