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c413e6f | 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 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 | #!/usr/bin/env python3
"""Statistics over all voice profiles, all 40 emotions and all 57 VoiceNet dimensions.
python build_stats.py --src <RELEASE_ROOT>/index --dst <RELEASE_ROOT>/stats [--workers 32]
Writes three CSVs, each split by `origin`:
stats_profiles.csv one row per (voice, origin) -- counts, hours, duration distribution
stats_emotions.csv one row per (emotion, origin) -- intensity distribution + argmax counts
stats_dimensions.csv one row per (vn dim, origin) -- reg distribution + bucket histogram
Percentiles are computed from fixed-width histograms (4096 bins over a per-column fixed range),
which is exact in mean/count/min/max and accurate to one bin width in the percentiles -- about
0.002 on the 0-7 emotion scale and 0.003 on the VoiceNet reg scale. This is what lets 26 M rows be
summarised in bounded memory in a single streaming pass. The bin width is reported in each file.
"""
import argparse, os, glob, csv, math
import numpy as np, pyarrow.parquet as pq
from concurrent.futures import ProcessPoolExecutor
EMO = ["Affection","Amusement","Anger","Astonishment_Surprise","Awe","Bitterness","Concentration",
"Confusion","Contemplation","Contempt","Contentment","Disappointment","Disgust","Distress","Doubt",
"Elation","Embarrassment","Emotional_Numbness","Fatigue_Exhaustion","Fear","Helplessness",
"Hope_Enthusiasm_Optimism","Impatience_and_Irritability","Infatuation","Interest",
"Intoxication_Altered_States_of_Consciousness","Longing","Malevolence_Malice","Pain",
"Pleasure_Ecstasy","Pride","Relief","Sadness","Sexual_Lust","Shame","Sourness","Teasing",
"Thankfulness_Gratitude","Triumph","Jealousy_and_Envy"]
VN = ["AGEV","AROU","ARSH","ATCK","BKGN","BRGT","CHNK","CLRT","COGL","DARC","DFLU","EMPH","ESTH",
"EXPL","FOCS","FULL","GEND","HARM","METL","RANG","RCQL","REGS","RESP","ROUG","R_CHST","R_HEAD",
"R_MASK","R_MIXD","R_NASL","R_ORAL","R_THRT","SMTH","STNC","STRU","S_ASMR","S_AUTH","S_CART",
"S_CASU","S_CONV","S_DRAM","S_FORM","S_MONO","S_NARR","S_NEWS","S_PLAY","S_RANT","S_STRY",
"S_TECH","S_WHIS","TEMP","TENS","VALN","VALS","VFLX","VOLT","VULN","WARM"]
NB = 4096
RANGES = {"emo": (0.0, 8.0), "vn": (-6.0, 10.0), "dur": (0.0, 120.0)}
class Hist:
__slots__ = ("lo","hi","h","n","s","mn","mx")
def __init__(self, lo, hi):
self.lo, self.hi = lo, hi
self.h = np.zeros(NB, np.int64); self.n = 0; self.s = 0.0
self.mn = math.inf; self.mx = -math.inf
def add(self, x):
x = x[np.isfinite(x)]
if x.size == 0: return
self.n += x.size; self.s += float(x.sum())
self.mn = min(self.mn, float(x.min())); self.mx = max(self.mx, float(x.max()))
i = np.clip(((x - self.lo) / (self.hi - self.lo) * NB).astype(np.int64), 0, NB - 1)
self.h += np.bincount(i, minlength=NB)
def merge(self, o):
self.h += o.h; self.n += o.n; self.s += o.s
self.mn = min(self.mn, o.mn); self.mx = max(self.mx, o.mx); return self
def pct(self, qs):
if self.n == 0: return [None] * len(qs)
c = np.cumsum(self.h); out = []
for q in qs:
k = np.searchsorted(c, q / 100.0 * self.n)
out.append(self.lo + (min(k, NB - 1) + 0.5) * (self.hi - self.lo) / NB)
return out
def row(self):
p = self.pct([1, 10, 25, 50, 75, 90, 99])
return dict(count=self.n, mean=(self.s / self.n if self.n else None),
min=(self.mn if self.n else None), max=(self.mx if self.n else None),
p1=p[0], p10=p[1], p25=p[2], median=p[3], p75=p[4], p90=p[5], p99=p[6])
def blank():
return dict(emo={e: Hist(*RANGES["emo"]) for e in EMO},
vn={d: Hist(*RANGES["vn"]) for d in VN},
bucket={d: np.zeros(8, np.int64) for d in VN},
top=({e: 0 for e in EMO}),
voice={})
def scan(f):
cols = (["voice","origin","dur_s","top_emotion","n_bursts","genuineness_0_6","blend_0_10"]
+ [f"emo_{e}" for e in EMO] + [f"vn_{d}_reg" for d in VN] + [f"vn_{d}_bucket" for d in VN])
t = pq.read_table(f, columns=[c for c in cols])
acc = {}
origins = np.array(t["origin"].to_pylist())
for org in np.unique(origins):
m = origins == org
A = acc.setdefault(str(org), blank())
for e in EMO:
A["emo"][e].add(np.asarray(t[f"emo_{e}"].to_numpy(zero_copy_only=False))[m])
for d in VN:
A["vn"][d].add(np.asarray(t[f"vn_{d}_reg"].to_numpy(zero_copy_only=False))[m])
b = np.asarray(t[f"vn_{d}_bucket"].to_numpy(zero_copy_only=False))[m]
b = b[np.isfinite(b)].astype(np.int64)
A["bucket"][d] += np.bincount(np.clip(b, 0, 7), minlength=8)
for te in np.asarray(t["top_emotion"].to_pylist())[m]:
if te in A["top"]: A["top"][te] += 1
vs = np.asarray(t["voice"].to_pylist())[m]
du = np.asarray(t["dur_s"].to_numpy(zero_copy_only=False))[m]
gn = np.asarray(t["genuineness_0_6"].to_numpy(zero_copy_only=False))[m]
bl = np.asarray(t["blend_0_10"].to_numpy(zero_copy_only=False))[m]
nb = np.asarray(t["n_bursts"].to_numpy(zero_copy_only=False))[m]
for v in np.unique(vs):
mm = vs == v
V = A["voice"].setdefault(str(v), dict(d=Hist(*RANGES["dur"]), n=0, sg=0.0, sb=0.0, nb=0))
V["d"].add(du[mm]); V["n"] += int(mm.sum())
V["sg"] += float(np.nansum(gn[mm])); V["sb"] += float(np.nansum(bl[mm]))
V["nb"] += int(np.nansum(nb[mm]))
return acc
def merge(A, B):
for org, b in B.items():
a = A.setdefault(org, blank())
for e in EMO: a["emo"][e].merge(b["emo"][e])
for d in VN:
a["vn"][d].merge(b["vn"][d]); a["bucket"][d] += b["bucket"][d]
for e, v in b["top"].items(): a["top"][e] += v
for v, s in b["voice"].items():
t = a["voice"].setdefault(v, dict(d=Hist(*RANGES["dur"]), n=0, sg=0.0, sb=0.0, nb=0))
t["d"].merge(s["d"]); t["n"] += s["n"]; t["sg"] += s["sg"]; t["sb"] += s["sb"]; t["nb"] += s["nb"]
return A
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--src", required=True); ap.add_argument("--dst", required=True)
ap.add_argument("--workers", type=int, default=32)
A = ap.parse_args()
fs = sorted(glob.glob(os.path.join(A.src, "origin=*", "*.parquet")))
print(f"{len(fs)} parquet files", flush=True)
acc = {}
with ProcessPoolExecutor(A.workers) as ex:
for k, r in enumerate(ex.map(scan, fs, chunksize=2), 1):
merge(acc, r)
if k % 500 == 0: print(f" {k}/{len(fs)}", flush=True)
os.makedirs(A.dst, exist_ok=True)
bw_e = (RANGES["emo"][1]-RANGES["emo"][0])/NB; bw_v = (RANGES["vn"][1]-RANGES["vn"][0])/NB
F = ["count","mean","min","max","p1","p10","p25","median","p75","p90","p99"]
with open(os.path.join(A.dst, "stats_emotions.csv"), "w", newline="") as fh:
w = csv.writer(fh); w.writerow(["# percentile bin width", f"{bw_e:.6f}"])
w.writerow(["emotion","origin"]+F+["n_top_emotion","frac_top_emotion"])
for org in sorted(acc):
tot = sum(acc[org]["top"].values()) or 1
for e in EMO:
r = acc[org]["emo"][e].row()
w.writerow([e,org]+[r[k] for k in F]+[acc[org]["top"][e], acc[org]["top"][e]/tot])
with open(os.path.join(A.dst, "stats_dimensions.csv"), "w", newline="") as fh:
w = csv.writer(fh); w.writerow(["# percentile bin width", f"{bw_v:.6f}"])
w.writerow(["dimension","origin"]+F+[f"bucket_{i}" for i in range(8)])
for org in sorted(acc):
for d in VN:
r = acc[org]["vn"][d].row()
w.writerow([d,org]+[r[k] for k in F]+list(acc[org]["bucket"][d]))
with open(os.path.join(A.dst, "stats_profiles.csv"), "w", newline="") as fh:
w = csv.writer(fh)
w.writerow(["voice","origin","n_utterances","hours","dur_mean","dur_min","dur_max",
"dur_p10","dur_median","dur_p90","mean_genuineness","mean_blend","total_bursts"])
for org in sorted(acc):
for v in sorted(acc[org]["voice"]):
s = acc[org]["voice"][v]; r = s["d"].row(); n = s["n"] or 1
w.writerow([v,org,s["n"], (s["d"].s/3600.0), r["mean"], r["min"], r["max"],
r["p10"], r["median"], r["p90"], s["sg"]/n, s["sb"]/n, s["nb"]])
# ---- shipped normalisation artifact -------------------------------------------------
# Descriptive statistics of THIS population, recomputed from the re-annotated output.
# NOTE: the captions do not consume these. `vn_*_bucket` is the argmax of an ordinal
# classifier trained on absolute prose anchors, so caption wording is population-independent.
# These are published so that users who WANT population-relative normalisation have a
# reference that is (a) specific to the voice profiles and (b) free of the half-speed values.
import json
norm = dict(
_about=("Descriptive statistics of the LAION voice-profile population, computed from the "
"re-annotated (stereo-fixed) output. NOT used by the caption renderer: vn_*_bucket "
"is an absolute ordinal-classifier argmax, not a percentile of this population. "
"Provided for users who want population-relative normalisation."),
_scope="voice profiles only (origin=original + origin=repair); not the wider 8-dataset corpus",
_bin_width=dict(emotion=bw_e, voicenet_reg=bw_v),
per_origin={})
for org in sorted(acc):
norm["per_origin"][org] = dict(
emotions={e: acc[org]["emo"][e].row() for e in EMO},
voicenet={d: dict(acc[org]["vn"][d].row(),
bucket_counts=[int(x) for x in acc[org]["bucket"][d]]) for d in VN},
n_voices=len(acc[org]["voice"]),
n_utterances=sum(v["n"] for v in acc[org]["voice"].values()))
with open(os.path.join(A.dst, "norm_stats_vprof.json"), "w") as fh:
json.dump(norm, fh, indent=1)
print("wrote 3 CSVs + norm_stats_vprof.json to", A.dst)
if __name__ == "__main__":
main()
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