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b2debad | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | import hashlib
import numpy as np
THRESHOLD = 0.60
def _seed_from_file(file_path):
with open(file_path, "rb") as f:
h = hashlib.md5(f.read()).hexdigest()
return int(h[:8], 16)
def generate_segment_scores(file_path, num_segments=12, force_stage2=False):
seed = _seed_from_file(file_path)
rng = np.random.default_rng(seed)
if force_stage2:
base = rng.uniform(0.62, 0.85, num_segments)
else:
base = rng.uniform(0.35, 0.58, num_segments)
variation = rng.normal(0, 0.03, num_segments)
scores = np.clip(base + variation, 0.0, 1.0)
return scores.tolist()
def summarize_segments(scores):
arr = np.array(scores)
mean_score = arr.mean()
below_thresh = (arr < THRESHOLD).sum()
ratio = below_thresh / len(arr)
return {
"mean": round(mean_score, 4),
"below_threshold_count": int(below_thresh),
"total_segments": len(arr),
"ratio_below_threshold": round(ratio, 4)
}
import librosa
import math
def get_num_segments(file_path, segment_duration=2):
y, sr = librosa.load(file_path, sr=16000)
duration = len(y) / sr
return max(1, math.ceil(duration / segment_duration))
def generate_histogram(scores):
bins = [0]*5
for s in scores:
if s < 0.2: bins[0]+=1
elif s < 0.4: bins[1]+=1
elif s < 0.6: bins[2]+=1
elif s < 0.8: bins[3]+=1
else: bins[4]+=1
return bins
def generate_mel_proxy(file_path, num_bands=8):
seed = _seed_from_file(file_path) + 42
rng = np.random.default_rng(seed)
energy = rng.uniform(0.2, 1.0, num_bands)
return np.round(energy, 3).tolist() |