diff --git a/codes/scripts/audio/prep_music/generate_long_cheaters.py b/codes/scripts/audio/prep_music/generate_long_cheaters.py
index e59bb067..d96e191a 100644
--- a/codes/scripts/audio/prep_music/generate_long_cheaters.py
+++ b/codes/scripts/audio/prep_music/generate_long_cheaters.py
@@ -14,9 +14,9 @@ import numpy as np
 import torch
 from tqdm import tqdm
 
-from trainer.injectors.audio_injectors import MusicCheaterLatentInjector
+from trainer.injectors.audio_injectors import MusicCheaterLatentInjector, normalize_torch_mel, denormalize_mel
 
-from codes.models.audio.music.transformer_diffusion14 import get_cheater_encoder_v2
+from models.audio.music.transformer_diffusion14 import get_cheater_encoder_v2
 
 
 def report_progress(progress_file, file):
@@ -24,16 +24,14 @@ def report_progress(progress_file, file):
         f.write(f'{file}\n')
 
 
-model = get_cheater_encoder_v2().eval().cpu()
-model.load_state_dict(torch.load('../experiments/tfd14_cheater_encoder.pth', map_location=torch.device('cpu')))
-
-
-def process_folder(file, base_path, output_path, progress_file):
+def process_folder(file, model, 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)
-        global model
+        # Fix the normalization issues with the old mels. This should get reverted when these mels are re-generated.
+        mel = normalize_torch_mel(denormalize_mel(mel))
+        assert mel.min() > -1.001 and mel.max() < 1.001
         model = model.cuda()
         with torch.no_grad():
             cheater = model(mel)
@@ -46,7 +44,7 @@ if __name__ == '__main__':
     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)
+    parser.add_argument('--num_threads', type=int, help='Number of concurrent workers processing files (there must be a GPU per-worker.)', default=1)
     args = parser.parse_args()
 
     os.makedirs(args.output_path, exist_ok=True)
@@ -65,9 +63,21 @@ if __name__ == '__main__':
         torch.save(root_music_files, cache_path)
 
     orig_len = len(root_music_files)
-    folders = root_music_files - processed_files
+    folders = list(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)))
+    k = 0
+    for k in range(args.num_threads-1):
+        if os.fork() == 0:
+            break
+
+    # k is now the process number.
+    partition_len = (len(folders)//args.num_threads)+1
+    folders = folders[k*partition_len:(k+1)*partition_len]
+
+    model = get_cheater_encoder_v2().eval().cpu()
+    model.load_state_dict(torch.load('../experiments/tfd14_cheater_encoder.pth', map_location=torch.device('cpu')))
+    model = model.to(f'cuda:{k}')
+
+    for folder in tqdm(folders):
+        process_folder(folder, model=model, output_path=args.output_path, base_path=args.path, progress_file=args.progress_file)