1 files changed, 65 insertions, 0 deletions
diff --git a/dnn/torch/lpcnet/utils/misc.py b/dnn/torch/lpcnet/utils/misc.py
new file mode 100644
index 00000000..b295d6c4
--- /dev/null
+++ b/dnn/torch/lpcnet/utils/misc.py
@@ -0,0 +1,65 @@
+"""
+/* Copyright (c) 2023 Amazon
+   Written by Jan Buethe */
+/*
+   Redistribution and use in source and binary forms, with or without
+   modification, are permitted provided that the following conditions
+   are met:
+
+   - Redistributions of source code must retain the above copyright
+   notice, this list of conditions and the following disclaimer.
+
+   - Redistributions in binary form must reproduce the above copyright
+   notice, this list of conditions and the following disclaimer in the
+   documentation and/or other materials provided with the distribution.
+
+   THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+   ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+   LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+   A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER
+   OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+   EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+   PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+   PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+   LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+   NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+   SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+*/
+"""
+
+import torch
+
+
+def find(a, v):
+    try:
+        idx = a.index(v)
+    except:
+        idx = -1
+    return idx
+
+def interleave_tensors(tensors, dim=-2):
+    """ interleave list of tensors along sequence dimension """
+
+    x = torch.cat([x.unsqueeze(dim) for x in tensors], dim=dim)
+    x = torch.flatten(x, dim - 1, dim)
+
+    return x
+
+def _interleave(x, pcm_levels=256):
+
+    repeats = pcm_levels // (2*x.size(-1))
+    x = x.unsqueeze(-1)
+    p = torch.flatten(torch.repeat_interleave(torch.cat((x, 1 - x), dim=-1), repeats, dim=-1), -2)
+
+    return p
+
+def get_pdf_from_tree(x):
+    pcm_levels = x.size(-1)
+
+    p = _interleave(x[..., 1:2])
+    n = 4
+    while n <= pcm_levels:
+        p = p * _interleave(x[..., n//2:n])
+        n *= 2
+
+    return p
+\ No newline at end of file