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# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# This source file is copied from https://github.com/facebookresearch/encodec
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""LSTM layers module."""
from torch import nn
class SLSTM(nn.Module):
"""
LSTM without worrying about the hidden state, nor the layout of the data.
Expects input as convolutional layout.
"""
def __init__(
self,
dimension: int,
num_layers: int = 2,
skip: bool = True,
bidirectional: bool = False,
):
super().__init__()
self.bidirectional = bidirectional
self.skip = skip
self.lstm = nn.LSTM(
dimension, dimension, num_layers, bidirectional=bidirectional
)
def forward(self, x):
x = x.permute(2, 0, 1)
y, _ = self.lstm(x)
if self.bidirectional:
x = x.repeat(1, 1, 2)
if self.skip:
y = y + x
y = y.permute(1, 2, 0)
return y
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