diff --git a/AutoCoverTool/ref/online/voice_class_online.py b/AutoCoverTool/ref/online/voice_class_online.py
index 6041c94..eab43a7 100644
--- a/AutoCoverTool/ref/online/voice_class_online.py
+++ b/AutoCoverTool/ref/online/voice_class_online.py
@@ -1,420 +1,420 @@
 """
 男女声分类在线工具
 1 转码为16bit单声道
 2 均衡化
 3 模型分类
 """
 
 import os
 import sys
 import librosa
 import shutil
 import logging
 import time
 import torch.nn.functional as F
 import numpy as np
 from model import *
 # from common import bind_kernel
 
 logging.basicConfig(level=logging.INFO)
 
 os.environ["LRU_CACHE_CAPACITY"] = "1"
 
 # torch.set_num_threads(1)
 # bind_kernel(1)
 
 """
 临时用一下,全局使用的变量
 """
 
 transcode_time = 0
 vb_time = 0
 mfcc_time = 0
 predict_time = 0
 
 """
 错误码
 """
 ERR_CODE_SUCCESS = 0  # 处理成功
 ERR_CODE_NO_FILE = -1  # 文件不存在
 ERR_CODE_TRANSCODE = -2  # 转码失败
 ERR_CODE_VOLUME_BALANCED = -3  # 均衡化失败
 ERR_CODE_FEATURE_TOO_SHORT = -4  # 特征文件太短
 
 """
 常量
 """
 
 FRAME_LEN = 128
 MFCC_LEN = 80
 
-EBUR128_BIN = "/opt/soft/bin/standard_audio_no_cut"
+EBUR128_BIN = "/data/gpu_env_common/res/av_svc/bin/standard_audio_no_cut"
 # EBUR128_BIN = "/Users/yangjianli/linux/opt/soft/bin/standard_audio_no_cut"
 GENDER_FEMALE = 0
 GENDER_MALE = 1
 GENDER_OTHER = 2
 """
 通用函数
 """
 
 
 def exec_cmd(cmd):
     ret = os.system(cmd)
     if ret != 0:
         return False
     return True
 
 
 """
 业务需要的函数
 """
 
 
 def get_one_mfcc(file_url):
     st = time.time()
     data, sr = librosa.load(file_url, sr=16000)
     if len(data) < 512:
         return []
     mfcc = librosa.feature.mfcc(y=data, sr=sr, n_fft=512, hop_length=256, n_mfcc=MFCC_LEN)
     mfcc = mfcc.transpose()
     print("get_one_mfcc:spend_time={}".format(time.time() - st))
     global mfcc_time
     mfcc_time += time.time() - st
     return mfcc
 
 
 def volume_balanced(src, dst):
     st = time.time()
     cmd = "{} {} {}".format(EBUR128_BIN, src, dst)
     logging.info(cmd)
     exec_cmd(cmd)
     if not os.path.exists(dst):
         logging.error("volume_balanced:cmd={}".format(cmd))
     print("volume_balanced:spend_time={}".format(time.time() - st))
 
     global vb_time
     vb_time += time.time() - st
     return os.path.exists(dst)
 
 
 def transcode(src, dst):
     st = time.time()
     cmd = "ffmpeg -loglevel quiet -i {} -ar 16000 -ac 1 {}".format(src, dst)
     logging.info(cmd)
     exec_cmd(cmd)
     if not os.path.exists(dst):
         logging.error("transcode:cmd={}".format(cmd))
     print("transcode:spend_time={}".format(time.time() - st))
     global transcode_time
     transcode_time += time.time() - st
     return os.path.exists(dst)
 
 
 class VoiceClass:
 
     def __init__(self, music_voice_pure_model, music_voice_no_pure_model, gender_pure_model, gender_no_pure_model):
         """
         四个模型
         :param music_voice_pure_model: 分辨纯净人声/其他
         :param music_voice_no_pure_model: 分辨有人声/其他
         :param gender_pure_model: 纯净人声分辨男女
         :param gender_no_pure_model: 有人声分辨男女
         """
         st = time.time()
         self.device = "cpu"
         self.batch_size = 256
         self.music_voice_pure_model = load_model(MusicVoiceV5Model, music_voice_pure_model, self.device)
         self.music_voice_no_pure_model = load_model(MusicVoiceV5Model, music_voice_no_pure_model, self.device)
         self.gender_pure_model = load_model(MobileNetV2Gender, gender_pure_model, self.device)
         self.gender_no_pure_model = load_model(MobileNetV2Gender, gender_no_pure_model, self.device)
         logging.info("load model ok ! spend_time={}".format(time.time() - st))
 
     def batch_predict(self, model, features):
         st = time.time()
         scores = []
         with torch.no_grad():
             for i in range(0, len(features), self.batch_size):
                 cur_data = features[i:i + self.batch_size].to(self.device)
                 predicts = model(cur_data)
                 predicts_score = F.softmax(predicts, dim=1)
                 scores.extend(predicts_score.cpu().numpy())
         ret = np.array(scores)
         global predict_time
         predict_time += time.time() - st
         return ret
 
     def predict_pure(self, filename, features):
         scores = self.batch_predict(self.music_voice_pure_model, features)
         new_features = []
         for idx, score in enumerate(scores):
             if score[0] > 0.5:  # 非人声
                 continue
             new_features.append(features[idx].numpy())
 
         # 人声段太少,不能进行处理
         # 参数可以改
         new_feature_len = len(new_features)
         new_feature_rate = len(new_features) / len(features)
         if new_feature_len < 4 or new_feature_rate < 0.4:
             logging.warning(
                 "filename={}|predict_pure|other|len={}|rate={}".format(filename, new_feature_len, new_feature_rate)
             )
             return GENDER_OTHER, -1
         new_features = torch.from_numpy(np.array(new_features))
         scores = self.batch_predict(self.gender_pure_model, new_features)
         f_avg = sum(scores[:, 0]) / len(scores)
         m_avg = sum(scores[:, 1]) / len(scores)
         female_rate = f_avg / (f_avg + m_avg)
         if female_rate > 0.65:
             return GENDER_FEMALE, female_rate
         if female_rate < 0.12:
             return GENDER_MALE, female_rate
         logging.warning(
             "filename={}|predict_pure|other|len={}|rate={}".format(filename, new_feature_len, new_feature_rate)
         )
         return GENDER_OTHER, female_rate
 
     def predict_no_pure(self, filename, features):
         scores = self.batch_predict(self.music_voice_no_pure_model, features)
         new_features = []
         for idx, score in enumerate(scores):
             if score[0] > 0.5:  # 非人声
                 continue
             new_features.append(features[idx].numpy())
 
         # 人声段太少,不能进行处理
         # 参数可以改
         new_feature_len = len(new_features)
         new_feature_rate = len(new_features) / len(features)
         if new_feature_len < 4 or new_feature_rate < 0.4:
             logging.warning(
                 "filename={}|predict_no_pure|other|len={}|rate={}".format(filename, new_feature_len, new_feature_rate)
             )
             return GENDER_OTHER, -1
         new_features = torch.from_numpy(np.array(new_features))
         scores = self.batch_predict(self.gender_no_pure_model, new_features)
         f_avg = sum(scores[:, 0]) / len(scores)
         m_avg = sum(scores[:, 1]) / len(scores)
         female_rate = f_avg / (f_avg + m_avg)
         if female_rate > 0.75:
             return GENDER_FEMALE, female_rate
         if female_rate < 0.1:
             return GENDER_MALE, female_rate
         logging.warning(
             "filename={}|predict_no_pure|other|len={}|rate={}".format(filename, new_feature_len, new_feature_rate)
         )
         return GENDER_OTHER, female_rate
 
     def predict(self, filename, features):
         st = time.time()
         new_features = []
         for i in range(FRAME_LEN, len(features), FRAME_LEN):
             new_features.append(features[i - FRAME_LEN: i])
         new_features = torch.from_numpy(np.array(new_features))
         gender, rate = self.predict_pure(filename, new_features)
         if gender == GENDER_OTHER:
             logging.info("start no pure process...")
             return self.predict_no_pure(filename, new_features)
         print("predict|spend_time={}".format(time.time() - st))
         return gender, rate
 
     def process_one_logic(self, filename, file_path, cache_dir):
         tmp_wav = os.path.join(cache_dir, "tmp.wav")
         tmp_vb_wav = os.path.join(cache_dir, "tmp_vb.wav")
         if not transcode(file_path, tmp_wav):
             return ERR_CODE_TRANSCODE
         if not volume_balanced(tmp_wav, tmp_vb_wav):
             return ERR_CODE_VOLUME_BALANCED
         features = get_one_mfcc(tmp_vb_wav)
         if len(features) < FRAME_LEN:
             logging.error("feature too short|file_path={}".format(file_path))
             return ERR_CODE_FEATURE_TOO_SHORT
         return self.predict(filename, features)
 
     def process_one(self, file_path):
         base_dir = os.path.dirname(file_path)
         filename = os.path.splitext(file_path)[0]
         cache_dir = os.path.join(base_dir, filename + "_cache")
         if os.path.exists(cache_dir):
             shutil.rmtree(cache_dir)
         os.makedirs(cache_dir)
         ret = self.process_one_logic(filename, file_path, cache_dir)
         shutil.rmtree(cache_dir)
         return ret
 
     def process(self, file_path):
         gender, female_rate = self.process_one(file_path)
         logging.info("{}|gender={}|female_rate={}".format(file_path, gender, female_rate))
         return gender, female_rate
 
     def process_by_feature(self, feature_file):
         """
         直接处理特征文件
         :param feature_file:
         :return:
         """
         filename = os.path.splitext(feature_file)[0]
         features = np.load(feature_file)
         gender, female_rate = self.predict(filename, features)
         return gender, female_rate
 
 
 def test_all_feature():
     import glob
     base_dir = "/data/datasets/music_voice_dataset_full/feature_online_data_v3"
     female = glob.glob(os.path.join(base_dir, "female/*feature.npy"))
     male = glob.glob(os.path.join(base_dir, "male/*feature.npy"))
     other = glob.glob(os.path.join(base_dir, "other/*feature.npy"))
     model_path = "/data/jianli.yang/voice_classification/online/models"
     music_voice_pure_model = os.path.join(model_path, "voice_005_rec_v5.pth")
     music_voice_no_pure_model = os.path.join(model_path, "voice_10_v5.pth")
     gender_pure_model = os.path.join(model_path, "gender_8k_ratev5_v6_adam.pth")
     gender_no_pure_model = os.path.join(model_path, "gender_8k_v6_adam.pth")
     vc = VoiceClass(music_voice_pure_model, music_voice_no_pure_model, gender_pure_model, gender_no_pure_model)
 
     tot_st = time.time()
     ret_map = {
         0: {0: 0, 1: 0, 2: 0},
         1: {0: 0, 1: 0, 2: 0},
         2: {0: 0, 1: 0, 2: 0}
     }
     for file in female:
         st = time.time()
         print("------------------------------>>>>>")
         gender, female_score = vc.process_by_feature(file)
         ret_map[0][gender] += 1
         if gender != 0:
             print("err:female->{}|{}|{}".format(gender, file, female_score))
         print("process|spend_tm=={}".format(time.time() - st))
 
     for file in male:
         st = time.time()
         print("------------------------------>>>>>")
         gender, female_score = vc.process_by_feature(file)
         ret_map[1][gender] += 1
         if gender != 1:
             print("err:male->{}|{}|{}".format(gender, file, female_score))
         print("process|spend_tm=={}".format(time.time() - st))
 
     for file in other:
         st = time.time()
         print("------------------------------>>>>>")
         gender, female_score = vc.process_by_feature(file)
         ret_map[2][gender] += 1
         if gender != 2:
             print("err:other->{}|{}|{}".format(gender, file, female_score))
         print("process|spend_tm=={}".format(time.time() - st))
 
     global transcode_time, vb_time, mfcc_time, predict_time
     print("spend_time:tot={}|transcode={}|vb={}|gen_feature={}|predict={}".format(time.time() - tot_st, transcode_time,
                                                                                   vb_time, mfcc_time, predict_time))
     f_f = ret_map[0][0]
     f_m = ret_map[0][1]
     f_o = ret_map[0][2]
     m_f = ret_map[1][0]
     m_m = ret_map[1][1]
     m_o = ret_map[1][2]
     o_f = ret_map[2][0]
     o_m = ret_map[2][1]
     o_o = ret_map[2][2]
 
     print("ff:{},fm:{},fo:{}".format(f_f, f_m, f_o))
     print("mm:{},mf:{},mo:{}".format(m_m, m_f, m_o))
     print("om:{},of:{},oo:{}".format(o_m, o_f, o_o))
     # 女性准确率和召回率
     f_acc = f_f / (f_f + m_f + o_f)
     f_recall = f_f / (f_f + f_m + f_o)
     # 男性准确率和召回率
     m_acc = m_m / (m_m + f_m + o_m)
     m_recall = m_m / (m_m + m_f + m_o)
     print("female: acc={}|recall={}".format(f_acc, f_recall))
     print("male: acc={}|recall={}".format(m_acc, m_recall))
 
 
 def test_all():
     import glob
     base_dir = "/data/datasets/music_voice_dataset_full/online_data_v3_top200"
     female = glob.glob(os.path.join(base_dir, "female/*mp4"))
     male = glob.glob(os.path.join(base_dir, "male/*mp4"))
     other = glob.glob(os.path.join(base_dir, "other/*mp4"))
     model_path = "/data/jianli.yang/voice_classification/online/models"
     music_voice_pure_model = os.path.join(model_path, "voice_005_rec_v5.pth")
     music_voice_no_pure_model = os.path.join(model_path, "voice_10_v5.pth")
     gender_pure_model = os.path.join(model_path, "gender_8k_ratev5_v6_adam.pth")
     gender_no_pure_model = os.path.join(model_path, "gender_8k_v6_adam.pth")
     vc = VoiceClass(music_voice_pure_model, music_voice_no_pure_model, gender_pure_model, gender_no_pure_model)
 
     tot_st = time.time()
     ret_map = {
         0: {0: 0, 1: 0, 2: 0},
         1: {0: 0, 1: 0, 2: 0},
         2: {0: 0, 1: 0, 2: 0}
     }
     for file in female:
         st = time.time()
         print("------------------------------>>>>>")
         gender, female_score = vc.process(file)
         ret_map[0][gender] += 1
         if gender != 0:
             print("err:female->{}|{}|{}".format(gender, file, female_score))
         print("process|spend_tm=={}".format(time.time() - st))
 
     for file in male:
         st = time.time()
         print("------------------------------>>>>>")
         gender, female_score = vc.process(file)
         ret_map[1][gender] += 1
         if gender != 1:
             print("err:male->{}|{}|{}".format(gender, file, female_score))
         print("process|spend_tm=={}".format(time.time() - st))
 
     for file in other:
         st = time.time()
         print("------------------------------>>>>>")
         gender, female_score = vc.process(file)
         ret_map[2][gender] += 1
         if gender != 2:
             print("err:other->{}|{}|{}".format(gender, file, female_score))
         print("process|spend_tm=={}".format(time.time() - st))
 
     global transcode_time, vb_time, mfcc_time, predict_time
     print("spend_time:tot={}|transcode={}|vb={}|gen_feature={}|predict={}".format(time.time() - tot_st, transcode_time,
                                                                                   vb_time, mfcc_time, predict_time))
     f_f = ret_map[0][0]
     f_m = ret_map[0][1]
     f_o = ret_map[0][2]
     m_f = ret_map[1][0]
     m_m = ret_map[1][1]
     m_o = ret_map[1][2]
     o_f = ret_map[2][0]
     o_m = ret_map[2][1]
     o_o = ret_map[2][2]
 
     print("ff:{},fm:{},fo:{}".format(f_f, f_m, f_o))
     print("mm:{},mf:{},mo:{}".format(m_m, m_f, m_o))
     print("om:{},of:{},oo:{}".format(o_m, o_f, o_o))
     # 女性准确率和召回率
     f_acc = f_f / (f_f + m_f + o_f)
     f_recall = f_f / (f_f + f_m + f_o)
     # 男性准确率和召回率
     m_acc = m_m / (m_m + f_m + o_m)
     m_recall = m_m / (m_m + m_f + m_o)
     print("female: acc={}|recall={}".format(f_acc, f_recall))
     print("male: acc={}|recall={}".format(m_acc, m_recall))
 
 
 if __name__ == "__main__":
     # test_all()
     # test_all_feature()
     model_path = sys.argv[1]
     voice_path = sys.argv[2]
     music_voice_pure_model = os.path.join(model_path, "voice_005_rec_v5.pth")
     music_voice_no_pure_model = os.path.join(model_path, "voice_10_v5.pth")
     gender_pure_model = os.path.join(model_path, "gender_8k_ratev5_v6_adam.pth")
     gender_no_pure_model = os.path.join(model_path, "gender_8k_v6_adam.pth")
     vc = VoiceClass(music_voice_pure_model, music_voice_no_pure_model, gender_pure_model, gender_no_pure_model)
     for i in range(0, 1):
         st = time.time()
         print("------------------------------>>>>>")
         vc.process(voice_path)
         print("process|spend_tm=={}".format(time.time() - st))
diff --git a/AutoCoverTool/script/get_song_url.py b/AutoCoverTool/script/get_song_url.py
index 2635982..9b42cdf 100644
--- a/AutoCoverTool/script/get_song_url.py
+++ b/AutoCoverTool/script/get_song_url.py
@@ -1,546 +1,128 @@
 """
 获取歌曲的地址
 
 # song_src=2 是来源108和109的歌曲，未被洗过的
 # song_src=1 是曲库给的
 # song_src=3 # 用于轻变调的
 """
 from script.common import *
 from copy import deepcopy
 from online.common import update_db
 
 
 def get_url_by_song_id(song_id):
     sql = "select task_url,starmaker_songid from silence where starmaker_songid = {} order by task_id desc limit 1".format(
         song_id)
     ban = deepcopy(banned_user_map)
     ban["db"] = "starmaker_musicbook"
     data = get_data_by_mysql(sql, ban)
     if len(data) > 0:
         return data[0][0]
     return None
 
 
 def process():
     arr = [
-        "611752105030534669",
-        "611752105030534671",
-        "611752105029291860",
-        "611752105030534665",
-        "611752105030534667",
-        "611752105027734182",
-        "611752105030532701",
-        "611752105027626957",
-        "611752105030532696",
-        "611752105030517855",
-        "611752105025184103",
-        "611752105030517856",
-        "611752105024164143",
-        "611752105030517852",
-        "611752105026681421",
-        "611752105024571437",
-        "611752105022779865",
-        "611752105030517845",
-        "611752105030493464",
-        "611752105030517843",
-        "611752105030517018",
-        "611752105030517014",
-        "611752105022838003",
-        "611752105024118499",
-        "611752105030517015",
-        "611752105030517012",
-        "611752105025587378",
-        "611752105023644389",
-        "611752105023616289",
-        "611752105025502433",
-        "611752105030517008",
-        "611752105024199100",
-        "611752105030517003",
-        "611752105026614996",
-        "611752105029087255",
-        "611752105030517518",
-        "611752105030517840",
-        "611752105030517844",
-        "611752105030517859",
-        "611752105022777749",
-        "611752105030532705",
-        "611752105026265975",
-        "611752105030532703",
-        "611752105030487368",
-        "611752105030483712",
-        "611752105030517850",
-        "611752105022782535",
-        "611752105030517021",
-        "611752105030517854",
-        "611752105023541359",
-        "611752105029792918",
-        "611752105024199117",
-        "611752105029673594",
-        "611752105023674624",
-        "611752105028990732",
-        "611752105028487811",
-        "611752105024194923",
-        "611752105023763921",
-        "611752105023434554",
-        "611752105022838184",
-        "611752105030553757",
-        "611752105027326100",
-        "611752105023977089",
-        "611752105023674617",
-        "611752105023620980",
-        "611752105023536537",
-        "611752105023301456",
-        "611752105022842491",
-        "611752105022841266",
-        "611752105030548398",
-        "611752105022839768",
-        "611752105022838572",
-        "611752105022781228",
-        "611752105029598755",
-        "611752105030517531",
-        "611752105023541369",
-        "611752105023678576",
-        "611752105023346238",
-        "611752105027648094",
-        "611752105027326090",
-        "611752105027832575",
-        "611752105030478339",
-        "611752105027795229",
-        "611752105027734187",
-        "611752105022614618",
-        "611752105023329571",
-        "611752105023234496",
-        "611752105026134338",
-        "611752105030554109",
-        "611752105030517489",
-        "611752105029836690",
-        "611752105030555726",
-        "611752105030555723",
-        "611752105030553598",
-        "611752105030555722",
-        "611752105029656131",
-        "611752105030555720",
-        "611752105023674607",
-        "611752105023478557",
-        "611752105030555716",
-        "611752105030555717",
-        "611752105022784360",
-        "611752105022836164",
-        "611752105029711726",
-        "611752105022783553",
-        "611752105030555714",
-        "611752105022824550",
-        "611752105022838413",
-        "611752105022783797",
-        "611752105022781620",
-        "611752105022775924",
-        "611752105022774759",
-        "611752105022759877",
-        "611752105022785234",
-        "611752105030555710",
-        "611752105030555712",
-        "611752105022768451",
-        "611752105022767499",
-        "611752105030555707",
-        "611752105022755443",
-        "611752105022777511",
-        "611752105022775921",
-        "611752105022841864",
-        "611752105022766831",
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+        "611752105029432787",
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     ]
 
     ban = deepcopy(banned_user_map)
     ban["db"] = "av_db"
 
     for sid in arr:
         url = get_url_by_song_id(sid)
         if url is not None:
             print("out,{},{}".format(url, sid))
             # 只要没有对外输出过，均可以向其中填充
-            sql = "select song_id from svc_queue_table where song_id={} and (song_src in (3, 4, 5) and state=2)".format(sid)
+            sql = "select song_id from svc_queue_table where song_id={} and (song_src in (3, 4, 5) and state=2)".format(
+                sid)
             data = get_data_by_mysql(sql, ban)
             if len(data) == 0:
                 tm = int(time.time())
-                sql = "replace INTO svc_queue_table (song_id, url, create_time, update_time, song_src) VALUES ({}, \"{}\",{}, {}, 4)" \
+                sql = "replace INTO svc_queue_table (song_id, url, create_time, update_time, song_src) VALUES ({}, \"{}\",{}, {}, 5)" \
                     .format(sid, url, tm, tm)
                 update_db(sql, ban)
 
 
 def get_data_from_song():
     sql = """
     select tb1.song_id, tb1.recording_count
     from (
         select song_id,recording_count
         from starmaker.song
         where song_src in (108,109) and song_status = 2
         order by recording_count desc
     ) as tb1
     left join 
     (
         select song_id
         from av_db.svc_queue_table
     ) as tb2
     on tb1.song_id = tb2.song_id
     where tb2.song_id is null
     order by tb1.recording_count desc limit 5000
     """
     ban = deepcopy(banned_user_map)
     ban_v1 = deepcopy(banned_user_map)
     ban["db"] = "starmaker_musicbook"
     ban_v1["db"] = "av_db"
     data = get_data_by_mysql(sql, ban)
     for dt in data:
         sid = dt[0]
         url = get_url_by_song_id(sid)
         if url is not None:
             print("out,{},{}".format(url, sid))
             tm = int(time.time())
             sql = "insert INTO svc_queue_table (song_id, url, create_time, update_time, song_src) VALUES ({}, \"{}\", {}, {}, 3)" \
                 .format(sid, url, tm, tm)
             update_db(sql, ban_v1)
 
 
 if __name__ == '__main__':
     # get_diff_song()
     # get_data_from_song()
     process()