| """Do the paper's audio-side findings survive when the CONTEXT is heard too? |
| |
| python analysis/07_audio_context_study/02_replication.py |
| python analysis/07_audio_context_study/02_replication.py --skip-models |
| |
| The voice study swapped the voice and held everything else fixed. This study swaps |
| the modality of the conversation history: the same 30 prompts are rated with |
| the history read (the original study) or heard, either in one voice for both |
| speakers (B1) or with a distinct voice per speaker (B2). |
| |
| Every metric is the one _voice_variation_replication.py computes -- imported from there |
| rather than reimplemented, so the two studies cannot drift apart in how a |
| threshold or an ICC is defined. The only differences are which long tables get |
| loaded and which prompt set the reference arms are restricted to (the 30 |
| context prompts, not the 30 voice prompts). |
| |
| B1 is the conservative cell: context modality changes, voice identity does not. |
| B2 adds a second speaker. Reading B2 against B1 rather than against the text |
| baseline is what separates speaker differentiation from audio context itself. |
| """ |
|
|
| from __future__ import annotations |
|
|
| import argparse |
| import sys |
| from pathlib import Path |
|
|
| import numpy as np |
| import pandas as pd |
|
|
| sys.path.insert(0, str(Path(__file__).resolve().parent)) |
|
|
| from _voice_variation_replication import ( |
| CONTEXT_DATA, ROOT, SD, decision_threshold, fit_bias_model, icc, load_lengths, |
| winner, |
| ) |
|
|
| DERIVED = ROOT / "results/07_audio_context_study" |
|
|
| |
| |
| ARMS = { |
| "B1 one-voice context": CONTEXT_DATA / "long_audiocontext_onevoice_normalized.csv", |
| "B2 two-voice context": CONTEXT_DATA / "long_audiocontext_normalized.csv", |
| "ORIGINAL af_heart": SD / "long_audio_table_normalized.csv", |
| "ORIGINAL text": SD / "long_text_table_normalized.csv", |
| } |
|
|
|
|
| def load_arms(prompts: set[str] | None) -> pd.DataFrame: |
| frames = [] |
| for name, f in ARMS.items(): |
| if not f.exists(): |
| print(f"!! {f.relative_to(ROOT)} missing — skipping {name}") |
| continue |
| d = pd.read_csv(f) |
| if "IsAttentionCheck" in d.columns: |
| d = d[~d["IsAttentionCheck"].astype(bool)] |
| d = d.copy() |
| d["Arm"] = name |
| frames.append(d) |
| df = pd.concat(frames, ignore_index=True).dropna(subset=["Rating"]) |
| if prompts is not None: |
| df = df[df["PromptID"].isin(prompts)] |
| return df |
|
|
|
|
| def threshold_median(g: pd.DataFrame) -> float: |
| """Median of the per-prompt mean gaps -- the paired half of Table 10's pair. |
| |
| The mean is pulled by a handful of prompts where every rater declared a |
| large gap; the paper reports both for that reason. |
| """ |
| d = g[g["overallPreference"].isin(["A", "B"])] |
| gaps = (d.groupby(["ParticipantID", "PromptID"])["Rating"] |
| .agg(lambda x: x.max() - x.min() if len(x) == 2 else np.nan) |
| .dropna()) |
| if gaps.empty: |
| return np.nan |
| return float(gaps.groupby(level="PromptID").mean().median()) |
|
|
|
|
| def main() -> None: |
| ap = argparse.ArgumentParser() |
| ap.add_argument("--skip-models", action="store_true") |
| ap.add_argument("--all-prompts", action="store_true") |
| args = ap.parse_args() |
|
|
| lengths = load_lengths() |
| sel = pd.read_csv(ROOT / "stimuli/audio_context_study/prompts.csv")["PromptID"] |
| prompts = None if args.all_prompts else set(sel) |
| if prompts: |
| print(f"restricted to the {len(prompts)} Ablation-B prompts") |
|
|
| df = load_arms(prompts) |
| text_win = winner(df[df["Arm"] == "ORIGINAL text"]) |
| b1_win = winner(df[df["Arm"] == "B1 one-voice context"]) |
|
|
| rows = [] |
| for arm, g in df.groupby("Arm", sort=True): |
| per_clip = g.groupby("AudioClip").size() |
| thr, _ = decision_threshold(g) |
|
|
| w = winner(g) |
| def agree(other): |
| both = w.index.intersection(other.index) |
| return (np.nan if not len(both) |
| else float((w[both] == other[both]).mean())) |
|
|
| rec_b = rec_p = len_b = len_p = i1 = ik = kbar = np.nan |
| if not args.skip_models: |
| print(f" fitting {arm} ...") |
| rec_b, rec_p, len_b, len_p = fit_bias_model(g, lengths) |
| i1, ik, kbar = icc(g) |
|
|
| rows.append(dict(arm=arm, n=len(g), |
| participants=g["ParticipantID"].nunique(), |
| prompts=g["PromptID"].nunique(), |
| raters_per_clip=f"{per_clip.min()}-{per_clip.max()}", |
| thr_mean=thr, thr_median=threshold_median(g), |
| recency_b=rec_b, recency_p=rec_p, |
| length_b=len_b, length_p=len_p, |
| icc1=i1, icck=ik, k=kbar, |
| agree_text=agree(text_win), agree_b1=agree(b1_win))) |
|
|
| res = pd.DataFrame(rows) |
| res.to_csv(DERIVED / "context_replication.csv", index=False) |
|
|
| pd.set_option("display.width", 250, "display.max_columns", 50) |
| print("\n" + "=" * 78) |
| print("PAPER'S AUDIO SIGNATURES WITH THE CONTEXT HEARD RATHER THAN READ") |
| print("=" * 78) |
| print(res.to_string(index=False, float_format=lambda x: f"{x:7.3f}")) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
