diff --git a/generate_data_100mix.py b/generate_data_100mix.py
deleted file mode 100755
index 0531a4b..0000000
--- a/generate_data_100mix.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import os
-
-# def get_wav_files_dict(directory):
-#     # 初始化空字典
-#     files_dict = {}
-    
-#     # 遍历指定目录下的所有文件
-#     for root, dirs, files in os.walk(directory):
-#         for file in files:
-#             # 只处理.wav文件
-#             if file.endswith(".wav"):
-#                 # 获取文件的绝对路径
-#                 file_path = os.path.join(root, file)
-#                 # 根据"_"分割文件名
-#                 key = file.split('_')[0]
-#                 # 如果key不在字典中，初始化一个空list
-#                 if key not in files_dict:
-#                     files_dict[key] = []
-#                 # 将文件路径加入到对应的key的list中
-#                 files_dict[key].append(file_path)
-    
-#     return files_dict
-
-# # 示例用法
-# directory = "/home/likai/ssd/vox2/vox2/audio_10w/wav16k/min/tr/s1"  # 替换为你的文件夹路径
-# wav_files_dict = get_wav_files_dict(directory)
-
-# # Save the dictionary to json
-# import json
-# with open('wav_files_dict.json', 'w') as f:
-#     json.dump(wav_files_dict, f)
-
-# Load the dictionary from json
-import json
-import random
-import torchaudio
-import torch
-import numpy as np
-import shutil
-import yaml
-
-from look2hear.models import IIANet
-from look2hear.datas.transform import get_preprocessing_pipelines
-from look2hear.videomodels import ResNetVideoModel
-
-with open('wav_files_dict.json', 'r') as f:
-    wav_files_dict = json.load(f)
-    
-# print(wav_files_dict)
-datas = []
-select_keys = random.sample(wav_files_dict.keys(), k=3)
-datapath = random.sample(wav_files_dict[select_keys[0]], k=1)
-mouthpath = datapath[0].split('/')[-1].split("_")
-mouthpath = f"{mouthpath[0]}_{mouthpath[1]}_{mouthpath[2]}.npz"
-audio_gt = torchaudio.load(datapath[0])[0]
-# mouth = torch.from_numpy(np.load(mouthpath)['data'])
-datas.append(audio_gt)
-
-for key in select_keys[1:]:
-    datapath = random.sample(wav_files_dict[key], k=1)
-    audio = torchaudio.load(datapath[0])[0]
-    sirs = torch.Tensor(1).uniform_(-30,-10).numpy()
-    audio *= 10.**(sirs/20.)
-    datas.append(audio)
-
-mix = torch.stack(datas).sum(0)
-torchaudio.save("mix.wav", mix, 16000)
-torchaudio.save("audio_gt.wav", audio_gt, 16000)
-shutil.copy(f"/home/likai/ssd/vox2/vox2/mouths/{mouthpath}", "mouth.npz")
-# Load training config
-with open("checkpoints/vox2/conf.yml", "rb") as f:
-    train_conf = yaml.safe_load(f)
-
-# Load model
-# print(["main_args"]["exp_dir"])
-checkpoint_path = os.path.join(train_conf["main_args"]["exp_dir"], "best_model.pth")
-audiomodel = IIANet.from_pretrain(checkpoint_path, sample_rate=train_conf["datamodule"]["data_config"]["sample_rate"], **train_conf["audionet"]["audionet_config"])
-videomodel = ResNetVideoModel(**train_conf["videonet"]["videonet_config"])
-audiomodel.cuda()
-audiomodel.eval()
-videomodel.cuda()
-videomodel.eval()
-
-with torch.no_grad():
-    mouth_roi = np.load("mouth.npz")["data"]
-    mouth_roi = get_preprocessing_pipelines()["val"](mouth_roi)
-
-    mix = torchaudio.load("mix.wav")[0].cuda()
-
-    mouth_emb = videomodel(torch.from_numpy(mouth_roi[None, None]).float().cuda())
-    est_sources = audiomodel(mix[None], mouth_emb)
-
-    torchaudio.save("est.wav", est_sources[0].cpu(), 16000)
\ No newline at end of file
diff --git a/replace_weight.py b/replace_weight.py
deleted file mode 100755
index 32a24f3..0000000
--- a/replace_weight.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import torch
-from look2hear.models import IIANet
-import yaml
-from collections import OrderedDict
-
-# Load training config
-with open("checkpoints/vox2/conf.yml", "rb") as f:
-    train_conf = yaml.safe_load(f)
-
-pretrained_weight = torch.load("/home/likai/ssd/IIANet/checkpoints/lrs2/best_model.pth", map_location="cpu")
-# import pdb; pdb.set_trace()
-model = IIANet(**train_conf["audionet"]["audionet_config"])
-
-
-# new_state_dict = OrderedDict()
-
-# for k, v in pretrained_weight["state_dict"].items():
-#     if "audio_model." in k:
-#         # print(k)
-#         if "mlp_avfusion" in k:
-#             k = k.replace("audio_model.", "").replace("mlp_avfusion", "InterA_T")
-#             new_state_dict[k] = v
-#             print(k)
-#             continue
-#         elif "concat_block" in k:
-#             k = k.replace("audio_model.", "").replace("concat_block", "InterA_B_A")
-#             new_state_dict[k] = v
-#             print(k)
-#             continue
-#         elif "video_concat" in k:
-#             k = k.replace("audio_model.", "").replace("video_concat", "InterA_B_V")
-#             new_state_dict[k] = v
-#             print(k)
-#             continue
-#         elif k.replace("audio_model.", "") in model.state_dict().keys():
-#             new_state_dict[k.replace("audio_model.", "")] = v
-#             print(k)
-#             continue
-
-# model.load_state_dict(new_state_dict, strict=True)
-
-# pretrained_weight["state_dict"] = new_state_dict
-
-# delete keys in pretrained_weight
-# for k in list(pretrained_weight.keys()):
-#     if k not in ['model_name', 'state_dict', 'model_args', 'infos']:
-#         del pretrained_weight[k]
-pretrained_weight["model_name"] = "IIANet"
-pretrained_weight["model_args"] = train_conf["audionet"]["audionet_config"]
-pretrained_weight["infos"] = []
-torch.save(pretrained_weight, "checkpoints/lrs2/best_model.pth")
-
-# for k, v in model.state_dict().items():
-#     print(k)
\ No newline at end of file