diff --git a/preprocessor/aishell3.py b/preprocessor/aishell3.py
index 27b92aab12..e32607b6e9 100644
--- a/preprocessor/aishell3.py
+++ b/preprocessor/aishell3.py
@@ -2,7 +2,7 @@
 
 import librosa
 import numpy as np
-from scipy.io import wavfile
+import soundfile as sf
 from tqdm import tqdm
 
 
@@ -10,7 +10,7 @@ def prepare_align(config):
     in_dir = config["path"]["corpus_path"]
     out_dir = config["path"]["raw_path"]
     sampling_rate = config["preprocessing"]["audio"]["sampling_rate"]
-    max_wav_value = config["preprocessing"]["audio"]["max_wav_value"]
+    # max_wav_value = config["preprocessing"]["audio"]["max_wav_value"]
     for dataset in ["train", "test"]:
         print("Processing {}ing set...".format(dataset))
         with open(os.path.join(in_dir, dataset, "content.txt"), encoding="utf-8") as f:
@@ -22,11 +22,12 @@ def prepare_align(config):
                 if os.path.exists(wav_path):
                     os.makedirs(os.path.join(out_dir, speaker), exist_ok=True)
                     wav, _ = librosa.load(wav_path, sampling_rate)
-                    wav = wav / max(abs(wav)) * max_wav_value
-                    wavfile.write(
+                    wav = wav / max(abs(wav))  # * max_wav_value
+                    sf.write(
                         os.path.join(out_dir, speaker, wav_name),
+                        wav,
                         sampling_rate,
-                        wav.astype(np.int16),
+                        subtype='PCM_16'
                     )
                     with open(
                         os.path.join(out_dir, speaker, "{}.lab".format(wav_name[:11])),
diff --git a/preprocessor/libritts.py b/preprocessor/libritts.py
index a90fcdee5d..6d13d30f4e 100644
--- a/preprocessor/libritts.py
+++ b/preprocessor/libritts.py
@@ -2,7 +2,7 @@
 
 import librosa
 import numpy as np
-from scipy.io import wavfile
+import soundfile as sf
 from tqdm import tqdm
 
 from text import _clean_text
@@ -12,7 +12,7 @@ def prepare_align(config):
     in_dir = config["path"]["corpus_path"]
     out_dir = config["path"]["raw_path"]
     sampling_rate = config["preprocessing"]["audio"]["sampling_rate"]
-    max_wav_value = config["preprocessing"]["audio"]["max_wav_value"]
+    # max_wav_value = config["preprocessing"]["audio"]["max_wav_value"]
     cleaners = config["preprocessing"]["text"]["text_cleaners"]
     for speaker in tqdm(os.listdir(in_dir)):
         for chapter in os.listdir(os.path.join(in_dir, speaker)):
@@ -32,11 +32,12 @@ def prepare_align(config):
 
                 os.makedirs(os.path.join(out_dir, speaker), exist_ok=True)
                 wav, _ = librosa.load(wav_path, sampling_rate)
-                wav = wav / max(abs(wav)) * max_wav_value
-                wavfile.write(
+                wav = wav / max(abs(wav))  # * max_wav_value
+                sf.write(
                     os.path.join(out_dir, speaker, "{}.wav".format(base_name)),
+                    wav,
                     sampling_rate,
-                    wav.astype(np.int16),
+                    subtype='PCM_16'
                 )
                 with open(
                     os.path.join(out_dir, speaker, "{}.lab".format(base_name)),
diff --git a/preprocessor/ljspeech.py b/preprocessor/ljspeech.py
index a2f664873a..7803770fb7 100644
--- a/preprocessor/ljspeech.py
+++ b/preprocessor/ljspeech.py
@@ -2,7 +2,7 @@
 
 import librosa
 import numpy as np
-from scipy.io import wavfile
+import soundfile as sf
 from tqdm import tqdm
 
 from text import _clean_text
@@ -12,7 +12,7 @@ def prepare_align(config):
     in_dir = config["path"]["corpus_path"]
     out_dir = config["path"]["raw_path"]
     sampling_rate = config["preprocessing"]["audio"]["sampling_rate"]
-    max_wav_value = config["preprocessing"]["audio"]["max_wav_value"]
+    # max_wav_value = config["preprocessing"]["audio"]["max_wav_value"]
     cleaners = config["preprocessing"]["text"]["text_cleaners"]
     speaker = "LJSpeech"
     with open(os.path.join(in_dir, "metadata.csv"), encoding="utf-8") as f:
@@ -26,11 +26,12 @@ def prepare_align(config):
             if os.path.exists(wav_path):
                 os.makedirs(os.path.join(out_dir, speaker), exist_ok=True)
                 wav, _ = librosa.load(wav_path, sampling_rate)
-                wav = wav / max(abs(wav)) * max_wav_value
-                wavfile.write(
+                wav = wav / max(abs(wav))  # * max_wav_value
+                sf.write(
                     os.path.join(out_dir, speaker, "{}.wav".format(base_name)),
+                    wav,
                     sampling_rate,
-                    wav.astype(np.int16),
+                    subtype='PCM_16'
                 )
                 with open(
                     os.path.join(out_dir, speaker, "{}.lab".format(base_name)),