diff --git a/train/data_utils.py b/train/data_utils.py
index ee7d4d1..759cfbb 100644
--- a/train/data_utils.py
+++ b/train/data_utils.py
@@ -98,7 +98,9 @@ class TextAudioLoaderMultiNSFsid(torch.utils.data.Dataset):
                     sampling_rate, self.sampling_rate
                 )
             )
-        audio_norm = audio / self.max_wav_value
+#        audio_norm = audio / self.max_wav_value
+#        audio_norm = audio / np.abs(audio).max()
+
         audio_norm = audio_norm.unsqueeze(0)
         spec_filename = filename.replace(".wav", ".spec.pt")
         if os.path.exists(spec_filename):
@@ -287,7 +289,9 @@ class TextAudioLoader(torch.utils.data.Dataset):
                     sampling_rate, self.sampling_rate
                 )
             )
-        audio_norm = audio / self.max_wav_value
+#        audio_norm = audio / self.max_wav_value
+#        audio_norm = audio / np.abs(audio).max()
+
         audio_norm = audio_norm.unsqueeze(0)
         spec_filename = filename.replace(".wav", ".spec.pt")
         if os.path.exists(spec_filename):
diff --git a/trainset_preprocess_pipeline_print.py b/trainset_preprocess_pipeline_print.py
index b698203..a771c0e 100644
--- a/trainset_preprocess_pipeline_print.py
+++ b/trainset_preprocess_pipeline_print.py
@@ -65,6 +65,15 @@ class PreProcess:
         # default resample type of librosa.resample is "soxr_hq".
         # Quality: soxr_vhq > soxr_hq
         tmp_audio = librosa.resample(tmp_audio, orig_sr=self.sr, target_sr=16000, res_type="soxr_vhq")
+        tmp_audio = (tmp_audio / np.abs(tmp_audio).max() * (self.max * self.alpha)) + (
+            1 - self.alpha
+        ) * tmp_audio
+        wavfile.write(
+            "%s/%s_%s.wav" % (self.gt_wavs_dir, idx0, idx1),
+            self.sr,
+            (tmp_audio * 1).astype(np.float32),
+        )
+
         wavfile.write(
             "%s/%s_%s.wav" % (self.wavs16k_dir, idx0, idx1),
             16000,