diff --git a/codes/data/audio/grand_conjoined_dataset.py b/codes/data/audio/grand_conjoined_dataset.py
index 4d095b04..7ba5bdb7 100644
--- a/codes/data/audio/grand_conjoined_dataset.py
+++ b/codes/data/audio/grand_conjoined_dataset.py
@@ -44,10 +44,12 @@ class GrandConjoinedDataset(torch.utils.data.Dataset):
     Performs tokenization at this level, ignoring any tokenization performed by upstream datasets.
     """
     def __init__(self, opt):
+        sample_rate = 22050  # Fixed.
         paired_dataset_args = opt['paired_dataset_args']
-        unsupervised_audio_args = opt['unsupervised_audio_args']
-        text_corpus_args = opt['text_corpus_args']
-        sample_rate = 22050
+        self.only_paired = opt_get(opt, ['only_paired'], False)
+        if not self.only_paired:
+            unsupervised_audio_args = opt['unsupervised_audio_args']
+            text_corpus_args = opt['text_corpus_args']
 
         self.max_paired_audio_length = opt['max_paired_audio_length']
         self.max_paired_text_length = opt['max_paired_text_length']
@@ -61,14 +63,15 @@ class GrandConjoinedDataset(torch.utils.data.Dataset):
         paired_dataset_args['sample_rate'] = sample_rate
         paired_dataset_args['max_wav_length'] = self.max_paired_audio_length
         paired_dataset_args['max_text_length'] = self.max_paired_text_length
-        unsupervised_audio_args['sampling_rate'] = sample_rate
-        unsupervised_audio_args['do_augmentation'] = False
-        unsupervised_audio_args['resample_clip'] = False
-        unsupervised_audio_args['pad_to_samples'] = self.max_solo_audio_length
-
         self.speech_and_text = build_paired_voice_dataset(paired_dataset_args)
-        self.speech = UnsupervisedAudioDataset(unsupervised_audio_args)
-        self.text = HfDataset(**text_corpus_args)
+
+        if not self.only_paired:
+            unsupervised_audio_args['sampling_rate'] = sample_rate
+            unsupervised_audio_args['do_augmentation'] = False
+            unsupervised_audio_args['resample_clip'] = False
+            unsupervised_audio_args['pad_to_samples'] = self.max_solo_audio_length
+            self.speech = UnsupervisedAudioDataset(unsupervised_audio_args)
+            self.text = HfDataset(**text_corpus_args)
 
     def fetch_text_at(self, i):
         try:
@@ -76,7 +79,7 @@ class GrandConjoinedDataset(torch.utils.data.Dataset):
             tok = self.speech_and_text.get_text(txt)
             padding_required = self.max_solo_text_length - tok.shape[0]
             if padding_required < 0:
-                # Just truncate since there is no conditioning requried.
+                # Just truncate since there is no conditioning required.
                 tok = tok[:self.max_solo_text_length]
             elif padding_required > 0:
                 tok = F.pad(tok, (0, padding_required))
@@ -88,29 +91,45 @@ class GrandConjoinedDataset(torch.utils.data.Dataset):
 
     def __getitem__(self, i):
         snt = self.speech_and_text[i % len(self.speech_and_text)]
-        sp = self.speech[i % len(self.speech)]
-        txt, txt_tok = self.fetch_text_at(i % len(self.text))
-
-        return {
-            'paired_audio': snt['wav'],
-            'paired_audio_lengths': snt['wav_lengths'],
-            'paired_text': snt['real_text'],
-            'paired_text_tokens': snt['padded_text'],
-            'paired_file': snt['filenames'],
-            'speech_audio': sp['clip'],
-            'speech_lengths': clamp(sp['clip_lengths'], 0, self.max_solo_audio_length),
-            'speech_file': sp['path'],
-            'text_text': txt,
-            'text_tokens': txt_tok,
-        }
+        if self.only_paired:
+            return {
+                'paired_audio': snt['wav'],
+                'paired_audio_lengths': snt['wav_lengths'],
+                'paired_text': snt['real_text'],
+                'paired_text_tokens': snt['padded_text'],
+                'paired_file': snt['filenames'],
+                'speech_audio': snt['wav'],
+                'speech_lengths': snt['wav_lengths'],
+                'speech_file': snt['filenames'],
+                'text_text': snt['real_text'],
+                'text_tokens': snt['padded_text'],
+            }
+        else:
+            sp = self.speech[i % len(self.speech)]
+            txt, txt_tok = self.fetch_text_at(i % len(self.text))
+            return {
+                'paired_audio': snt['wav'],
+                'paired_audio_lengths': snt['wav_lengths'],
+                'paired_text': snt['real_text'],
+                'paired_text_tokens': snt['padded_text'],
+                'paired_file': snt['filenames'],
+                'speech_audio': sp['clip'],
+                'speech_lengths': clamp(sp['clip_lengths'], 0, self.max_solo_audio_length),
+                'speech_file': sp['path'],
+                'text_text': txt,
+                'text_tokens': txt_tok,
+            }
 
     def __len__(self):
-        return max(len(self.speech), len(self.speech_and_text), len(self.text))
+        if self.only_paired:
+            return len(self.speech_and_text)
+        else:
+            return max(len(self.speech), len(self.speech_and_text), len(self.text))
 
 
 if __name__ == '__main__':
     batch_sz = 8
-    params = {
+    train_params = {
         'mode': 'grand_conjoined_voice',
         'phase': 'train',
         'n_workers': 0,
@@ -133,10 +152,26 @@ if __name__ == '__main__':
             'cache_path': 'Z:\\huggingface_datasets\\cache',
         },
     }
+    val_params = {
+        'mode': 'grand_conjoined_voice',
+        'phase': 'val',
+        'n_workers': 0,
+        'batch_size': batch_sz,
+
+        'max_paired_audio_length': 255995,
+        'max_paired_text_length': 80,
+        'max_solo_text_length': 330,
+        'max_solo_audio_length': 300000,
+        'only_paired': True,
+        'paired_dataset_args': {
+            'path': ['Z:\\bigasr_dataset\\libritts\\test-clean_list.txt'],
+            'fetcher_mode': ['libritts'],
+        },
+    }
     from data import create_dataset, create_dataloader
 
-    ds = create_dataset(params)
-    dl = create_dataloader(ds, params)
+    ds = create_dataset(val_params)
+    dl = create_dataloader(ds, val_params)
 
     def save(b, i, ib, key):
         torchaudio.save(f'{i}_clip_{ib}_{key}.wav', b[key][ib], 22050)