mirror of https://github.com/malarinv/tacotron2
data_utils.py: adding support for loading mel from disk
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2d41ea0682
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62d2c8b957
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@ -1,4 +1,5 @@
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import random
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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
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import torch.utils.data
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import torch.utils.data
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@ -19,6 +20,7 @@ class TextMelLoader(torch.utils.data.Dataset):
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self.text_cleaners = hparams.text_cleaners
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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.max_wav_value = hparams.max_wav_value
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self.sampling_rate = hparams.sampling_rate
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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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self.stft = layers.TacotronSTFT(
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hparams.filter_length, hparams.hop_length, hparams.win_length,
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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.n_mel_channels, hparams.sampling_rate, hparams.mel_fmin,
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@ -35,12 +37,19 @@ class TextMelLoader(torch.utils.data.Dataset):
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return (text, mel)
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return (text, mel)
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def get_mel(self, filename):
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def get_mel(self, filename):
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audio = load_wav_to_torch(filename, self.sampling_rate)
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if not self.load_mel_from_disk:
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audio_norm = audio / self.max_wav_value
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audio = load_wav_to_torch(filename, self.sampling_rate)
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audio_norm = audio_norm.unsqueeze(0)
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audio_norm = audio / self.max_wav_value
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audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
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audio_norm = audio_norm.unsqueeze(0)
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melspec = self.stft.mel_spectrogram(audio_norm)
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audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
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melspec = torch.squeeze(melspec, 0)
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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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return melspec
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def get_text(self, text):
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def get_text(self, text):
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