89 lines
3.9 KiB
Python
89 lines
3.9 KiB
Python
# *****************************************************************************
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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" AND
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# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# 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 torch
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import random
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import common.layers as layers
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from common.utils import load_wav_to_torch, load_filepaths_and_text, to_gpu
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class MelAudioLoader(torch.utils.data.Dataset):
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"""
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1) loads audio,text pairs
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2) computes mel-spectrograms from audio files.
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"""
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def __init__(self, dataset_path, audiopaths_and_text, args):
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self.audiopaths_and_text = load_filepaths_and_text(dataset_path, audiopaths_and_text)
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self.max_wav_value = args.max_wav_value
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self.sampling_rate = args.sampling_rate
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self.stft = layers.TacotronSTFT(
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args.filter_length, args.hop_length, args.win_length,
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args.n_mel_channels, args.sampling_rate, args.mel_fmin,
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args.mel_fmax)
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self.segment_length = args.segment_length
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random.seed(1234)
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random.shuffle(self.audiopaths_and_text)
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def get_mel_audio_pair(self, filename):
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audio, sampling_rate = load_wav_to_torch(filename)
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if sampling_rate != self.stft.sampling_rate:
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raise ValueError("{} {} SR doesn't match target {} SR".format(
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sampling_rate, self.stft.sampling_rate))
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# Take segment
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if audio.size(0) >= self.segment_length:
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max_audio_start = audio.size(0) - self.segment_length
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audio_start = random.randint(0, max_audio_start)
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audio = audio[audio_start:audio_start+self.segment_length]
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else:
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audio = torch.nn.functional.pad(
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audio, (0, self.segment_length - audio.size(0)), 'constant').data
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audio = audio / self.max_wav_value
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audio_norm = audio.unsqueeze(0)
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audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
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melspec = self.stft.mel_spectrogram(audio_norm)
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melspec = melspec.squeeze(0)
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return (melspec, audio, len(audio))
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def __getitem__(self, index):
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return self.get_mel_audio_pair(self.audiopaths_and_text[index][0])
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def __len__(self):
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return len(self.audiopaths_and_text)
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def batch_to_gpu(batch):
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x, y, len_y = batch
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x = to_gpu(x).float()
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y = to_gpu(y).float()
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len_y = to_gpu(torch.sum(len_y))
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return ((x, y), y, len_y)
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