Upload verify_matty.py

verify_matty.py

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+import os
+import io
+import time
+import numpy as np
+import torch
+import soundfile as sf
+from tqdm import tqdm
+import pyarrow.parquet as pq
+
+from transformers import AutoFeatureExtractor, ASTForAudioClassification
+
+# =========================
+# 0) 你只改这里
+# =========================
+PARQUET_DIR = r"D:\capstone\asv_spoof\parquet"
+MODEL_DIR   = r"D:\capstone\models\matty_snr"
+
+SPLIT = "test"          # "train" / "validation" / "test"
+BATCH_SIZE = 32         # 4090 可 64
+CPU_THREADS = 8         # 影响音频解码/预处理
+
+# key 的定义：根据你的数据分布 & system_id 对齐： key=1 是 spoof，key=0 是 bonafide
+# （system_id: '-' 是 bonafide；'Axx' 是 spoof）
+KEY_SPOOF_VALUE = 1
+
+PARQUET_FILE = os.path.join(PARQUET_DIR, f"{SPLIT}-00000-of-00001.parquet")
+
+# 是否做 system_id 与 key 的一致性检查（不影响推理，只打印检查结果）
+CHECK_LABEL_CONSISTENCY = True
+
+
+# =========================
+# 1) 音频解码/重采样（不落盘）
+# =========================
+def decode_audio(bytes_blob: bytes | None, path_str: str | None):
+    if bytes_blob is not None:
+        wav, sr = sf.read(io.BytesIO(bytes_blob), dtype="float32", always_2d=False)
+    else:
+        if not path_str or not os.path.exists(path_str):
+            raise RuntimeError("audio.bytes 为空，且 audio.path 不存在/不可用")
+        wav, sr = sf.read(path_str, dtype="float32", always_2d=False)
+
+    if isinstance(wav, np.ndarray) and wav.ndim > 1:
+        wav = wav.mean(axis=1)
+    return wav.astype(np.float32), int(sr)
+
+
+def simple_resample(wav: np.ndarray, sr: int, new_sr: int) -> np.ndarray:
+    if sr == new_sr:
+        return wav
+    if wav.size == 0:
+        return wav
+    x_old = np.linspace(0, 1, num=wav.shape[0], endpoint=False)
+    new_len = int(round(wav.shape[0] * (new_sr / sr)))
+    x_new = np.linspace(0, 1, num=new_len, endpoint=False)
+    return np.interp(x_new, x_old, wav).astype(np.float32)
+
+
+def key_to_label01(k) -> int:
+    # parquet 里 key 是 int64，但有时 to_pylist 可能给 int 或 str
+    v = int(k)
+    return 1 if v == KEY_SPOOF_VALUE else 0
+
+
+def system_id_to_label01(sid: str) -> int:
+    sid = str(sid).strip()
+    return 0 if sid == "-" else 1  # '-' bonafide, 'Axx' spoof
+
+
+# =========================
+# 2) 设备 & 模型
+# =========================
+torch.set_num_threads(CPU_THREADS)
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print("Device:", device)
+if device.type == "cuda":
+    print("GPU:", torch.cuda.get_device_name(0))
+    torch.backends.cudnn.benchmark = True
+
+use_amp = (device.type == "cuda")
+
+extractor = AutoFeatureExtractor.from_pretrained(MODEL_DIR)
+model = ASTForAudioClassification.from_pretrained(MODEL_DIR).to(device).eval()
+target_sr = getattr(extractor, "sampling_rate", 16000)
+
+# =========================
+# 3) 读 parquet
+# =========================
+pf = pq.ParquetFile(PARQUET_FILE)
+num_rows = pf.metadata.num_rows
+num_batches = (num_rows + BATCH_SIZE - 1) // BATCH_SIZE
+
+print(f"Parquet: {PARQUET_FILE}")
+print(f"Rows: {num_rows}, Batches: {num_batches}, BatchSize: {BATCH_SIZE}")
+
+# =========================
+# 4) 推理 + 指标统计
+# =========================
+correct = 0
+total = 0
+tp = fp = tn = fn = 0  # pos=spoof=1
+
+# 可选：检查 key 与 system_id 是否一致
+mismatch = 0
+checked = 0
+
+t0 = time.time()
+with torch.no_grad():
+    pbar = tqdm(total=num_batches, desc=f"Predicting [{SPLIT}]", unit="batch")
+
+    for rb in pf.iter_batches(batch_size=BATCH_SIZE, columns=["audio", "key", "system_id"]):
+        audio_struct = rb.column(rb.schema.get_field_index("audio"))
+        key_arr = rb.column(rb.schema.get_field_index("key"))
+        sys_arr = rb.column(rb.schema.get_field_index("system_id"))
+
+        bytes_arr = audio_struct.field("bytes") if audio_struct.type.get_field_index("bytes") != -1 else None
+        path_arr  = audio_struct.field("path")  if audio_struct.type.get_field_index("path")  != -1 else None
+
+        keys = key_arr.to_pylist()
+        sysids = sys_arr.to_pylist()
+        bytes_list = bytes_arr.to_pylist() if bytes_arr is not None else [None] * len(keys)
+        path_list  = path_arr.to_pylist()  if path_arr  is not None else [None] * len(keys)
+
+        waves = []
+        labels = []
+
+        for b, p, k, sid in zip(bytes_list, path_list, keys, sysids):
+            y = key_to_label01(k)
+            labels.append(y)
+
+            if CHECK_LABEL_CONSISTENCY:
+                y2 = system_id_to_label01(sid)
+                checked += 1
+                if y != y2:
+                    mismatch += 1
+
+            wav, sr = decode_audio(b, p)
+            wav = simple_resample(wav, sr, target_sr)
+            waves.append(wav)
+
+        inputs = extractor(
+            waves,
+            sampling_rate=target_sr,
+            return_tensors="pt",
+            padding=True,
+        )
+        inputs = {k: v.to(device, non_blocking=True) for k, v in inputs.items()}
+        labels_t = torch.tensor(labels, dtype=torch.long, device=device)
+
+        if use_amp:
+            with torch.amp.autocast("cuda"):
+                logits = model(**inputs).logits
+        else:
+            logits = model(**inputs).logits
+
+        preds = torch.argmax(logits, dim=-1)
+
+        total += labels_t.numel()
+        correct += (preds == labels_t).sum().item()
+
+        tp += ((preds == 1) & (labels_t == 1)).sum().item()
+        fp += ((preds == 1) & (labels_t == 0)).sum().item()
+        tn += ((preds == 0) & (labels_t == 0)).sum().item()
+        fn += ((preds == 0) & (labels_t == 1)).sum().item()
+
+        pbar.update(1)
+
+    pbar.close()
+
+elapsed = time.time() - t0
+
+# =========================
+# 5) 计算指标
+# =========================
+acc = correct / max(total, 1)
+
+eps = 1e-12
+precision = tp / (tp + fp + eps)
+recall    = tp / (tp + fn + eps)  # TPR
+f1        = 2 * precision * recall / (precision + recall + eps)
+fnr       = fn / (fn + tp + eps)
+fpr       = fp / (fp + tn + eps)
+
+print("\n===== Summary =====")
+print(f"Split      : {SPLIT}")
+print(f"Accuracy   : {acc:.6f}  ({correct}/{total})")
+print(f"Confusion  : TP={tp}, FP={fp}, TN={tn}, FN={fn}")
+print(f"Time       : {elapsed:.2f}s, {total / max(elapsed,1e-9):.2f} samples/s")
+
+if CHECK_LABEL_CONSISTENCY:
+    print(f"Label check: key vs system_id mismatches = {mismatch}/{checked}")
+
+print("\n===== Metrics (pos=spoof=1) =====")
+print(f"Precision  : {precision:.6f}")
+print(f"FNR        : {fnr:.6f}")
+print(f"FPR        : {fpr:.6f}")
+print(f"F1-score   : {f1:.6f}")
+
+
+'''
+===== Summary =====
+Split      : test
+Accuracy   : 0.898845  (64031/71237)
+Confusion  : TP=57091, FP=415, TN=6940, FN=6791
+Time       : 1135.30s, 62.75 samples/s
+Label check: key vs system_id mismatches = 0/71237
+
+===== Metrics (pos=spoof=1) =====
+Precision  : 0.992783
+FNR        : 0.106305
+FPR        : 0.056424
+F1-score   : 0.940637
+'''