| """Prompt-clustered bootstrap CIs for Table 12's alignment correlations. |
| |
| python analysis/06_voice_study/07_synth_alignment_bootstrap.py [--reps 5000] |
| |
| Table 12's correlations are computed over 60 clips that come in 30 prompt pairs, so |
| clip-level CIs would overstate precision. Here the bootstrap resamples the 30 |
| PROMPTS with replacement and both of a prompt's clips travel together, giving |
| cluster-robust percentile CIs for r and rho per (voice, rater), plus CIs for |
| each voice's difference from the af_heart baseline within the same rater. |
| |
| Read the differences before claiming voice dependence: a baseline-vs-voice |
| difference whose CI covers zero "varied descriptively across renderers", no |
| more. |
| """ |
|
|
| from __future__ import annotations |
|
|
| import argparse |
| import sys |
| from pathlib import Path |
|
|
| import numpy as np |
| import pandas as pd |
| from scipy import stats |
|
|
| sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "common")) |
|
|
| from _voice_synth_alignment import MODELS, clip_means |
|
|
| ROOT = Path(__file__).resolve().parent.parent.parent |
| VOICE_DATA = ROOT / "data/voice_study" |
| CONTEXT_DATA = ROOT / "data/audio_context_study" |
| DERIVED = ROOT / "results/06_voice_study" |
| SD = ROOT / "data/main_study" |
|
|
|
|
| def load_pairs() -> dict[tuple[str, str], pd.DataFrame]: |
| """(voice, rater) -> per-clip h/s means with a PromptID column.""" |
| prompts = set(pd.read_csv(ROOT / "stimuli/voice_study/prompts.csv")["PromptID"]) |
| human = {} |
| voice = pd.read_csv(VOICE_DATA / "long_voice_normalized.csv") |
| for arm, g in voice.groupby("Arm"): |
| human[arm] = clip_means(g) |
| base = pd.read_csv(SD / "long_audio_table_normalized.csv") |
| base = base[~base["IsAttentionCheck"].astype(bool)] |
| human["kokoro_af_heart (baseline)"] = clip_means( |
| base[base["PromptID"].isin(prompts)]) |
|
|
| out = {} |
| for voice_name, h in human.items(): |
| for model in MODELS: |
| path = (SD / f"synth_long_audio_table_{model}.csv" |
| if "baseline" in voice_name else |
| VOICE_DATA / "synth" / |
| f"synth_long_audio_table_{voice_name}_{model}.csv") |
| if not path.exists(): |
| continue |
| s = pd.read_csv(path) |
| s = s[s["PromptID"].astype(str).isin(prompts)] |
| m = pd.concat([h.rename("h"), clip_means(s).rename("s")], |
| axis=1).dropna().reset_index() |
| m["PromptID"] = m["AudioClip"].str.rsplit("_", n=1).str[0] |
| out[(voice_name, model)] = m |
| return out |
|
|
|
|
| def corrs(m: pd.DataFrame) -> tuple[float, float]: |
| if m["h"].nunique() < 2 or m["s"].nunique() < 2: |
| return np.nan, np.nan |
| return (stats.pearsonr(m.h, m.s)[0], stats.spearmanr(m.h, m.s)[0]) |
|
|
|
|
| def boot(m: pd.DataFrame, reps: int, rng) -> np.ndarray: |
| groups = {p: g[["h", "s"]] for p, g in m.groupby("PromptID")} |
| ids = np.array(list(groups)) |
| out = np.empty((reps, 2)) |
| for i in range(reps): |
| take = rng.choice(ids, size=len(ids), replace=True) |
| out[i] = corrs(pd.concat([groups[p] for p in take], ignore_index=True)) |
| return out |
|
|
|
|
| def ci(x: np.ndarray) -> tuple[float, float]: |
| x = x[~np.isnan(x)] |
| return np.percentile(x, 2.5), np.percentile(x, 97.5) |
|
|
|
|
| def main() -> None: |
| ap = argparse.ArgumentParser() |
| ap.add_argument("--reps", type=int, default=5000) |
| ap.add_argument("--seed", type=int, default=0) |
| args = ap.parse_args() |
| rng = np.random.default_rng(args.seed) |
|
|
| pairs = load_pairs() |
| draws = {k: boot(m, args.reps, rng) for k, m in pairs.items()} |
|
|
| rows = [] |
| for (voice_name, model), m in pairs.items(): |
| r, rho = corrs(m) |
| d = draws[(voice_name, model)] |
| r_lo, r_hi = ci(d[:, 0]) |
| rho_lo, rho_hi = ci(d[:, 1]) |
| row = dict(voice=voice_name, rater=model, r=r, |
| r_lo=r_lo, r_hi=r_hi, rho=rho, |
| rho_lo=rho_lo, rho_hi=rho_hi) |
| basekey = ("kokoro_af_heart (baseline)", model) |
| if "baseline" not in voice_name and basekey in draws: |
| |
| |
| |
| dd = draws[(voice_name, model)] - draws[basekey] |
| row["dr_vs_base"] = r - corrs(pairs[basekey])[0] |
| row["dr_lo"], row["dr_hi"] = ci(dd[:, 0]) |
| rows.append(row) |
|
|
| out = pd.DataFrame(rows) |
| print(out.to_string(index=False, float_format=lambda v: f"{v:.3f}")) |
| out.to_csv(DERIVED / "synth_alignment_bootstrap.csv", index=False) |
| print(f"\n({args.reps} prompt-cluster resamples, seed {args.seed})") |
| print(f" -> results/06_voice_study/synth_alignment_bootstrap.csv") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
