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import os, argparse, numpy as np
from glob import glob
try: from app.inference_wav2vec import Detector
except ImportError: from inference_wav2vec import Detector
def collect(root):
H = sorted(glob(os.path.join(root, "human", "*.wav")))
A = sorted(glob(os.path.join(root, "ai", "*.wav")))
return H, A
def main(a):
det = Detector(weights_path=a.weights)
H, A = collect(a.root)
ys, ps_mic, ps_up = [], [], []
for p in H:
ys.append(0)
ps_mic.append(det.predict_proba(p, source_hint="microphone")["ai"])
ps_up.append(det.predict_proba(p, source_hint="upload")["ai"])
for p in A:
ys.append(1)
ps_mic.append(det.predict_proba(p, source_hint="microphone")["ai"])
ps_up.append(det.predict_proba(p, source_hint="upload")["ai"])
ys = np.array(ys); ps_mic = np.array(ps_mic); ps_up = np.array(ps_up)
def sweep(ps):
best = (0.5, -1, 1e9)
for thr in np.linspace(0.5, 0.8, 61):
pred = (ps >= thr).astype(int)
tp = ((pred==1)&(ys==1)).sum(); fp = ((pred==1)&(ys==0)).sum()
fn = ((pred==0)&(ys==1)).sum()
prec = tp / max(tp+fp,1); rec = tp / max(tp+fn,1)
f1 = 2*prec*rec / max(prec+rec,1e-9)
if (f1 > best[1]) or (f1==best[1] and fp < best[2]):
best = (float(thr), float(f1), int(fp))
return best
mt, mf1, mfp = sweep(ps_mic)
ut, uf1, ufp = sweep(ps_up)
print(f"MIC threshold ~ {mt:.2f} (F1={mf1:.3f}, human_as_ai={mfp})")
print(f"UPLOAD threshold ~ {ut:.2f} (F1={uf1:.3f}, human_as_ai={ufp})")
print("Set:")
print(f' $env:DETECTOR_MIC_THRESHOLD="{mt:.2f}"')
print(f' $env:DETECTOR_UPLOAD_THRESHOLD="{ut:.2f}"')
if __name__ == "__main__":
ap = argparse.ArgumentParser()
ap.add_argument("--root", required=True, help="folder with human/ and ai/")
ap.add_argument("--weights", default="app/models/weights/wav2vec2_classifier.pth")
main(ap.parse_args()) |