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# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: MIT
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
import torch
from audio_processing import STFT
class Denoiser(torch.nn.Module):
"""Removes model bias from audio produced with hifigan"""
def __init__(
self, hifigan, filter_length=1024, n_overlap=4, win_length=1024, mode="zeros"
):
super(Denoiser, self).__init__()
self.stft = STFT(
filter_length=filter_length,
hop_length=int(filter_length / n_overlap),
win_length=win_length,
)
self.stft = self.stft.to(hifigan.ups[0].weight.device)
if mode == "zeros":
mel_input = torch.zeros(
(1, 80, 88),
dtype=hifigan.ups[0].weight.dtype,
device=hifigan.ups[0].weight.device,
)
elif mode == "normal":
mel_input = torch.randn(
(1, 80, 88),
dtype=hifigan.upsample.weight.dtype,
device=hifigan.upsample.weight.device,
)
else:
raise Exception("Mode {} if not supported".format(mode))
with torch.no_grad():
bias_audio = hifigan(mel_input).float()[0]
bias_spec, _ = self.stft.transform(bias_audio)
self.register_buffer("bias_spec", bias_spec[:, :, 0][:, :, None])
def forward(self, audio, strength=0.1):
audio_spec, audio_angles = self.stft.transform(audio.float())
audio_spec_denoised = audio_spec - self.bias_spec * strength
audio_spec_denoised = torch.clamp(audio_spec_denoised, 0.0)
audio_denoised = self.stft.inverse(audio_spec_denoised, audio_angles)
return audio_denoised
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