mirror of
https://github.com/xiph/opus.git
synced 2025-05-17 08:58:30 +00:00
71 lines
2.6 KiB
Python
71 lines
2.6 KiB
Python
"""
|
|
/* 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.
|
|
*/
|
|
"""
|
|
|
|
""" 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
|