diff --git a/inference/infer_tool.py b/inference/infer_tool.py
index 3a2635b..6440fb0 100644
--- a/inference/infer_tool.py
+++ b/inference/infer_tool.py
@@ -102,6 +102,10 @@ def pad_array(arr, target_length):
         pad_right = pad_width - pad_left
         padded_arr = np.pad(arr, (pad_left, pad_right), 'constant', constant_values=(0, 0))
         return padded_arr
+    
+def split_list_by_n(list_collection, n, pre=0):
+    for i in range(0, len(list_collection), n):
+        yield list_collection[i-pre if i-pre>=0 else i: i + n]
 
 
 class Svc(object):
diff --git a/inference_main.py b/inference_main.py
index f869369..bde6a2c 100644
--- a/inference_main.py
+++ b/inference_main.py
@@ -25,6 +25,7 @@ def main():
     # 一定要设置的部分
     parser.add_argument('-m', '--model_path', type=str, default="logs/44k/G_0.pth", help='模型路径')
     parser.add_argument('-c', '--config_path', type=str, default="configs/config.json", help='配置文件路径')
+    parser.add_argument('-cl', '--clip', type=float, default=0, help='音频自动切片，0为不切片，单位为秒/s')
     parser.add_argument('-n', '--clean_names', type=str, nargs='+', default=["君の知らない物語-src.wav"], help='wav文件名列表，放在raw文件夹下')
     parser.add_argument('-t', '--trans', type=int, nargs='+', default=[0], help='音高调整，支持正负（半音）')
     parser.add_argument('-s', '--spk_list', type=str, nargs='+', default=['nen'], help='合成目标说话人名称')
@@ -34,6 +35,7 @@ def main():
                         help='语音转换自动预测音高，转换歌声时不要打开这个会严重跑调')
     parser.add_argument('-cm', '--cluster_model_path', type=str, default="logs/44k/kmeans_10000.pt", help='聚类模型路径，如果没有训练聚类则随便填')
     parser.add_argument('-cr', '--cluster_infer_ratio', type=float, default=0, help='聚类方案占比，范围0-1，若没有训练聚类模型则填0即可')
+    parser.add_argument('-lg', '--linear_gradient', type=float, default=0, help='两端音频切片的交叉淡入长度，如果自动切片后出现人声不连贯可调整该数值，如果连贯建议采用默认值0，单位为秒/s')
 
     # 不用动的部分
     parser.add_argument('-sd', '--slice_db', type=int, default=-40, help='默认-40，嘈杂的音频可以-30，干声保留呼吸可以-50')
@@ -55,6 +57,8 @@ def main():
     cluster_infer_ratio = args.cluster_infer_ratio
     noice_scale = args.noice_scale
     pad_seconds = args.pad_seconds
+    clip = args.clip
+    lg = args.linear_gradient
 
     infer_tool.fill_a_to_b(trans, clean_names)
     for clean_name, tran in zip(clean_names, trans):
@@ -65,22 +69,32 @@ def main():
         wav_path = Path(raw_audio_path).with_suffix('.wav')
         chunks = slicer.cut(wav_path, db_thresh=slice_db)
         audio_data, audio_sr = slicer.chunks2audio(wav_path, chunks)
+        per_size = int(clip*audio_sr)
+        lg_size = int(lg*audio_sr)
+        lg = np.linspace(0,1,lg_size) if lg_size!=0 else 0
 
         for spk in spk_list:
             audio = []
             for (slice_tag, data) in audio_data:
                 print(f'#=====segment start, {round(len(data) / audio_sr, 3)}s======')
-
                 length = int(np.ceil(len(data) / audio_sr * svc_model.target_sample))
                 if slice_tag:
                     print('jump empty segment')
                     _audio = np.zeros(length)
+                    audio.extend(list(infer_tool.pad_array(_audio, length)))
+                    continue
+                if per_size != 0:
+                    datas = infer_tool.split_list_by_n(data, per_size,lg_size)
                 else:
+                    datas = [data]
+                for k,dat in enumerate(datas):
+                    per_length = int(np.ceil(len(dat) / audio_sr * svc_model.target_sample)) if clip!=0 else length
+                    if clip!=0: print(f'###=====segment clip start, {round(len(dat) / audio_sr, 3)}s======')
                     # padd
                     pad_len = int(audio_sr * pad_seconds)
-                    data = np.concatenate([np.zeros([pad_len]), data, np.zeros([pad_len])])
+                    dat = np.concatenate([np.zeros([pad_len]), dat, np.zeros([pad_len])])
                     raw_path = io.BytesIO()
-                    soundfile.write(raw_path, data, audio_sr, format="wav")
+                    soundfile.write(raw_path, dat, audio_sr, format="wav")
                     raw_path.seek(0)
                     out_audio, out_sr = svc_model.infer(spk, tran, raw_path,
                                                         cluster_infer_ratio=cluster_infer_ratio,
@@ -90,8 +104,15 @@ def main():
                     _audio = out_audio.cpu().numpy()
                     pad_len = int(svc_model.target_sample * pad_seconds)
                     _audio = _audio[pad_len:-pad_len]
-
-                audio.extend(list(infer_tool.pad_array(_audio, length)))
+                    _audio = infer_tool.pad_array(_audio, per_length)
+                    if lg_size!=0 and k!=0:
+                        lg1 = audio[-lg_size:]
+                        lg2 = _audio[0:lg_size]
+                        lg_pre = lg1*(1-lg)+lg2*lg
+                        audio = audio[0:-lg_size]
+                        audio.extend(lg_pre)
+                        _audio = _audio[lg_size:]
+                    audio.extend(list(_audio))
             key = "auto" if auto_predict_f0 else f"{tran}key"
             cluster_name = "" if cluster_infer_ratio == 0 else f"_{cluster_infer_ratio}"
             res_path = f'./results/{clean_name}_{key}_{spk}{cluster_name}.{wav_format}'