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