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69 lines
No EOL
2.5 KiB
Python
69 lines
No EOL
2.5 KiB
Python
"""
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/* Copyright (c) 2023 Amazon
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Written by Jan Buethe */
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/*
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions
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are met:
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- Redistributions of source code must retain the above copyright
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notice, this list of conditions and the following disclaimer.
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- Redistributions in binary form must reproduce the above copyright
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notice, this list of conditions and the following disclaimer in the
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documentation and/or other materials provided with the distribution.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER
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OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
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LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
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NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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"""
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import torch
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from torch import nn
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import torch.nn.functional as F
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class NNSBase(nn.Module):
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def __init__(self, skip=91, preemph=0.85):
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super().__init__()
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self.skip = skip
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self.preemph = preemph
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def process(self, sig, features, periods, numbits, debug=False):
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self.eval()
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has_numbits = 'numbits' in self.forward.__code__.co_varnames
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device = next(iter(self.parameters())).device
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with torch.no_grad():
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# run model
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x = sig.view(1, 1, -1).to(device)
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f = features.unsqueeze(0).to(device)
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p = periods.unsqueeze(0).to(device)
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n = numbits.unsqueeze(0).to(device)
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if has_numbits:
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y = self.forward(x, f, p, n, debug=debug).squeeze()
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else:
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y = self.forward(x, f, p, debug=debug).squeeze()
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# deemphasis
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if self.preemph > 0:
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for i in range(len(y) - 1):
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y[i + 1] += self.preemph * y[i]
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# delay compensation
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y = torch.cat((y[self.skip:], torch.zeros(self.skip, dtype=y.dtype, device=y.device)))
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out = torch.clip((2**15) * y, -2**15, 2**15 - 1).short()
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return out |