# ***************************************************************************** # 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 torch.utils.data import common.layers as layers from common.utils import load_wav_to_torch, load_filepaths_and_text, to_gpu class TextMelLoader(torch.utils.data.Dataset): """ 1) loads audio,text pairs 2) normalizes text and converts them to sequences of one-hot vectors 3) computes mel-spectrograms from audio files. """ def __init__( self, dataset_path, audiopaths_and_text, text_cleaners, n_mel_channels, symbol_set="english_basic", n_speakers=1, load_mel_from_disk=True, max_wav_value=None, sampling_rate=None, filter_length=None, hop_length=None, win_length=None, mel_fmin=None, mel_fmax=None, **ignored ): self.audiopaths_and_text = load_filepaths_and_text( dataset_path, audiopaths_and_text, has_speakers=(n_speakers > 1) ) self.length = len(self.audiopaths_and_text) self.load_mel_from_disk = load_mel_from_disk if not load_mel_from_disk: 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 ) def get_mel(self, filename): if not self.load_mel_from_disk: 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)) audio_norm = audio / self.max_wav_value audio_norm = audio_norm.unsqueeze(0) audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False) melspec = self.stft.mel_spectrogram(audio_norm) melspec = torch.squeeze(melspec, 0) else: melspec = torch.load(filename) # assert melspec.size(0) == self.stft.n_mel_channels, ( # 'Mel dimension mismatch: given {}, expected {}'.format( # melspec.size(0), self.stft.n_mel_channels)) return melspec def get_text(self, text): text_encoded = torch.IntTensor(self.tp.encode_text(text)) return text_encoded def __getitem__(self, index): # separate filename and text audiopath, text = self.audiopaths_and_text[index] text = self.get_text(text) len_text = len(text) mel = self.get_mel(audiopath) return (text, mel, len_text) def __len__(self): return self.length class TextMelCollate: """Zero-pads model inputs and targets based on number of frames per step""" def __init__(self, n_frames_per_step): self.n_frames_per_step = n_frames_per_step def __call__(self, batch): """Collate's training batch from normalized text and mel-spectrogram PARAMS ------ batch: [text_normalized, mel_normalized] """ # Right zero-pad all one-hot text sequences to max input length input_lengths, ids_sorted_decreasing = torch.sort( torch.LongTensor([len(x[0]) for x in batch]), dim=0, descending=True ) max_input_len = input_lengths[0] text_padded = torch.LongTensor(len(batch), max_input_len) text_padded.zero_() for i in range(len(ids_sorted_decreasing)): text = batch[ids_sorted_decreasing[i]][0] text_padded[i, : text.size(0)] = text # Right zero-pad mel-spec num_mels = batch[0][1].size(0) max_target_len = max([x[1].size(1) for x in batch]) if max_target_len % self.n_frames_per_step != 0: max_target_len += self.n_frames_per_step - max_target_len % self.n_frames_per_step assert max_target_len % self.n_frames_per_step == 0 # include mel padded and gate padded mel_padded = torch.FloatTensor(len(batch), num_mels, max_target_len) mel_padded.zero_() gate_padded = torch.FloatTensor(len(batch), max_target_len) gate_padded.zero_() output_lengths = torch.LongTensor(len(batch)) for i in range(len(ids_sorted_decreasing)): mel = batch[ids_sorted_decreasing[i]][1] mel_padded[i, :, : mel.size(1)] = mel gate_padded[i, mel.size(1) - 1 :] = 1 output_lengths[i] = mel.size(1) # count number of items - characters in text len_x = [x[2] for x in batch] len_x = torch.Tensor(len_x) # Return any extra fields as sorted lists num_fields = len(batch[0]) extra_fields = tuple([batch[i][f] for i in ids_sorted_decreasing] for f in range(3, num_fields)) return (text_padded, input_lengths, mel_padded, gate_padded, output_lengths, len_x) + extra_fields def batch_to_gpu(batch): text_padded, input_lengths, mel_padded, gate_padded, output_lengths, len_x = batch text_padded = to_gpu(text_padded).long() input_lengths = to_gpu(input_lengths).long() max_len = torch.max(input_lengths.data).item() mel_padded = to_gpu(mel_padded).float() gate_padded = to_gpu(gate_padded).float() output_lengths = to_gpu(output_lengths).long() x = (text_padded, input_lengths, mel_padded, max_len, output_lengths) y = (mel_padded, gate_padded) len_x = torch.sum(output_lengths) return (x, y, len_x)