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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 torch | |
| import random | |
| import common.layers as layers | |
| from common.utils import load_wav_to_torch, load_filepaths_and_text, to_gpu | |
| class MelAudioLoader(torch.utils.data.Dataset): | |
| """ | |
| 1) loads audio,text pairs | |
| 2) computes mel-spectrograms from audio files. | |
| """ | |
| def __init__(self, | |
| dataset_path, | |
| audiopaths_and_text, | |
| segment_length, | |
| n_mel_channels, | |
| max_wav_value, | |
| sampling_rate, | |
| filter_length, | |
| hop_length, | |
| win_length, | |
| mel_fmin, | |
| mel_fmax, | |
| args): | |
| self.audiopaths_and_text = load_filepaths_and_text(dataset_path, audiopaths_and_text) | |
| self.max_wav_value = max_wav_value | |
| self.sampling_rate = sampling_rate | |
| self.stft = layers.TacotronSTFT( | |
| filter_length, hop_length, win_length, | |
| n_mel_channels, sampling_rate, mel_fmin, | |
| mel_fmax) | |
| self.segment_length = segment_length | |
| random.seed(1234) | |
| random.shuffle(self.audiopaths_and_text) | |
| def get_mel_audio_pair(self, filename): | |
| audio, sampling_rate = load_wav_to_torch(filename) | |
| if sampling_rate != self.stft.sampling_rate: | |
| raise ValueError("{} {} SR doesn't match target {} SR".format( | |
| sampling_rate, self.stft.sampling_rate)) | |
| # Take segment | |
| if audio.size(0) >= self.segment_length: | |
| max_audio_start = audio.size(0) - self.segment_length | |
| audio_start = random.randint(0, max_audio_start) | |
| audio = audio[audio_start:audio_start+self.segment_length] | |
| else: | |
| audio = torch.nn.functional.pad( | |
| audio, (0, self.segment_length - audio.size(0)), 'constant').data | |
| audio = audio / self.max_wav_value | |
| audio_norm = audio.unsqueeze(0) | |
| audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False) | |
| melspec = self.stft.mel_spectrogram(audio_norm) | |
| melspec = melspec.squeeze(0) | |
| return (melspec, audio, len(audio)) | |
| def __getitem__(self, index): | |
| return self.get_mel_audio_pair(self.audiopaths_and_text[index][0]) | |
| def __len__(self): | |
| return len(self.audiopaths_and_text) | |
| def batch_to_gpu(batch): | |
| x, y, len_y = batch | |
| x = to_gpu(x).float() | |
| y = to_gpu(y).float() | |
| len_y = to_gpu(torch.sum(len_y)) | |
| return ((x, y), y, len_y) | |