add files

funasr_local/datasets/large_datasets/utils/low_frame_rate.py

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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)