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- import numpy as np
- def build_LFR_features(data, m, n):
- """
- Actually, this implements stacking frames and skipping frames.
- if m = 1 and n = 1, just return the origin features.
- if m = 1 and n > 1, it works like skipping.
- if m > 1 and n = 1, it works like stacking but only support right frames.
- if m > 1 and n > 1, it works like LFR.
- Args:
- inputs_batch: inputs is T x D np.ndarray
- m: number of frames to stack
- n: number of frames to skip
- """
- LFR_inputs = []
- T = data.shape[0]
- T_lfr = int(np.ceil(T / n))
- for i in range(T_lfr):
- if m <= T - i * n:
- LFR_inputs.append(np.hstack(data[i*n:i*n+m]))
- else:
- num_padding = m - (T - i * n)
- frame = np.hstack(data[i*n:])
- for _ in range(num_padding):
- frame = np.hstack((frame, data[-1]))
- LFR_inputs.append(frame)
- return np.vstack(LFR_inputs)
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