diff --git a/codes/data/audio/nv_tacotron_dataset.py b/codes/data/audio/nv_tacotron_dataset.py
index 98f085af..7dadb7ad 100644
--- a/codes/data/audio/nv_tacotron_dataset.py
+++ b/codes/data/audio/nv_tacotron_dataset.py
@@ -51,7 +51,7 @@ 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.load_mel_from_disk = opt_get(hparams, ['load_mel_from_disk'], False)
         self.return_wavs = opt_get(hparams, ['return_wavs'], False)
         self.input_sample_rate = opt_get(hparams, ['input_sample_rate'], self.sampling_rate)
         assert not (self.load_mel_from_disk and self.return_wavs)
@@ -76,7 +76,11 @@ class TextMelLoader(torch.utils.data.Dataset):
         return (text_seq, mel, text, audiopath_and_text[0])
 
     def get_mel(self, filename):
-        if not self.load_mel_from_disk:
+        if self.load_mel_from_disk and os.path.exists(f'{filename}_mel.npy'):
+            melspec = torch.from_numpy(np.load(f'{filename}_mel.npy'))
+            assert melspec.size(0) == self.stft.n_mel_channels, (
+                'Mel dimension mismatch: given {}, expected {}'.format(melspec.size(0), self.stft.n_mel_channels))
+        else:
             if filename.endswith('.wav'):
                 audio, sampling_rate = load_wav_to_torch(filename)
                 audio = (audio / self.max_wav_value)
@@ -105,9 +109,6 @@ class TextMelLoader(torch.utils.data.Dataset):
                 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
@@ -209,10 +210,6 @@ def save_mel_buffer_to_file(mel, path):
     np.save(path, mel.numpy())
 
 
-def load_mel_buffer_from_file(path):
-    return torch.tensor(np.load(path))
-
-
 def dump_mels_to_disk():
     params = {
         'mode': 'nv_tacotron',
diff --git a/codes/scripts/audio/random_mp3_splitter.py b/codes/scripts/audio/random_mp3_splitter.py
index 758bf6d0..1c0e278c 100644
--- a/codes/scripts/audio/random_mp3_splitter.py
+++ b/codes/scripts/audio/random_mp3_splitter.py
@@ -9,11 +9,11 @@ import numpy as np
 
 
 if __name__ == '__main__':
-    src_dir = 'O:\\podcast_dumps'
+    src_dir = 'P:\\Audiobooks-Podcasts'
     #src_dir = 'E:\\audio\\books'
-    output_dir = 'D:\\data\\audio\\podcasts-split'
-    output_dir_lq = 'D:\\data\\audio\\podcasts-split-with-bg'
-    output_dir_garbage = 'D:\\data\\audio\\podcasts-split-garbage'
+    output_dir = 'D:\\data\\audio\\misc-split'
+    output_dir_lq = 'D:\\data\\audio\\misc-split-with-bg'
+    output_dir_garbage = 'D:\\data\\audio\\misc-split-garbage'
     #output_dir = 'E:\\audio\\books-clips'
     clip_length = 5  # In seconds
     sparsity = .1  # Only this proportion of the total clips are extracted as wavs.