diff --git a/codes/data/audio/paired_voice_audio_dataset.py b/codes/data/audio/paired_voice_audio_dataset.py
index 8f74e31f..6df37389 100644
--- a/codes/data/audio/paired_voice_audio_dataset.py
+++ b/codes/data/audio/paired_voice_audio_dataset.py
@@ -222,8 +222,8 @@ if __name__ == '__main__':
     batch_sz = 8
     params = {
         'mode': 'paired_voice_audio',
-        'path': ['Z:\\clips\\podcasts-0-transcribed.tsv'],
-        'fetcher_mode': ['tsv'],
+        'path': ['Y:\\bigasr_dataset\\hifi_tts_mp3\\test.txt'],
+        'fetcher_mode': ['libritts'],
         'phase': 'train',
         'n_workers': 0,
         'batch_size': batch_sz,
@@ -238,11 +238,20 @@ if __name__ == '__main__':
     }
     from data import create_dataset, create_dataloader
 
+    def save(b, i, ib, key, c=None):
+        if c is not None:
+            torchaudio.save(f'{i}_clip_{ib}_{key}_{c}.wav', b[key][ib][c], 22050)
+        else:
+            torchaudio.save(f'{i}_clip_{ib}_{key}.wav', b[key][ib], 22050)
+
     ds, c = create_dataset(params, return_collate=True)
     dl = create_dataloader(ds, params, collate_fn=c)
     i = 0
     m = None
     for i, b in tqdm(enumerate(dl)):
         for ib in range(batch_sz):
-            print(f"text_seq: {b['text_lengths'].max()}, speech_seq: {b['wav_lengths'].max()//1024}")
+            print(f'{i} {ib} {b["real_text"][ib]}')
+            save(b, i, ib, 'wav')
+        if i > 5:
+            break
 
diff --git a/codes/data/audio/unsupervised_audio_dataset.py b/codes/data/audio/unsupervised_audio_dataset.py
index e82d857e..dd0ab5c1 100644
--- a/codes/data/audio/unsupervised_audio_dataset.py
+++ b/codes/data/audio/unsupervised_audio_dataset.py
@@ -38,9 +38,7 @@ def load_audio(audiopath, sampling_rate):
             audio = audio[:, 0]
 
     if lsr != sampling_rate:
-        #if lsr < sampling_rate:
-        #    warn(f'{audiopath} has a sample rate of {sampling_rate} which is lower than the requested sample rate of {sampling_rate}. This is not a good idea.')
-        audio = torch.nn.functional.interpolate(audio.unsqueeze(0).unsqueeze(1), scale_factor=sampling_rate/lsr, mode='nearest', recompute_scale_factor=False).squeeze()
+        audio = torchaudio.functional.resample(audio, lsr, sampling_rate)
 
     # Check some assumptions about audio range. This should be automatically fixed in load_wav_to_torch, but might not be in some edge cases, where we should squawk.
     # '2' is arbitrarily chosen since it seems like audio will often "overdrive" the [-1,1] bounds.