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0bb1236 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 | """Validation suite for the surgical emotion module.
Checks:
1. Frame-rate contract: T* == ceil(duration_s * 12.5) for varied durations.
2. Interface contract: shapes/dims/ranges of all outputs.
3. Emotion fidelity: top-1 agreement + JS divergence of student vs teacher
utterance probabilities over the val set; per-RAVDESS-emotion breakdown.
4. Dynamics: correlation between student intensity curve and teacher
max-prob proxy; frame-prob trajectory plot for one emotional utterance.
Writes /tmp/work/surgical_out/eval_report.json + dynamics_plot.png
"""
from __future__ import annotations
import glob
import json
import os
import random
import sys
import time
import numpy as np
import torch
import torchaudio
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from surgical_emotion import SurgicalEmotionEncoder
FUNASR_DIR = "/tmp/work/emotion2vec_plus_base"
CKPT = "/tmp/work/surgical_out/surgical_emotion_module.pt"
OUT_DIR = "/tmp/work/surgical_out"
RAVDESS_DIR = "/tmp/work/ravdess_kag"
TEST_ACTORS = set(range(19, 25)) # held-out actors
def _actor_of(path):
try:
return int(os.path.basename(path).split("-")[-1].split(".")[0])
except Exception:
return 0
def _is_speech(path):
try:
return os.path.basename(path).split("-")[1] == "01"
except Exception:
return False
def rms_envelope_50hz_t(wav: torch.Tensor, T_frames: int) -> torch.Tensor:
frames = torch.zeros(T_frames)
for i in range(T_frames):
seg = wav[i * 320 : (i + 1) * 320]
frames[i] = float(seg.pow(2).mean().sqrt()) if seg.numel() else (frames[i - 1] if i else 0.0)
if T_frames >= 9:
frames = torch.nn.functional.conv1d(
frames[None, None], torch.ones(9)[None, None] / 9.0, padding=4)[0, 0]
lo, hi = frames.min(), frames.max()
return torch.full_like(frames, 0.5) if hi - lo < 1e-6 else (frames - lo) / (hi - lo)
def js_div(p, q, eps=1e-8):
p = p.clamp(min=eps); q = q.clamp(min=eps)
p = p.clamp(min=eps); q = q.clamp(min=eps)
m = 0.5 * (p + q)
kl = lambda a, b: (a * (a.log() - b.log())).sum(-1)
return 0.5 * kl(p, m) + 0.5 * kl(q, m)
def load_wav(path):
wav, sr = torchaudio.load(path)
wav = wav.mean(dim=0)
if sr != 16000:
wav = torchaudio.functional.resample(wav, sr, 16000)
return wav
def main():
device = "cuda:0" if torch.cuda.is_available() else "cpu"
enc = SurgicalEmotionEncoder.from_pretrained(FUNASR_DIR, CKPT, device=device)
enc.eval()
cfg = enc.cfg
report = {"frame_rate_hz": cfg.frame_rate_hz, "checks": {}, "files": {}}
# ---------- 1. rate & interface contract ----------
ok = True
for sec in [1.0, 2.5, 5.0, 10.0]:
n = int(sec * 16000)
wav = torch.randn(1, n, device=device)
wav = wav / wav.abs().max().clamp(min=1e-3)
out = enc(wav, torch.tensor([n], device=device))
expect = int(np.ceil(sec * cfg.frame_rate_hz))
got = int(out["frame_embs"].shape[1])
good = abs(got - expect) <= 1
ok &= good
report["checks"][f"{sec}s"] = {"expected~": expect, "got": got, "ok": good}
assert out["frame_embs"].shape[-1] == cfg.out_dim
assert out["frame_probs"].shape[-1] == cfg.n_emotions
assert 0.0 <= float(out["frame_intensity"].min()) and float(out["frame_intensity"].max()) <= 1.0
report["rate_contract_ok"] = bool(ok)
print("rate contract ok:", ok)
# ---------- 2. fidelity on RAVDESS ----------
files = sorted(glob.glob(os.path.join(RAVDESS_DIR, "**", "*.wav"), recursive=True))
files = [f for f in files if _is_speech(f) and _actor_of(f) in TEST_ACTORS]
if not files:
files = [FUNASR_DIR + "/example/test.wav"]
random.seed(0)
sel = random.sample(files, min(60, len(files)))
agree, jss, corrs = [], [], []
per_emo = {}
plot_sample = None
with torch.no_grad():
for f in sel:
wav = load_wav(f)[: 12 * 16000]
if wav.numel() < 8000:
continue
wav = wav / wav.abs().max().clamp(min=1e-4)
wav = wav[None].to(device)
out = enc(wav, torch.tensor([wav.size(1)], device=device))
states, lengths = enc.extract_teacher_states(wav, torch.tensor([wav.size(1)], device=device))
t_probs, t_inten = enc.teacher_frame_probs(states, lengths)
tp = torch.softmax(enc.teacher.proj(states.mean(1)), dim=-1)[0]
sp = out["utt_probs"][0]
agree.append(int(sp.argmax().item() == tp.argmax().item()))
jss.append(float(js_div(sp[None], tp[None])))
si = out["frame_intensity"][0, :, 0].cpu()
# composite target: confidence-gated expression energy (same as training)
T_frames = int(lengths[0].item())
rms = rms_envelope_50hz_t(wav.cpu().squeeze(0), T_frames)
tmax = t_inten[0, :, 0].cpu().clamp(min=0, max=1).sqrt()
ti = tmax * rms
ti_ds = torch.nn.functional.interpolate(
ti[None, None], size=si.size(0), mode="linear")[0, 0]
if si.var() > 1e-6 and ti_ds.var() > 1e-6:
c = float(np.corrcoef(si.numpy(), ti_ds.numpy())[0, 1])
corrs.append(c)
name = os.path.basename(f)
emo = {"01": "neutral", "02": "calm", "03": "happy", "04": "sad",
"05": "angry", "06": "fearful", "07": "disgust", "08": "surprised"}.get(
name.split("-")[2], "?")
d = per_emo.setdefault(emo, {"n": 0, "agree": 0})
d["n"] += 1
d["agree"] += agree[-1]
report["files"][name] = {
"raveil_label": emo,
"teacher_top": int(tp.argmax()), "student_top": int(sp.argmax()),
"teacher_probs": [round(x, 3) for x in tp.tolist()],
"student_probs": [round(x, 3) for x in sp.tolist()],
"intensity_std": round(float(si.std()), 4),
}
if plot_sample is None and emo in ("angry", "happy", "sad"):
plot_sample = (name, out["frame_probs"][0].cpu().numpy(),
si.numpy(), ti_ds.numpy(), tp.cpu().numpy(), emo)
report["agreement"] = float(np.mean(agree))
report["mean_js_teacher"] = float(np.mean(jss))
report["intensity_corr"] = float(np.mean(corrs)) if corrs else None
report["per_emotion"] = per_emo
print(f"agreement={report['agreement']:.3f} JS={report['mean_js_teacher']:.4f} "
f"intensity_corr={report['intensity_corr']}")
# ---------- 3. dynamics plot ----------
try:
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
if plot_sample is not None:
name, fp, si, ti_ds, tp_, emo = plot_sample
t = np.arange(len(si)) * 0.08
fig, ax = plt.subplots(3, 1, figsize=(11, 7), sharex=True)
for k, lab in enumerate(["angry", "disgusted", "fearful", "happy",
"neutral", "other", "sad", "surprised"]):
ax[0].plot(t, fp[:, k], label=lab, lw=1.2)
ax[0].set_title(f"student frame emotion probs @12.5Hz — {name} ({emo})")
ax[0].legend(fontsize=7, ncol=4); ax[0].set_ylabel("p")
ax[1].plot(t, si, color="crimson", lw=1.8, label="student intensity")
ax[1].plot(t, ti_ds, color="steelblue", lw=1.4, ls="--",
label="target (conf-gated energy)")
ax[1].set_ylabel("intensity"); ax[1].set_ylim(0, 1.05); ax[1].legend(fontsize=8)
ax[2].bar(["angry", "disg", "fear", "happy", "neut", "other", "sad", "surp", "unk"],
tp_[:9], color="gray")
ax[2].set_ylabel("teacher utt probs"); ax[2].set_xlabel("utterance (frame-aligned time axis)")
fig.tight_layout()
fig.savefig(os.path.join(OUT_DIR, "dynamics_plot.png"), dpi=110)
print("plot saved")
except Exception as e:
print("plot skipped:", e)
with open(os.path.join(OUT_DIR, "eval_report.json"), "w") as f:
json.dump(report, f, indent=1)
print("EVAL_DONE")
if __name__ == "__main__":
main()
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