feat: ASRX hybrid pipeline, identity history, worker fixes, checkpoint system

scripts/asrx_self/speaker_classifier.py

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+"""
+Speaker Classifier - 聲紋品質評估與性別分類
+
+提供品質計算與性別分類功能，作為 main_fixed.py 的輔助模組。
+"""
+
+import numpy as np
+
+
+def compute_embedding_quality(embeddings, labels):
+    """每個 embedding 到所屬 cluster centroid 的餘弦相似度
+
+    Args:
+        embeddings: [n_segments, 192] 聲紋向量矩陣
+        labels: [n_segments] 聚類標籤
+
+    Returns:
+        qualities: [n_segments] 品質分數 (0-1)
+    """
+    from sklearn.metrics.pairwise import cosine_similarity
+
+    unique_labels = set(labels)
+    centroids = {}
+    for label in unique_labels:
+        mask = labels == label
+        centroid = np.mean(embeddings[mask], axis=0)
+        norm = np.linalg.norm(centroid)
+        if norm > 0:
+            centroid = centroid / norm
+        centroids[label] = centroid
+
+    qualities = []
+    for emb, label in zip(embeddings, labels):
+        sim = cosine_similarity([emb], [centroids[label]])[0][0]
+        qualities.append(sim)
+
+    return np.array(qualities)
+
+
+def classify_gender(audio_wav, sample_rate, classifier):
+    """從音頻段分類性別
+
+    Args:
+        audio_wav: 音頻波形 (numpy array)
+        sample_rate: 採樣率
+        classifier: SpeechBrain EncoderClassifier (gender-recognition-ecapa)
+
+    Returns:
+        dict: {"gender": "male"|"female"|"unknown", "confidence": float}
+    """
+    default = {"gender": "unknown", "confidence": 0.0}
+    if classifier is None or len(audio_wav) == 0:
+        return default
+    try:
+        import torch
+        seg_tensor = torch.from_numpy(audio_wav).float().unsqueeze(0)
+        out = classifier.classify_batch(seg_tensor)
+        probs = torch.softmax(out[0], dim=-1).squeeze().cpu().detach().numpy()
+        if len(probs) >= 2:
+            idx = int(np.argmax(probs))
+            label = "male" if idx == 0 else "female"
+            return {"gender": label, "confidence": float(probs[idx])}
+    except Exception as e:
+        pass
+    return default