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import random |
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import numpy as np |
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import torch |
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import torch.utils.data |
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import layers |
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from utils import load_wav_to_torch, load_filepaths_and_text |
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from text import text_to_sequence |
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class TextMelLoader(torch.utils.data.Dataset): |
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""" |
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1) loads audio,text pairs |
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2) normalizes text and converts them to sequences of one-hot vectors |
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3) computes mel-spectrograms from audio files. |
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""" |
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def __init__(self, audiopaths_and_text, hparams): |
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self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text) |
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self.text_cleaners = hparams.text_cleaners |
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self.max_wav_value = hparams.max_wav_value |
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self.sampling_rate = hparams.sampling_rate |
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self.load_mel_from_disk = hparams.load_mel_from_disk |
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self.stft = layers.TacotronSTFT( |
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hparams.filter_length, hparams.hop_length, hparams.win_length, |
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hparams.n_mel_channels, hparams.sampling_rate, hparams.mel_fmin, |
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hparams.mel_fmax) |
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random.seed(hparams.seed) |
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random.shuffle(self.audiopaths_and_text) |
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def get_mel_text_pair(self, audiopath_and_text): |
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audiopath, text = audiopath_and_text[0], audiopath_and_text[1] |
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text = self.get_text(text) |
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mel = self.get_mel(audiopath) |
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return (text, mel) |
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def get_mel(self, filename): |
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if not self.load_mel_from_disk: |
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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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audio_norm = audio / self.max_wav_value |
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audio_norm = audio_norm.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 = torch.squeeze(melspec, 0) |
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else: |
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melspec = torch.from_numpy(np.load(filename)) |
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assert melspec.size(0) == self.stft.n_mel_channels, ( |
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'Mel dimension mismatch: given {}, expected {}'.format( |
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melspec.size(0), self.stft.n_mel_channels)) |
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return melspec |
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def get_text(self, text): |
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text_norm = torch.IntTensor(text_to_sequence(text, self.text_cleaners)) |
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return text_norm |
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def __getitem__(self, index): |
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return self.get_mel_text_pair(self.audiopaths_and_text[index]) |
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def __len__(self): |
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return len(self.audiopaths_and_text) |
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class TextMelCollate(): |
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""" Zero-pads model inputs and targets based on number of frames per setep |
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""" |
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def __init__(self, n_frames_per_step): |
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self.n_frames_per_step = n_frames_per_step |
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def __call__(self, batch): |
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"""Collate's training batch from normalized text and mel-spectrogram |
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PARAMS |
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------ |
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batch: [text_normalized, mel_normalized] |
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""" |
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input_lengths, ids_sorted_decreasing = torch.sort( |
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torch.LongTensor([len(x[0]) for x in batch]), |
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dim=0, descending=True) |
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max_input_len = input_lengths[0] |
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text_padded = torch.LongTensor(len(batch), max_input_len) |
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text_padded.zero_() |
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for i in range(len(ids_sorted_decreasing)): |
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text = batch[ids_sorted_decreasing[i]][0] |
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text_padded[i, :text.size(0)] = text |
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num_mels = batch[0][1].size(0) |
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max_target_len = max([x[1].size(1) for x in batch]) |
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if max_target_len % self.n_frames_per_step != 0: |
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max_target_len += self.n_frames_per_step - max_target_len % self.n_frames_per_step |
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assert max_target_len % self.n_frames_per_step == 0 |
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mel_padded = torch.FloatTensor(len(batch), num_mels, max_target_len) |
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mel_padded.zero_() |
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gate_padded = torch.FloatTensor(len(batch), max_target_len) |
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gate_padded.zero_() |
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output_lengths = torch.LongTensor(len(batch)) |
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for i in range(len(ids_sorted_decreasing)): |
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mel = batch[ids_sorted_decreasing[i]][1] |
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mel_padded[i, :, :mel.size(1)] = mel |
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gate_padded[i, mel.size(1)-1:] = 1 |
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output_lengths[i] = mel.size(1) |
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return text_padded, input_lengths, mel_padded, gate_padded, \ |
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output_lengths |
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