Fork of https://github.com/alokprasad/fastspeech_squeezewave to also fix denoising in squeezewave
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57 lines
2.6 KiB
57 lines
2.6 KiB
# *****************************************************************************
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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"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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# ARE 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 os
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from scipy.io.wavfile import write
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import torch
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from waveglow.mel2samp import files_to_list, MAX_WAV_VALUE
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# from denoiser import Denoiser
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def inference(mel, waveglow, audio_path, sigma=1.0, sampling_rate=22050):
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with torch.no_grad():
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audio = waveglow.infer(mel, sigma=sigma)
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audio = audio * MAX_WAV_VALUE
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audio = audio.squeeze()
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audio = audio.cpu().numpy()
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audio = audio.astype('int16')
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write(audio_path, sampling_rate, audio)
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def test_speed(mel, waveglow, sigma=1.0, sampling_rate=22050):
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with torch.no_grad():
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audio = waveglow.infer(mel, sigma=sigma)
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audio = audio * MAX_WAV_VALUE
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def get_wav(mel, waveglow, sigma=1.0, sampling_rate=22050):
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with torch.no_grad():
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audio = waveglow.infer(mel, sigma=sigma)
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audio = audio * MAX_WAV_VALUE
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audio = audio.squeeze()
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audio = audio.cpu()
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return audio
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