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diff --git a/AIMeiSheng/docker_demo/common.py b/AIMeiSheng/docker_demo/common.py
index 6d28a72..098cba8 100644
--- a/AIMeiSheng/docker_demo/common.py
+++ b/AIMeiSheng/docker_demo/common.py
@@ -1,112 +1,121 @@
import os
import sys
import time
# import logging
import urllib, urllib.request
# 测试/正式环境
-gs_prod = False
-if len(sys.argv) > 1 and sys.argv[1] == "prod":
- gs_prod = True
-print(gs_prod)
+gs_prod = True
+# if len(sys.argv) > 1 and sys.argv[1] == "prod":
+# gs_prod = True
+# print(gs_prod)
gs_tmp_dir = "/tmp/ai_meisheng_tmp"
gs_model_dir = "/tmp/ai_meisheng_models"
gs_resource_cache_dir = "/tmp/ai_meisheng_resource_cache"
gs_embed_model_path = os.path.join(gs_model_dir, "RawNet3/models/weights/model.pt")
gs_svc_model_path = os.path.join(gs_model_dir,
"weights/xusong_v2_org_version_alldata_embed_spkenx200x_double_e14_s90706.pth")
gs_hubert_model_path = os.path.join(gs_model_dir, "hubert.pt")
gs_rmvpe_model_path = os.path.join(gs_model_dir, "rmvpe.pt")
gs_embed_model_spk_path = os.path.join(gs_model_dir, "SpeakerEncoder/pretrained_model/best_model.pth.tar")
gs_embed_config_spk_path = os.path.join(gs_model_dir, "SpeakerEncoder/pretrained_model/config.json")
# errcode
gs_err_code_success = 0
gs_err_code_download_vocal = 100
gs_err_code_download_svc_url = 101
gs_err_code_svc_process = 102
gs_err_code_transcode = 103
gs_err_code_volume_adjust = 104
gs_err_code_upload = 105
gs_err_code_params = 106
gs_err_code_pending = 107
gs_err_code_target_silence = 108
gs_err_code_too_many_connections = 429
gs_redis_conf = {
"host": "av-credis.starmaker.co",
"port": 6379,
"pwd": "lKoWEhz%jxTO",
}
gs_server_redis_conf = {
"producer": "test_ai_meisheng_producer", # 输入的队列
"ai_meisheng_key_prefix": "test_ai_meisheng_key_", # 存储结果情况
}
if gs_prod:
gs_server_redis_conf = {
"producer": "ai_meisheng_producer", # 输入的队列
"ai_meisheng_key_prefix": "ai_meisheng_key_", # 存储结果情况
}
+gs_feishu_conf = {
+ "url": "http://sg-prod-songbook-webmp-1:8000/api/feishu/people",
+ "users": [
+ "18810833785", # 杨建利
+ "17778007843", # 王健军
+ "18612496315" # 郭子豪
+ ]
+}
+
def download2disk(url, dst_path):
try:
urllib.request.urlretrieve(url, dst_path)
return os.path.exists(dst_path)
except Exception as ex:
print(f"download url={url} error", ex)
return False
def exec_cmd(cmd):
# gs_logger.info(cmd)
print(cmd)
ret = os.system(cmd)
if ret != 0:
return False
return True
def exec_cmd_and_result(cmd):
r = os.popen(cmd)
text = r.read()
r.close()
return text
def upload_file2cos(key, file_path, region='ap-singapore', bucket_name='av-audit-sync-sg-1256122840'):
"""
将文件上传到cos
:param key: 桶上的具体地址
:param file_path: 本地文件地址
:param region: 区域
:param bucket_name: 桶地址
:return:
"""
gs_coscmd = "coscmd"
gs_coscmd_conf = "~/.cos.conf"
cmd = "{} -c {} -r {} -b {} upload {} {}".format(gs_coscmd, gs_coscmd_conf, region, bucket_name, file_path, key)
if exec_cmd(cmd):
cmd = "{} -c {} -r {} -b {} info {}".format(gs_coscmd, gs_coscmd_conf, region, bucket_name, key) \
+ "| grep Content-Length |awk \'{print $2}\'"
res_str = exec_cmd_and_result(cmd)
# logging.info("{},res={}".format(key, res_str))
size = float(res_str)
if size > 0:
return True
return False
return False
def check_input(input_data):
key_list = ["record_song_url", "target_url", "start", "end", "vocal_loudness", "female_recording_url",
"male_recording_url"]
for key in key_list:
if key not in input_data.keys():
return False
return True
diff --git a/AIMeiSheng/docker_demo/feishu_helper.py b/AIMeiSheng/docker_demo/feishu_helper.py
new file mode 100644
index 0000000..7f0c492
--- /dev/null
+++ b/AIMeiSheng/docker_demo/feishu_helper.py
@@ -0,0 +1,11 @@
+import json
+import requests
+
+
+def feishu_send(feishu_url, msg, users):
+ data = {
+ "mobiles": users,
+ "msg": msg
+ }
+ ret = requests.post(feishu_url, json.dumps(data))
+ print(ret)
diff --git a/AIMeiSheng/docker_demo/http_server.py b/AIMeiSheng/docker_demo/http_server.py
index 4649ad5..ef40068 100644
--- a/AIMeiSheng/docker_demo/http_server.py
+++ b/AIMeiSheng/docker_demo/http_server.py
@@ -1,83 +1,91 @@
# -*- coding: UTF-8 -*-
"""
SVC处理逻辑
1. 根据跟定的vocal_url 判别男女
2. 根据男女信息选择适合的男女url
3. 模型推理
"""
import gc
import os
import sys
sys.path.append(os.path.dirname(__file__))
sys.path.append(os.path.join(os.path.dirname(__file__), "../"))
import json
import time
import socket
import logging
import hashlib
from flask import Flask, jsonify, request, abort
from redis_helper import RedisHelper
+from feishu_helper import feishu_send
from common import *
# 全局设置
logging.basicConfig(format='%(asctime)s %(levelname)s %(message)s', datefmt='%Y-%m-%d %I:%M:%S', level=logging.INFO)
app = Flask(__name__)
class HttpServer:
def __init__(self, redis_conf, server_conf):
st = time.time()
self.redis_helper = RedisHelper(redis_conf)
self.server_conf = server_conf
+ self.last_send_time = time.time()
+ self.time_interval = 60
logging.info(f"HttpServer init conf={redis_conf}, {server_conf} sp={time.time() - st}")
def process(self, in_data):
msg = {
"status": gs_err_code_params,
"schedule": 100,
"gender": "unknown",
"target_song_url": "",
}
if not check_input(in_data):
return msg
- if self.redis_helper.llen(self.server_conf["producer"]) > 10:
+ num = self.redis_helper.llen(self.server_conf["producer"])
+ if num > 10:
msg["status"] = gs_err_code_too_many_connections
+ if time.time() - self.last_send_time > 60:
+ err_msg = f"ai meisheng process_async too many connections:{num}"
+ self.last_send_time = time.time()
+ feishu_send(gs_feishu_conf["url"], err_msg, gs_feishu_conf["users"])
return msg
distinct_id = hashlib.md5(in_data["record_song_url"].encode()).hexdigest()
distinct_key = self.server_conf["ai_meisheng_key_prefix"] + distinct_id
if not self.redis_helper.exists(distinct_key):
msg["status"] = gs_err_code_pending
self.redis_helper.set(distinct_key, json.dumps(msg))
self.redis_helper.lpush(self.server_conf["producer"], json.dumps(in_data))
self.redis_helper.expire(distinct_key, 15)
msg = self.redis_helper.get(distinct_key)
return json.loads(msg)
gs_http_server = HttpServer(gs_redis_conf, gs_server_redis_conf)
@app.route("/ai_meisheng", methods=["POST"])
def ai_meisheng():
data = request.json
st = time.time()
logging.info(f"ai_meisheng:in:{data}")
msg = gs_http_server.process(data)
json_msg = jsonify(msg)
logging.info(f"ai_meisheng:out:{data}-{msg}, sp={time.time() - st}")
return json_msg
if __name__ == "__main__":
app.run(host='0.0.0.0', port=5000, threaded=False)

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