Speed up training by loading all audio in RAM.

This fixes descriptinc#19

mel2wav/dataset.py

@@ -36,10 +36,16 @@ def __init__(self, training_files, segment_length, sampling_rate, augment=True):
         random.shuffle(self.audio_files)
         self.augment = augment
 
+        # Load all audio files into memory
+        self.audio_data = []
+        for audio_file in self.audio_files:
+            audio, _ = self.load_wav_to_torch(audio_file)
+            self.audio_data.append(audio)
+
     def __getitem__(self, index):
-        # Read audio
-        filename = self.audio_files[index]
-        audio, sampling_rate = self.load_wav_to_torch(filename)
+        # Get audio from memory
+        audio = self.audio_data[index]
+
         # Take segment
         if audio.size(0) >= self.segment_length:
             max_audio_start = audio.size(0) - self.segment_length