diff --git a/codes/data/audio/preprocessed_mel_dataset.py b/codes/data/audio/preprocessed_mel_dataset.py
index 8667ed88..a88e704e 100644
--- a/codes/data/audio/preprocessed_mel_dataset.py
+++ b/codes/data/audio/preprocessed_mel_dataset.py
@@ -25,11 +25,14 @@ class PreprocessedMelDataset(torch.utils.data.Dataset):
             self.paths = [str(p) for p in path.rglob("*.npz")]
             torch.save(self.paths, cache_path)
         self.pad_to = opt_get(opt, ['pad_to_samples'], 10336)
+        self.squeeze = opt_get(opt, ['should_squeeze'], False)
 
     def __getitem__(self, index):
         with np.load(self.paths[index]) as npz_file:
             mel = torch.tensor(npz_file['arr_0'])
         assert mel.shape[-1] <= self.pad_to
+        if self.squeeze:
+            mel = mel.squeeze()
         padding_needed = self.pad_to - mel.shape[-1]
         mask = torch.zeros_like(mel)
         if padding_needed > 0:
@@ -52,9 +55,9 @@ class PreprocessedMelDataset(torch.utils.data.Dataset):
 if __name__ == '__main__':
     params = {
         'mode': 'preprocessed_mel',
-        'path': 'Y:\\separated\\large_mels',
-        'cache_path': 'Y:\\separated\\large_mels.pth',
-        'pad_to_samples': 10336,
+        'path': 'Y:\\separated\\large_mel_cheaters',
+        'cache_path': 'Y:\\separated\\large_mel_cheaters_win.pth',
+        'pad_to_samples': 646,
         'phase': 'train',
         'n_workers': 0,
         'batch_size': 16,
diff --git a/codes/scripts/audio/prep_music/generate_long_cheaters.py b/codes/scripts/audio/prep_music/generate_long_cheaters.py
new file mode 100644
index 00000000..2ee11f6a
--- /dev/null
+++ b/codes/scripts/audio/prep_music/generate_long_cheaters.py
@@ -0,0 +1,67 @@
+
+
+"""
+Master script that processes all MP3 files found in an input directory. Splits those files up into sub-files of a
+predetermined duration.
+"""
+import argparse
+import functools
+import os
+from multiprocessing.pool import ThreadPool
+from pathlib import Path
+
+import numpy as np
+import torch
+from tqdm import tqdm
+
+from trainer.injectors.audio_injectors import MusicCheaterLatentInjector
+
+
+def report_progress(progress_file, file):
+    with open(progress_file, 'a', encoding='utf-8') as f:
+        f.write(f'{file}\n')
+
+
+cheater_inj = MusicCheaterLatentInjector({'in': 'in', 'out': 'out'}, {})
+
+
+def process_folder(file, base_path, output_path, progress_file):
+    outdir = os.path.join(output_path, f'{os.path.relpath(os.path.dirname(file), base_path)}')
+    os.makedirs(outdir, exist_ok=True)
+    with np.load(file) as npz_file:
+        mel = torch.tensor(npz_file['arr_0']).cuda().unsqueeze(0)
+        cheater = cheater_inj({'in': mel})['out']
+        np.savez(os.path.join(outdir, os.path.basename(file)), cheater.cpu().numpy())
+    report_progress(progress_file, file)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--path', type=str, help='Path to search for files', default='Y:\\separated\\large_mels')
+    parser.add_argument('--progress_file', type=str, help='Place to store all files that have already been processed', default='Y:\\separated\\large_mel_cheaters\\already_processed.txt')
+    parser.add_argument('--output_path', type=str, help='Path for output files', default='Y:\\separated\\large_mel_cheaters')
+    parser.add_argument('--num_threads', type=int, help='Number of concurrent workers processing files.', default=1)
+    args = parser.parse_args()
+
+    os.makedirs(args.output_path, exist_ok=True)
+    processed_files = set()
+    if os.path.exists(args.progress_file):
+        with open(args.progress_file, 'r', encoding='utf-8') as f:
+            for line in f.readlines():
+                processed_files.add(line.strip())
+
+    cache_path = os.path.join(args.output_path, 'cache.pth')
+    if os.path.exists(cache_path):
+        root_music_files = torch.load(cache_path)
+    else:
+        path = Path(args.path)
+        root_music_files = set(path.rglob("*.npz"))
+        torch.save(root_music_files, cache_path)
+
+    orig_len = len(root_music_files)
+    folders = root_music_files - processed_files
+    print(f"Found {len(folders)} files to process. Total processing is {100 * (orig_len - len(folders)) / orig_len}% complete.")
+
+    with ThreadPool(args.num_threads) as pool:
+        list(tqdm(pool.imap(functools.partial(process_folder, output_path=args.output_path, base_path=args.path,
+                                              progress_file=args.progress_file), folders), total=len(folders)))