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>
59 lines
2.0 KiB
Python
59 lines
2.0 KiB
Python
#!/opt/aime-venv/bin/python
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"""Batched NPU transcriber A/B test: wav -> JSON words on stdout.
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large-v3-turbo decoder CPU (int8), encoder on RK3588 NPU (rknn fp16),
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faster-whisper BatchedInferencePipeline (Fabio-style batched+NPU).
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Usage: transcribe_batched.py <wav> [batch_size]
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"""
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import sys, json, time
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import numpy as np
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WAV = sys.argv[1]
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BATCH = int(sys.argv[2]) if len(sys.argv) > 2 else 8
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MODEL = "large-v3-turbo"
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ROOT = "/opt/callrec/models/whisper"
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RKNN = "/opt/callrec/models/turbo-encoder-fp16.rknn"
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sys.path.insert(0, "/opt/callrec")
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from faster_whisper import WhisperModel, BatchedInferencePipeline
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from faster_whisper.transcribe import get_ctranslate2_storage
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from inference.rknpu_whisper_encoder import RknnWhisperEncoder
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t0 = time.time()
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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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npu = RknnWhisperEncoder(RKNN, lib_path="/usr/lib/librknnrt.so")
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def npu_encode(features):
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feats = np.asarray(features, dtype=np.float32)
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out = npu.run(feats)
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return get_ctranslate2_storage(np.ascontiguousarray(out))
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model.encode = npu_encode
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batched = BatchedInferencePipeline(model=model)
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t_load = time.time() - t0
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t0 = time.time()
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segments, info = batched.transcribe(WAV, language="de", beam_size=1,
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batch_size=BATCH, word_timestamps=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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t_trans = time.time() - t0
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out = {
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"text": " ".join(texts).strip(),
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"n_words": len(words),
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"duration": round(float(info.duration or 0), 1),
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"load_s": round(t_load, 1),
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"transcribe_s": round(t_trans, 1),
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"rtf": round(t_trans / max(float(info.duration or 0.1), 0.1), 3),
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"batch": BATCH,
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}
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print(json.dumps(out, ensure_ascii=False))
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