diff --git a/hallo/datasets/audio_processor.py b/hallo/datasets/audio_processor.py
index 683282fb..50738970 100644
--- a/hallo/datasets/audio_processor.py
+++ b/hallo/datasets/audio_processor.py
@@ -73,7 +73,7 @@ def __init__(
         self.wav2vec_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(wav2vec_model_path, local_files_only=True)
 
 
-    def preprocess(self, wav_file: str):
+    def preprocess(self, wav_file: str, clip_length: int):
         """
         Preprocess a WAV audio file by separating the vocals from the background and resampling it to a 16 kHz sample rate.
         The separated vocal track is then converted into wav2vec2 for further processing or analysis.
@@ -106,8 +106,12 @@ def preprocess(self, wav_file: str):
         speech_array, sampling_rate = librosa.load(vocal_audio_file, sr=self.sample_rate)
         audio_feature = np.squeeze(self.wav2vec_feature_extractor(speech_array, sampling_rate=sampling_rate).input_values)
         seq_len = math.ceil(len(audio_feature) / self.sample_rate * self.fps)
+        audio_length = seq_len
 
         audio_feature = torch.from_numpy(audio_feature).float().to(device=self.device)
+        if seq_len % clip_length != 0:
+            audio_feature = torch.nn.functional.pad(audio_feature, (0, (clip_length - seq_len % clip_length) * (self.sample_rate // self.fps)), 'constant', 0.0)
+            seq_len += clip_length - seq_len % clip_length
         audio_feature = audio_feature.unsqueeze(0)
 
         with torch.no_grad():
@@ -121,7 +125,7 @@ def preprocess(self, wav_file: str):
 
         audio_emb = audio_emb.cpu().detach()
 
-        return audio_emb
+        return audio_emb, audio_length
 
     def get_embedding(self, wav_file: str):
         """preprocess wav audio file convert to embeddings
diff --git a/scripts/inference.py b/scripts/inference.py
index c2ef0bbb..8dfefbf8 100644
--- a/scripts/inference.py
+++ b/scripts/inference.py
@@ -178,7 +178,7 @@ def inference_process(args: argparse.Namespace):
         os.path.basename(audio_separator_model_file),
         os.path.join(save_path, "audio_preprocess")
     ) as audio_processor:
-        audio_emb = audio_processor.preprocess(driving_audio_path)
+        audio_emb, audio_length = audio_processor.preprocess(driving_audio_path, clip_length)
 
     # 4. build modules
     sched_kwargs = OmegaConf.to_container(config.noise_scheduler_kwargs)
@@ -339,6 +339,7 @@ def inference_process(args: argparse.Namespace):
 
     tensor_result = torch.cat(tensor_result, dim=2)
     tensor_result = tensor_result.squeeze(0)
+    tensor_result = tensor_result[:, :audio_length]
 
     output_file = config.output
     # save the result after all iteration