added LPCNet torch implementation

Signed-off-by: Jan Buethe <jbuethe@amazon.de>

dnn/torch/lpcnet/utils/layers/pcm_embeddings.py

@@ -0,0 +1,42 @@
+""" module implementing PCM embeddings for LPCNet """
+
+import math as m
+
+import torch
+from torch import nn
+
+
+class PCMEmbedding(nn.Module):
+    def __init__(self, embed_dim=128, num_levels=256):
+        super(PCMEmbedding, self).__init__()
+
+        self.embed_dim  = embed_dim
+        self.num_levels = num_levels
+
+        self.embedding = nn.Embedding(self.num_levels, self.num_dim)
+
+        # initialize
+        with torch.no_grad():
+            num_rows, num_cols = self.num_levels, self.embed_dim
+            a = m.sqrt(12) * (torch.rand(num_rows, num_cols) - 0.5)
+            for i in range(num_rows):
+                a[i, :] += m.sqrt(12) * (i -  num_rows / 2)
+            self.embedding.weight[:, :] = 0.1 * a
+
+    def forward(self, x):
+        return self.embeddint(x)
+
+
+class DifferentiablePCMEmbedding(PCMEmbedding):
+    def __init__(self, embed_dim, num_levels=256):
+        super(DifferentiablePCMEmbedding, self).__init__(embed_dim, num_levels)
+
+    def forward(self, x):
+        x_int = (x - torch.floor(x)).detach().long()
+        x_frac = x - x_int
+        x_next = torch.minimum(x_int + 1, self.num_levels)
+
+        embed_0 = self.embedding(x_int)
+        embed_1 = self.embedding(x_next)
+
+        return (1 - x_frac) * embed_0 + x_frac * embed_1