ref: 35ee397e060283d30c098ae5e17836316bbec08b
dir: /dnn/torch/lpcnet/utils/layers/pcm_embeddings.py/
""" 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