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- # We retain the copyright notice by NVIDIA from the original code. However, we
- # we reserve our rights on the modifications based on the original code.
- #
- # *****************************************************************************
- # 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, squeezewave_path, sigma, output_dir, sampling_rate, is_fp16,
- denoiser_strength):
- mel_files = files_to_list(mel_files)
- squeezewave = torch.load(squeezewave_path)['model']
- squeezewave = squeezewave.remove_weightnorm(squeezewave)
- squeezewave.cuda().eval()
- if is_fp16:
- from apex import amp
- squeezewave, _ = amp.initialize(squeezewave, [], opt_level="O3")
-
- if denoiser_strength > 0:
- denoiser = Denoiser(squeezewave).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 = squeezewave.infer(mel, sigma=sigma).float()
- 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', '--squeezewave_path',
- help='Path to squeezewave 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.squeezewave_path, args.sigma, args.output_dir,
- args.sampling_rate, args.is_fp16, args.denoiser_strength)
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