diff --git a/se_extractor.py b/se_extractor.py
index b916406..e7d897f 100644
--- a/se_extractor.py
+++ b/se_extractor.py
@@ -1,6 +1,9 @@
 import os
 import glob
 import torch
+import hashlib
+import librosa
+import base64
 from glob import glob
 import numpy as np
 from pydub import AudioSegment
@@ -13,7 +16,7 @@ from whisper_timestamped.transcribe import get_audio_tensor, get_vad_segments
 model_size = "medium"
 # Run on GPU with FP16
 model = None
-def split_audio_whisper(audio_path, target_dir, audio_name):
+def split_audio_whisper(audio_path, audio_name, target_dir='processed'):
     global model
     if model is None:
         model = WhisperModel(model_size, device="cuda", compute_type="float16")
@@ -71,7 +74,7 @@ def split_audio_whisper(audio_path, target_dir, audio_name):
     return wavs_folder
 
 
-def split_audio_vad(audio_path, target_dir, audio_name, split_seconds=10.0):
+def split_audio_vad(audio_path, audio_name, target_dir, split_seconds=10.0):
     SAMPLE_RATE = 16000
     audio_vad = get_audio_tensor(audio_path)
     segments = get_vad_segments(
@@ -112,19 +115,21 @@ def split_audio_vad(audio_path, target_dir, audio_name, split_seconds=10.0):
         count += 1
     return wavs_folder
 
-
-def hash_numpy_array(array):
+def hash_numpy_array(audio_path):
+    array, _ = librosa.load(audio_path, sr=None, mono=True)
+    # Convert the array to bytes
     array_bytes = array.tobytes()
+    # Calculate the hash of the array bytes
     hash_object = hashlib.sha256(array_bytes)
     hash_value = hash_object.digest()
+    # Convert the hash value to base64
     base64_value = base64.b64encode(hash_value)
-    return base64_value.decode('utf-8')[:16].replace('/', '&')    
-
+    return base64_value.decode('utf-8')[:16].replace('/', '_^')
 
 def get_se(audio_path, vc_model, target_dir='processed', vad=True):
     device = vc_model.device
-    audio_hash = hash_numpy_array(librosa.load(audio_path, mono=True)[0])
-    audio_name = os.path.basename(audio_path).rsplit('.', 1)[0] + '_' + audio_hash
+
+    audio_name = f"{os.path.basename(audio_path).rsplit('.', 1)[0]}_{hash_numpy_array(audio_path)}"
     se_path = os.path.join(target_dir, audio_name, 'se.pth')
 
     if os.path.isfile(se_path):
@@ -133,9 +138,9 @@ def get_se(audio_path, vc_model, target_dir='processed', vad=True):
     if os.path.isdir(audio_path):
         wavs_folder = audio_path
     elif vad:
-        wavs_folder = split_audio_vad(audio_path, target_dir, audio_name)
+        wavs_folder = split_audio_vad(audio_path, target_dir=target_dir, audio_name=audio_name)
     else:
-        wavs_folder = split_audio_whisper(audio_path, target_dir, audio_name)
+        wavs_folder = split_audio_whisper(audio_path, target_dir=target_dir, audio_name=audio_name)
     
     audio_segs = glob(f'{wavs_folder}/*.wav')
     if len(audio_segs) == 0: