diff --git a/server/voice_changer/DDSP_SVC/DDSP_SVC.py b/server/voice_changer/DDSP_SVC/DDSP_SVC.py
index d36aa092..54d78318 100644
--- a/server/voice_changer/DDSP_SVC/DDSP_SVC.py
+++ b/server/voice_changer/DDSP_SVC/DDSP_SVC.py
@@ -24,7 +24,7 @@ from slicer import Slicer
 import librosa
 providers = ['OpenVINOExecutionProvider', "CUDAExecutionProvider", "DmlExecutionProvider", "CPUExecutionProvider"]
 
-
+import resampy
 from scipy.io import wavfile
 SAMPLING_RATE = 44100
 
@@ -234,7 +234,7 @@ class DDSP_SVC:
             return np.zeros(convertSize).astype(np.int16)
 
         with torch.no_grad():
-            spk_id = torch.LongTensor(np.array([[int(2)]]))
+            spk_id = torch.LongTensor(np.array([[int(1)]]))
             seg_output, _, (s_h, s_n) = self.model(c, f0, volume, spk_id=spk_id, spk_mix_dict=None)
             seg_output *= mask
 
@@ -245,7 +245,6 @@ class DDSP_SVC:
                 self.args.data.block_size,
                 adaptive_key=float(0))
             result = seg_output.squeeze().cpu().numpy() * 32768.0
-
         return np.array(result).astype(np.int16)
 
     def inference(self, data):
diff --git a/server/voice_changer/VoiceChanger.py b/server/voice_changer/VoiceChanger.py
index 97bb327a..3441c56b 100755
--- a/server/voice_changer/VoiceChanger.py
+++ b/server/voice_changer/VoiceChanger.py
@@ -244,12 +244,10 @@ class VoiceChanger():
                 self.ioRecorder.writeOutput(outputData.tobytes())
 
             # if receivedData.shape[0] != outputData.shape[0]:
+            #     print(f"Padding, in:{receivedData.shape[0]} out:{outputData.shape[0]}")
             #     outputData = pad_array(outputData, receivedData.shape[0])
             #     # print_convert_processing(
             #     #     f" Padded!, Output data size of {result.shape[0]}/{processing_sampling_rate}hz {outputData.shape[0]}/{self.settings.inputSampleRate}hz")
-            #     print(
-            #         f" Padded!, Output data size of {result.shape[0]}/{processing_sampling_rate}hz {outputData.shape[0]}/{self.settings.inputSampleRate}hz")
-
         postprocess_time = t.secs
 
         print_convert_processing(f" [fin] Input/Output size:{receivedData.shape[0]},{outputData.shape[0]}")