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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 json
import torch
from hifigan_models import Generator
from hifigan_env import AttrDict
from hifigan_denoiser import Denoiser
def lines_to_list(filename):
"""
Takes a text file of filenames and makes a list of filenames
"""
with open(filename, encoding="utf-8") as f:
files = f.readlines()
files = [f.rstrip() for f in files]
return files
def load_vocoder(vocoder_path, config_path, to_cuda=False):
with open(config_path) as f:
data_vocoder = f.read()
config_vocoder = json.loads(data_vocoder)
h = AttrDict(config_vocoder)
if "blur" in vocoder_path:
config_vocoder["gaussian_blur"]["p_blurring"] = 0.5
else:
if "gaussian_blur" in config_vocoder:
config_vocoder["gaussian_blur"]["p_blurring"] = 0.0
else:
config_vocoder["gaussian_blur"] = {"p_blurring": 0.0}
h["gaussian_blur"] = {"p_blurring": 0.0}
state_dict_g = torch.load(vocoder_path, map_location="cpu")["generator"]
# load hifigan
vocoder = Generator(h)
vocoder.load_state_dict(state_dict_g)
denoiser = Denoiser(vocoder)
if to_cuda:
vocoder.cuda()
denoiser.cuda()
vocoder.eval()
denoiser.eval()
return vocoder, denoiser
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