fix: drop z-score norm, ckpt never applied it

app.py

@@ -24,7 +24,7 @@ import torch
 from pyharp import ModelCard, build_endpoint, load_audio
 
 from models.moe_research.w2v2_moe_fz24_aasist import Model
-from utils.tools.tools import pad, norm
+from utils.tools.tools import pad
 
 DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 CKPT_PATH = os.path.join(os.path.dirname(__file__), "checkpoints", "fz24_moe_aasist.ckpt")
@@ -85,15 +85,14 @@ model_card = ModelCard(
 
 
 def preprocess(sig):
-    """Mirrors the original repo's eval-time pipeline: pad or crop to ~4s, then
-    per-utterance z-score normalize (the model expects a raw normalized
-    waveform, not wav2vec2's own feature-extractor normalization). The
-    original assumed pre-formatted 16kHz mono ASVspoof audio and skipped this
-    step; HARP users upload arbitrary files, so mono/resample is added here."""
+    """Pad/crop to ~4s, raw waveform, no z-score norm -- matches
+    asvspoof_data_DA.py, the data module this checkpoint's hparams.yaml
+    actually names. Mono/16kHz resample added since HARP users upload
+    arbitrary files, unlike the original's pre-formatted 16kHz mono
+    ASVspoof set."""
     sig = sig.to_mono().resample(16000)
     wav = sig.audio_data[0, 0].cpu().numpy()
     wav = pad(wav, TRUNCATE)
-    wav = norm(wav)
     return torch.tensor(wav, dtype=torch.float32)