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