diff --git a/codes/data/audio/unsupervised_audio_dataset.py b/codes/data/audio/unsupervised_audio_dataset.py
index f4d2fedb..7808033e 100644
--- a/codes/data/audio/unsupervised_audio_dataset.py
+++ b/codes/data/audio/unsupervised_audio_dataset.py
@@ -111,6 +111,7 @@ class UnsupervisedAudioDataset(torch.utils.data.Dataset):
             self.pad_to *= self.sampling_rate
         self.pad_to = opt_get(opt, ['pad_to_samples'], self.pad_to)
         self.min_length = opt_get(opt, ['min_length'], 0)
+        self.dont_clip = opt_get(opt, ['dont_clip'], False)
 
         # "Resampled clip" is audio data pulled from the basis of "clip" but with randomly different bounds. There are no
         # guarantees that "clip_resampled" is different from "clip": in fact, if "clip" is less than pad_to_seconds/samples,
@@ -126,6 +127,8 @@ class UnsupervisedAudioDataset(torch.utils.data.Dataset):
         audiopath = self.audiopaths[index]
         audio = load_audio(audiopath, self.sampling_rate)
         assert audio.shape[1] > self.min_length
+        if self.dont_clip:
+            assert audio.shape[1] <= self.pad_to
         return audio, audiopath
 
     def get_related_audio_for_index(self, index):
diff --git a/codes/scripts/audio/preparation/gen_dvae_codes.py b/codes/scripts/audio/preparation/gen_dvae_codes.py
new file mode 100644
index 00000000..c9de4e85
--- /dev/null
+++ b/codes/scripts/audio/preparation/gen_dvae_codes.py
@@ -0,0 +1,41 @@
+import os
+
+import torch
+from tqdm import tqdm
+
+from scripts.audio.gen.speech_synthesis_utils import load_speech_dvae, wav_to_mel
+
+if __name__ == '__main__':
+    input_folder = 'C:\\Users\\James\\Downloads\\lex2\\lexfridman_training_mp3'
+    output_folder = 'C:\\Users\\James\\Downloads\\lex2\\quantized'
+
+    params = {
+        'mode': 'unsupervised_audio',
+        'path': [input_folder],
+        'cache_path': f'{input_folder}/cache.pth',
+        'sampling_rate': 22050,
+        'pad_to_samples': 441000,
+        'resample_clip': False,
+        'extra_samples': 0,
+        'phase': 'train',
+        'n_workers': 2,
+        'batch_size': 64,
+    }
+    from data import create_dataset, create_dataloader
+    os.makedirs(output_folder, exist_ok=True)
+
+    ds = create_dataset(params)
+    dl = create_dataloader(ds, params)
+
+    dvae = load_speech_dvae().cuda()
+    with torch.no_grad():
+        for batch in tqdm(dl):
+            audio = batch['clip'].cuda()
+            mel = wav_to_mel(audio)
+            codes = dvae.get_codebook_indices(mel)
+            for i in range(audio.shape[0]):
+                c = codes[i, :batch['clip_lengths'][i]//1024+4]  # +4 seems empirically to be a good clipping point - it seems to preserve the termination codes.
+                fn = batch['path'][i]
+                outp = os.path.join(output_folder, os.path.relpath(fn, input_folder) + ".pth")
+                os.makedirs(os.path.dirname(outp), exist_ok=True)
+                torch.save(c.tolist(), outp)