data_utils.py: adding support for loading mel from disk

data_utils.py

@@ -1,4 +1,5 @@
 import random
+import numpy as np
 import torch
 import torch.utils.data
 
@@ -19,6 +20,7 @@ class TextMelLoader(torch.utils.data.Dataset):
         self.text_cleaners = hparams.text_cleaners
         self.max_wav_value = hparams.max_wav_value
         self.sampling_rate = hparams.sampling_rate
+        self.load_mel_from_disk = hparams.load_mel_from_disk
         self.stft = layers.TacotronSTFT(
             hparams.filter_length, hparams.hop_length, hparams.win_length,
             hparams.n_mel_channels, hparams.sampling_rate, hparams.mel_fmin,
@@ -35,12 +37,19 @@ class TextMelLoader(torch.utils.data.Dataset):
         return (text, mel)
 
     def get_mel(self, filename):
-        audio = load_wav_to_torch(filename, self.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)
+        if not self.load_mel_from_disk:
+            audio = load_wav_to_torch(filename, self.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.from_numpy(np.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):