initial

hsmm/toy_data.py

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+import torch
+import numpy as np
+
+def generate_toy_data(n_samples=50, seq_len=300, n_clusters=10, dim=5):
+    """
+    Generates sequences where the hidden states are clusters in D-dimensional space.
+    
+    Args:
+        n_samples: Number of audio 'files'
+        seq_len: Length of each file
+        n_clusters: Number of states (phonemes)
+        dim: Number of features (e.g. 30 for Wav2Vec PCA, 5 for testing)
+    """
+    data_list = []
+    
+    # 1. Generate Random Cluster Centers
+    # We multiply by 10 to ensure they are far apart in space
+    # Shape: (10, 5)
+    centers = np.random.randn(n_clusters, dim) * 10.0
+    
+    print(f"Generated {n_clusters} cluster centers in {dim}D space.")
+    print(f"Example Center 0: {np.round(centers[0], 2)}")
+    
+    for _ in range(n_samples):
+        seq = []
+        state = np.random.randint(0, n_clusters)
+        
+        t = 0
+        while t < seq_len:
+            # Random duration
+            dur = np.random.randint(10, 30)
+            
+            # 2. Generate Segment
+            # Shape: (Duration, Dim)
+            # Center[state] + Gaussian Noise
+            noise = np.random.randn(dur, dim) # Standard normal noise
+            segment = noise + centers[state]
+            seq.append(segment)
+            
+            # 3. Transition (No self-loops)
+            next_state = state
+            while next_state == state:
+                next_state = np.random.randint(0, n_clusters)
+            state = next_state
+            
+            t += dur
+            
+        full_seq = np.concatenate(seq)[:seq_len]
+        data_list.append(torch.tensor(full_seq, dtype=torch.float32))
+        
+    return data_list