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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()
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