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# *****************************************************************************
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of the NVIDIA CORPORATION nor the
# names of its contributors may be used to endorse or promote products
# derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# *****************************************************************************
import os
from scipy.io.wavfile import write
import torch
from mel2samp import files_to_list, MAX_WAV_VALUE
from denoiser import Denoiser
def main(mel_files, waveglow_path, sigma, output_dir, sampling_rate, is_fp16,
denoiser_strength):
mel_files = files_to_list(mel_files)
waveglow = torch.load(waveglow_path)['model']
waveglow = waveglow.remove_weightnorm(waveglow)
waveglow.cuda().eval()
if is_fp16:
from apex import amp
waveglow, _ = amp.initialize(waveglow, [], opt_level="O3")
if denoiser_strength > 0:
denoiser = Denoiser(waveglow).cuda()
for i, file_path in enumerate(mel_files):
file_name = os.path.splitext(os.path.basename(file_path))[0]
mel = torch.load(file_path)
mel = torch.autograd.Variable(mel.cuda())
mel = torch.unsqueeze(mel, 0)
mel = mel.half() if is_fp16 else mel
with torch.no_grad():
audio = waveglow.infer(mel, sigma=sigma)
if denoiser_strength > 0:
audio = denoiser(audio, denoiser_strength)
audio = audio * MAX_WAV_VALUE
audio = audio.squeeze()
audio = audio.cpu().numpy()
audio = audio.astype('int16')
audio_path = os.path.join(
output_dir, "{}_synthesis.wav".format(file_name))
write(audio_path, sampling_rate, audio)
print(audio_path)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-f', "--filelist_path", required=True)
parser.add_argument('-w', '--waveglow_path',
help='Path to waveglow decoder checkpoint with model')
parser.add_argument('-o', "--output_dir", required=True)
parser.add_argument("-s", "--sigma", default=1.0, type=float)
parser.add_argument("--sampling_rate", default=22050, type=int)
parser.add_argument("--is_fp16", action="store_true")
parser.add_argument("-d", "--denoiser_strength", default=0.0, type=float,
help='Removes model bias. Start with 0.1 and adjust')
args = parser.parse_args()
main(args.filelist_path, args.waveglow_path, args.sigma, args.output_dir,
args.sampling_rate, args.is_fp16, args.denoiser_strength)
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