ارزیابی مدل‌های زایشی

mu_r = mean_vec(real_features)
mu_g = mean_vec(gen_features)
cov_r = covariance(real_features, mu_r)
mean_sq = sum((a - b) ** 2 for a, b in zip(mu_r, mu_g))
sqrt_prod = jacobi_sqrt(prod)     # جذر ماتریسی
return mean_sq + trace(cov_r) + trace(cov_g) - 2 * trace(sqrt_prod)
score = clip_like(image, prompt)  # کسینوسی، بیشتر بهتر است
expected_a = 1 / (1 + 10 ** ((r_b - r_a) / 400))
actual_a = 1.0 if winner == "a" else 0.0
delta = k * (actual_a - expected_a)              # k=32
return r_a + delta, r_b - delta

transform = Compose([ToLowerCase(), RemovePunctuation(), Strip()])
score = wer(truth="<ref>", hypothesis="<hyp>",
            truth_transform=transform, hypothesis_transform=transform)
sv = EncoderClassifier.from_hparams("speechbrain/spkrec-ecapa-voxceleb")
emb_ref = sv.encode_batch(load_wav("reference.wav"))
emb_clone = sv.encode_batch(load_wav("cloned.wav"))
secs = torch.nn.functional.cosine_similarity(emb_ref, emb_clone, dim=-1).item()
fad = FrechetAudioDistance()
score = fad.get_fad_score("generated_folder/", "reference_folder/")