diff --git a/codes/scripts/audio/prep_music/generate_long_mels.py b/codes/scripts/audio/prep_music/generate_long_mels.py
new file mode 100644
index 00000000..00a74075
--- /dev/null
+++ b/codes/scripts/audio/prep_music/generate_long_mels.py
@@ -0,0 +1,97 @@
+
+
+"""
+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 torch
+import torchaudio
+import numpy as np
+from tqdm import tqdm
+
+from data.util import find_audio_files, find_files_of_type
+from trainer.injectors.audio_injectors import TorchMelSpectrogramInjector
+from utils.util import load_audio
+
+
+def report_progress(progress_file, file):
+    with open(progress_file, 'a', encoding='utf-8') as f:
+        f.write(f'{file}\n')
+
+
+spec_fn = TorchMelSpectrogramInjector({'n_mel_channels': 256, 'mel_fmax': 11000, 'filter_length': 16000,
+                                       'true_normalization': True, 'normalize': True, 'in': 'in', 'out': 'out'}, {}).cuda()
+
+def produce_mel(audio):
+    return spec_fn({'in': audio.unsqueeze(0)})['out'].squeeze(0)
+
+
+def process_folder(folder, base_path, output_path, progress_file, max_duration, sampling_rate=22050):
+    outdir = os.path.join(output_path, f'{os.path.relpath(folder, base_path)}')
+    os.makedirs(outdir, exist_ok=True)
+
+    files = list(os.listdir(folder))
+    i = 0
+    output_i = 0
+    while i < len(files):
+        last_ordinal = -1
+        total_progress = 0
+        to_combine = []
+        while i < len(files) and total_progress < max_duration:
+            audio_file = os.path.join(folder, files[i], "no_vocals.wav")
+            if not os.path.exists(audio_file):
+                break
+            to_combine.append(load_audio(audio_file, 22050))
+            file_ordinal = int(files[i])
+            if last_ordinal != -1 and file_ordinal != last_ordinal+1:
+                break
+            else:
+                i += 1
+                total_progress += 30
+        if total_progress > 30:
+            combined = torch.cat(to_combine, dim=-1).cuda()
+            mel = produce_mel(combined).cpu().numpy()
+            np.savez(os.path.join(outdir, f'{output_i}'), mel)
+            output_i += 1
+    report_progress(progress_file, folder)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--path', type=str, help='Path to search for files', default='Y:\\separated')
+    parser.add_argument('--progress_file', type=str, help='Place to store all files that have already been processed', default='Y:\\separated\\large_mels\\already_processed.txt')
+    parser.add_argument('--output_path', type=str, help='Path for output files', default='Y:\\separated\\large_mels')
+    parser.add_argument('--num_threads', type=int, help='Number of concurrent workers processing files.', default=3)
+    parser.add_argument('--max_duration', type=int, help='Duration per clip in seconds', default=120)
+    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)
+        def collect(p):
+            return str(os.path.dirname(os.path.dirname(p)))
+        root_music_files = {collect(p) for p in path.rglob("*no_vocals.wav")}
+        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, max_duration=args.max_duration), folders), total=len(folders)))