File size: 4,005 Bytes
1d5cf8b | 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 | #!/usr/bin/env python3
"""Frozen-model replication on the CosyVoice 2 extension subset (data/experiment2/).
Reads only released files. For each of the 60 analysed pairs it computes the
listener agreement rate P(same) (choice 3 or 4 on the 4-point scale), scores
every model with the cosine similarity between the claimed speaker's reference
clip and the comparison clip, and reports the Pearson r with P(same), a
95% cluster-bootstrap interval over the 10 speakers, and the Spearman rank
correlation with the main-benchmark Task 1 ranking.
Models are applied frozen. Supervised models use their native embedding; SSL
models and Whisper use the layer selected on the main benchmark (the modal
layer of the nested 10-fold selection).
Usage:
python3 extension_cosyvoice2.py
"""
import numpy as np
import pandas as pd
from scipy.stats import pearsonr, spearmanr
from extraction_utils import DEFAULT_BASE_DIR
EXT = DEFAULT_BASE_DIR / "data" / "experiment2"
SEED = 42
N_BOOT = 2000
SUPERVISED = ["resemblyzer", "ecapa_tdnn", "titanet", "rawnet3", "xvector"]
SSL_LAYER = {"wavlm": 0, "wav2vec2": 0, "xlsr": 1, "hubert": 0, "whisper": 3}
# Task 1 Pearson r on the 9,800 main-benchmark pairs, as reported in the paper
# (SSL and Whisper at the nested best layer).
MAIN_TASK1_R = {
"resemblyzer": 0.656, "ecapa_tdnn": 0.646, "titanet": 0.638, "rawnet3": 0.617,
"xvector": 0.597, "wavlm": 0.510, "wav2vec2": 0.471, "xlsr": 0.465,
"hubert": 0.458, "whisper": 0.226,
}
EXTRA = ["wespeaker_resnet34_lm"] # added after review; not in the paper's 10-model ranking
def cosine(a, b):
return float(a @ b / (np.linalg.norm(a) * np.linalg.norm(b) + 1e-10))
def main():
pairs = pd.read_csv(EXT / "pairs.csv")
pairs = pairs[pairs["analysed"]].reset_index(drop=True)
responses = pd.read_csv(EXT / "responses.csv")
responses["same"] = responses["choice"].isin([3, 4]).astype(int)
votes = responses.groupby("stimulus_id")["same"].agg(["mean", "count"])
pairs["p_same"] = pairs["stimulus_id"].map(votes["mean"])
pairs["n_votes"] = pairs["stimulus_id"].map(votes["count"])
print(f"{len(pairs)} analysed pairs, {int(pairs.n_votes.sum())} judgments, "
f"{responses[responses.stimulus_id.isin(pairs.stimulus_id)].listener_id.nunique()} "
f"listeners, median {pairs.n_votes.median():.0f} votes per pair")
for m in SUPERVISED + EXTRA + list(SSL_LAYER):
if m in SSL_LAYER:
emb = np.load(EXT / "embeddings" / "layers" / f"{m}.npz")
get = lambda k, e=emb, L=SSL_LAYER[m]: e[k][L]
else:
emb = np.load(EXT / "embeddings" / f"{m}.npz")
get = lambda k, e=emb: e[k]
pairs[m] = [cosine(get(f"{r.claimed_speaker_id}R"), get(r.comparison_clip_id))
for r in pairs.itertuples()]
models = list(MAIN_TASK1_R) + EXTRA
ext_r = {m: pearsonr(pairs[m], pairs["p_same"])[0] for m in models}
rng = np.random.default_rng(SEED)
speakers = sorted(pairs["claimed_speaker_id"].unique())
boot = {m: [] for m in models}
for _ in range(N_BOOT):
draw = rng.choice(speakers, size=len(speakers), replace=True)
sub = pd.concat([pairs[pairs["claimed_speaker_id"] == s] for s in draw])
for m in models:
ok = sub[m].std() > 1e-10 and sub["p_same"].std() > 1e-10
boot[m].append(pearsonr(sub[m], sub["p_same"])[0] if ok else np.nan)
print(f"\n{'model':24s} {'main r':>7s} {'ext r':>7s} 95% CI (speaker bootstrap)")
for m in sorted(models, key=lambda k: -ext_r[k]):
lo, hi = np.nanpercentile(boot[m], [2.5, 97.5])
main_r = f"{MAIN_TASK1_R[m]:.3f}" if m in MAIN_TASK1_R else " -"
print(f"{m:24s} {main_r:>7s} {ext_r[m]:7.3f} [{lo:.3f}, {hi:.3f}]")
ten = list(MAIN_TASK1_R)
rho = spearmanr([MAIN_TASK1_R[m] for m in ten], [ext_r[m] for m in ten])[0]
print(f"\nSpearman rho, main vs extension ranking (10 paper models): {rho:.3f}")
if __name__ == "__main__":
main()
|