diff --git a/add_punc.py b/add_punc.py
deleted file mode 100644
index c299de1..0000000
--- a/add_punc.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import json
-import os
-import multiprocessing
-from modelscope.pipelines import pipeline
-from modelscope.utils.constant import Tasks
-from tqdm import tqdm
-import logging
-
-logging.getLogger("modelscope").setLevel(logging.ERROR)
-
-metadata_path = "filelists/genshin.list"
-output_path = "filelists/genshin_punc.list"
-
-os.makedirs("tmp", exist_ok=True)
-
-
-def split_list(lst, n):
-    avg = len(lst) // n  # 每份的平均长度
-    rem = len(lst) % n  # 剩余的长度
-
-    result = []
-    start = 0
-
-    for i in range(n):
-        end = start + avg + (1 if i < rem else 0)  # 计算当前份的结束位置
-        result.append(lst[start:end])  # 切片并将结果添加到结果列表中
-        start = end  # 更新下一份的起始位置
-
-    return result
-
-
-def process(audios, pid):
-    print("loading model")
-    inference_pipeline = pipeline(
-        task=Tasks.punctuation,
-        model='damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch'
-    )
-    print("loaded model")
-
-    total_cnt = 0
-    skip_cnt = 0
-    for audiofile in tqdm(audios):
-        for k, sentence in enumerate(audiofile['segments']):
-            confidence = sentence['confidence']
-            if confidence >= 0.95:
-                text = sentence['text']
-                rec_result = inference_pipeline(text_in=text)
-                sentence['text'] = rec_result['text']
-                total_cnt += 1
-            else:
-                skip_cnt += 1
-        print("preprocessed text count :", total_cnt, "skip count :", skip_cnt)
-    with open(f"tmp/{pid}.json", 'w') as f:
-        json.dump(audios, f, ensure_ascii=False, indent=2)
-
-
-def process_wrapper(args):
-    audios, pid = args
-    process(audios, pid)
-
-
-if __name__ == '__main__':
-    # Define the number of processes
-    n_process = 8
-
-    # Split the audios into chunks for multiprocessing
-    with open(metadata_path, "r") as f:
-        lines = f.readlines()
-    audio_chunks = split_list(audios, n_process)
-
-    # Create a pool of worker processes
-    pool = multiprocessing.Pool(processes=n_process)
-
-    # Map the process function to each chunk of audios
-    pool.map(process_wrapper, zip(audio_chunks, range(n_process)))
-
-    # Close the pool and wait for the work to finish
-    pool.close()
-    pool.join()
-
-    # Merge output files
-    merged_data = []