diff --git a/codes/scripts/audio/mel_bin_norm_compute.py b/codes/scripts/audio/mel_bin_norm_compute.py
index e50fff20..8155c6de 100644
--- a/codes/scripts/audio/mel_bin_norm_compute.py
+++ b/codes/scripts/audio/mel_bin_norm_compute.py
@@ -5,14 +5,14 @@ import yaml
 from tqdm import tqdm
 
 from data import create_dataset, create_dataloader
-from trainer.injectors.base_injectors import TorchMelSpectrogramInjector
+from scripts.audio.gen.speech_synthesis_utils import wav_to_univnet_mel
 from utils.options import Loader
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to options YAML file used to train the diffusion model', default='D:\\dlas\\options\\train_dvae_audio_clips.yml')
-    parser.add_argument('-key', type=str, help='Key where audio data is stored', default='clip')
-    parser.add_argument('-num_batches', type=int, help='Number of batches to collect to compute the norm', default=10)
+    parser.add_argument('-opt', type=str, help='Path to options YAML file used to train the diffusion model', default='D:\\dlas\\options\\train_diffusion_tts9.yml')
+    parser.add_argument('-key', type=str, help='Key where audio data is stored', default='wav')
+    parser.add_argument('-num_batches', type=int, help='Number of batches to collect to compute the norm', default=50000)
     args = parser.parse_args()
 
     with open(args.opt, mode='r') as f:
@@ -21,14 +21,26 @@ if __name__ == '__main__':
     dopt['phase'] = 'train'
     dataset, collate = create_dataset(dopt, return_collate=True)
     dataloader = create_dataloader(dataset, dopt, collate_fn=collate, shuffle=True)
-    inj = TorchMelSpectrogramInjector({'in': 'wav', 'out': 'mel'},{}).cuda()
 
-    mels = []
+    mel_means = []
+    mel_max = -999999999
+    mel_min = 999999999
+    mel_stds = []
+    mel_vars = []
     for batch in tqdm(dataloader):
-        clip = batch[args.key].cuda()
-        mel = inj({'wav': clip})['mel']
-        mels.append(mel.mean((0,2)).cpu())
-        if len(mels) > args.num_batches:
+        if len(mel_means) > args.num_batches:
             break
-    mel_norms = torch.stack(mels).mean(0)
-    torch.save(mel_norms, 'mel_norms.pth')
\ No newline at end of file
+        clip = batch[args.key].cuda()
+        for b in range(clip.shape[0]):
+            wav = clip[b].unsqueeze(0)
+            wav = wav[:, :, :batch[f'{args.key}_lengths'][b]]
+            mel = wav_to_univnet_mel(clip)  # Caution: make sure this isn't already normed.
+            mel_means.append(mel.mean((0,2)).cpu())
+            mel_max = max(mel.max().item(), mel_max)
+            mel_min = min(mel.min().item(), mel_min)
+            mel_stds.append(mel.std((0,2)).cpu())
+            mel_vars.append(mel.var((0,2)).cpu())
+    mel_means = torch.stack(mel_means).mean(0)
+    mel_stds = torch.stack(mel_stds).mean(0)
+    mel_vars = torch.stack(mel_vars).mean(0)
+    torch.save((mel_means,mel_max,mel_min,mel_stds,mel_vars), 'univnet_mel_norms.pth')
\ No newline at end of file
diff --git a/codes/trainer/injectors/audio_injectors.py b/codes/trainer/injectors/audio_injectors.py
index 694981d0..e255cd3e 100644
--- a/codes/trainer/injectors/audio_injectors.py
+++ b/codes/trainer/injectors/audio_injectors.py
@@ -21,6 +21,14 @@ class MelSpectrogramInjector(Injector):
         mel_fmax = opt_get(opt, ['mel_fmax'], 8000)
         sampling_rate = opt_get(opt, ['sampling_rate'], 22050)
         self.stft = TacotronSTFT(filter_length, hop_length, win_length, n_mel_channels, sampling_rate, mel_fmin, mel_fmax)
+        self.mel_norm_file = opt_get(opt, ['mel_norm_file'], None)
+        if self.mel_norm_file is not None:
+            # Note that this format is different from TorchMelSpectrogramInjector.
+            mel_means, mel_max, mel_min, mel_stds, mel_vars = torch.load(self.mel_norm_file)
+            self.mel_max = mel_max
+            self.mel_min = mel_min
+        else:
+            self.mel_norms = None
 
     def forward(self, state):
         inp = state[self.input]
@@ -28,7 +36,10 @@ class MelSpectrogramInjector(Injector):
             inp = inp.squeeze(1)
         assert len(inp.shape) == 2
         self.stft = self.stft.to(inp.device)
-        return {self.output: self.stft.mel_spectrogram(inp)}
+        mel = self.stft.mel_spectrogram(inp)
+        if self.mel_norms is not None:
+            mel = 2 * ((mel - self.mel_min) / (self.mel_max - self.mel_min)) - 1
+        return {self.output: mel}
 
 
 class TorchMelSpectrogramInjector(Injector):