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Arc B580 lost the kill test: batch-14 mask p50 103 ms vs Torch CPU 70 ms enhance / OpenVINO CPU 5 ms. Production stays on known-good CPU workers (tag known-good-cpu-2026-08-28). Hop stays 200 ms.
77 lines
2.3 KiB
Python
77 lines
2.3 KiB
Python
#!/usr/bin/env python3
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"""CPU baseline: thread-capped MetricGAN enhance_batch (project .venv)."""
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from __future__ import annotations
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import argparse
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import os
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import statistics
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import time
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os.environ.setdefault("OMP_NUM_THREADS", "1")
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os.environ.setdefault("MKL_NUM_THREADS", "1")
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os.environ.setdefault("OPENBLAS_NUM_THREADS", "1")
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import torch
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from speechbrain.inference.enhancement import SpectralMaskEnhancement
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SAVEDIR = "/root/.cache/speechbrain-enhancement"
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def pct(xs, p):
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xs = sorted(xs)
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i = int(round((p / 100.0) * (len(xs) - 1)))
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return xs[max(0, min(i, len(xs) - 1))]
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def main() -> None:
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ap = argparse.ArgumentParser()
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ap.add_argument("--batch", type=int, default=14)
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ap.add_argument("--samples", type=int, default=16000)
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ap.add_argument("--iters", type=int, default=30)
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ap.add_argument("--warmup", type=int, default=5)
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args = ap.parse_args()
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torch.set_num_threads(1)
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torch.set_num_interop_threads(1)
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m = SpectralMaskEnhancement.from_hparams(
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source="speechbrain/metricgan-plus-voicebank",
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savedir=SAVEDIR,
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run_opts={"device": "cpu"},
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)
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wav = torch.randn(args.batch, args.samples)
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lengths = torch.ones(args.batch)
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with torch.no_grad():
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for _ in range(args.warmup):
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m.enhance_batch(wav, lengths=lengths)
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times = []
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for _ in range(args.iters):
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t0 = time.perf_counter()
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m.enhance_batch(wav, lengths=lengths)
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times.append((time.perf_counter() - t0) * 1000.0)
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feats = m.compute_features(wav)
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net = m.mods.enhance_model
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for _ in range(args.warmup):
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net(feats, lengths=lengths)
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mask_times = []
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for _ in range(args.iters):
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t0 = time.perf_counter()
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net(feats, lengths=lengths)
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mask_times.append((time.perf_counter() - t0) * 1000.0)
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def report(name, xs):
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per = [x / args.batch for x in xs]
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print(
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f"{name} batch={args.batch} n={len(xs)} "
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f"p50={statistics.median(xs):.2f}ms p95={pct(xs,95):.2f}ms "
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f"p99={pct(xs,99):.2f}ms "
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f"per_cam_p50={statistics.median(per):.2f}ms "
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f"per_cam_p95={pct(per,95):.2f}ms"
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)
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report("cpu_enhance_batch", times)
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report("cpu_mask_only", mask_times)
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if __name__ == "__main__":
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main()
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