73 lines
3.5 KiB
Python
73 lines
3.5 KiB
Python
# *****************************************************************************
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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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 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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# * Neither the name of the NVIDIA CORPORATION nor the
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# names of its contributors may be used to endorse or promote products
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# derived from this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
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# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
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# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
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# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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# (INCLUDING 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 sys
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sys.path.append('tacotron2')
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import torch
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from common.layers import STFT
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class Denoiser(torch.nn.Module):
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""" Removes model bias from audio produced with waveglow """
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def __init__(self, waveglow, cpu_run=False, filter_length=1024, n_overlap=4,
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win_length=1024, mode='zeros'):
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super(Denoiser, self).__init__()
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if cpu_run:
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self.stft = STFT(filter_length=filter_length,
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hop_length=int(filter_length/n_overlap),
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win_length=win_length)
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else:
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self.stft = STFT(filter_length=filter_length,
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hop_length=int(filter_length/n_overlap),
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win_length=win_length).cuda()
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if mode == 'zeros':
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mel_input = torch.zeros(
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(1, 80, 88),
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dtype=waveglow.upsample.weight.dtype,
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device=waveglow.upsample.weight.device)
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elif mode == 'normal':
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mel_input = torch.randn(
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(1, 80, 88),
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dtype=waveglow.upsample.weight.dtype,
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device=waveglow.upsample.weight.device)
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else:
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raise Exception("Mode {} if not supported".format(mode))
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with torch.no_grad():
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bias_audio = waveglow.infer(mel_input, sigma=0.0).float()
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bias_spec, _ = self.stft.transform(bias_audio)
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self.register_buffer('bias_spec', bias_spec[:, :, 0][:, :, None])
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def forward(self, audio, strength=0.1):
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audio_spec, audio_angles = self.stft.transform(audio.float())
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audio_spec_denoised = audio_spec - self.bias_spec * strength
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audio_spec_denoised = torch.clamp(audio_spec_denoised, 0.0)
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audio_denoised = self.stft.inverse(audio_spec_denoised, audio_angles)
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return audio_denoised
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