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example_usage.py

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+"""
+Example usage of the MLX ResNet34 speaker embedding model.
+"""
+
+import mlx.core as mx
+import numpy as np
+from resnet_embedding import load_resnet34_embedding
+
+
+def main():
+    print("="*70)
+    print(" MLX ResNet34 Speaker Embedding - Example Usage")
+    print("="*70)
+
+    # Load model
+    print("\n[1] Loading model...")
+    model = load_resnet34_embedding("weights.npz")
+    print("✓ Model loaded successfully!")
+
+    # Create example mel spectrogram input
+    print("\n[2] Creating example mel spectrogram input...")
+    # In practice, you would extract this from real audio
+    # Shape: (batch_size, time_frames, freq_bins)
+    batch_size = 2
+    time_frames = 150  # ~1.5 seconds at 10ms hop
+    freq_bins = 80     # mel filterbanks
+
+    mel_spec = mx.array(np.random.randn(batch_size, time_frames, freq_bins).astype(np.float32))
+    print(f"  Input shape: {mel_spec.shape}")
+
+    # Extract embeddings
+    print("\n[3] Extracting speaker embeddings...")
+    embeddings = model(mel_spec)
+    print(f"  Output shape: {embeddings.shape}")
+    print(f"  Embedding norms: {mx.linalg.norm(embeddings, axis=1)}")
+
+    # Compute speaker similarity
+    print("\n[4] Computing speaker similarity...")
+    emb1 = embeddings[0]
+    emb2 = embeddings[1]
+
+    # Cosine similarity
+    similarity = mx.sum(emb1 * emb2) / (
+        mx.linalg.norm(emb1) * mx.linalg.norm(emb2)
+    )
+
+    print(f"  Cosine similarity between speaker 1 and 2: {float(similarity):.4f}")
+    print(f"  Interpretation:")
+    print(f"    > 0.9  = Likely same speaker")
+    print(f"    0.5-0.9 = Uncertain")
+    print(f"    < 0.5  = Likely different speakers")
+
+    # Batch processing example
+    print("\n[5] Batch processing multiple utterances...")
+    num_utterances = 5
+    utterances = mx.array(np.random.randn(num_utterances, time_frames, freq_bins).astype(np.float32))
+
+    batch_embeddings = model(utterances)
+    print(f"  Processed {num_utterances} utterances")
+    print(f"  Embeddings shape: {batch_embeddings.shape}")
+
+    # Compute pairwise similarities
+    print("\n[6] Computing pairwise similarity matrix...")
+    # Normalize embeddings for faster cosine similarity
+    emb_normalized = batch_embeddings / mx.linalg.norm(batch_embeddings, axis=1, keepdims=True)
+
+    # Similarity matrix via matrix multiplication
+    similarity_matrix = emb_normalized @ emb_normalized.T
+    sim_np = np.array(similarity_matrix)
+
+    print("\n  Similarity Matrix:")
+    print("        ", end="")
+    for i in range(num_utterances):
+        print(f"Utt{i}  ", end="")
+    print()
+
+    for i in range(num_utterances):
+        print(f"  Utt{i}:", end="")
+        for j in range(num_utterances):
+            print(f" {sim_np[i,j]:.3f} ", end="")
+        print()
+
+    print("\n" + "="*70)
+    print(" ✓ Example completed successfully!")
+    print("="*70)
+
+
+if __name__ == "__main__":
+    main()