diff --git a/codes/data/audio/wavfile_dataset.py b/codes/data/audio/wavfile_dataset.py
index d8c73415..f77ac7a5 100644
--- a/codes/data/audio/wavfile_dataset.py
+++ b/codes/data/audio/wavfile_dataset.py
@@ -15,13 +15,17 @@ from utils.util import opt_get
 class WavfileDataset(torch.utils.data.Dataset):
 
     def __init__(self, opt):
+        cache_path = opt_get(opt, ['cache_path'], os.path.join(self.path, 'cache.pth'))  # Will fail when multiple paths specified, must be specified in this case.
         self.path = os.path.dirname(opt['path'])
-        cache_path = os.path.join(self.path, 'cache.pth')
+        if not isinstance(self.path, list):
+            self.path = [self.path]
         if os.path.exists(cache_path):
             self.audiopaths = torch.load(cache_path)
         else:
             print("Building cache..")
-            self.audiopaths = find_files_of_type('img', opt['path'], qualifier=is_wav_file)[0]
+            self.audiopaths = []
+            for p in self.path:
+                self.audiopaths.extend(find_files_of_type('img', p, qualifier=is_wav_file)[0])
             torch.save(self.audiopaths, cache_path)
 
         # Parse options
diff --git a/codes/scripts/audio/random_mp3_splitter.py b/codes/scripts/audio/random_mp3_splitter.py
index 9170f1b6..01f52638 100644
--- a/codes/scripts/audio/random_mp3_splitter.py
+++ b/codes/scripts/audio/random_mp3_splitter.py
@@ -23,7 +23,7 @@ if __name__ == '__main__':
     separator = Separator('spleeter:2stems')
     files = find_audio_files(src_dir, include_nonwav=True)
     for e, file in enumerate(tqdm(files)):
-        if e < 575:
+        if e < 3055:
             continue
         file_basis = osp.relpath(file, src_dir)\
             .replace('/', '_')\
@@ -56,7 +56,7 @@ if __name__ == '__main__':
             bmax = np.abs(bg).mean()
 
             # Only output to the "good" sample dir if the ratio of background noise to vocal noise is high enough.
-            ratio = vmax / bmax
+            ratio = vmax / (bmax+.0000001)
             if ratio >= 25:  # These values were derived empirically
                 od = output_dir
                 os = clip