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diff --git a/AIMeiSheng/myinfer_multi_spk_embed_in_dec_diff_fi_meisheng.py b/AIMeiSheng/myinfer_multi_spk_embed_in_dec_diff_fi_meisheng.py
index b68dc3b..f1da5a9 100644
--- a/AIMeiSheng/myinfer_multi_spk_embed_in_dec_diff_fi_meisheng.py
+++ b/AIMeiSheng/myinfer_multi_spk_embed_in_dec_diff_fi_meisheng.py
@@ -1,215 +1,217 @@
import os,sys,pdb,torch
now_dir = os.getcwd()
sys.path.append(now_dir)
import argparse
import glob
import sys
import torch
from multiprocessing import cpu_count
class Config:
def __init__(self,device,is_half):
self.device = device
self.is_half = is_half
self.n_cpu = 0
self.gpu_name = None
self.gpu_mem = None
self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
def device_config(self) -> tuple:
+ current_dir = os.path.dirname(os.path.abspath(__file__))
+ config_path = os.path.join(current_dir, "configs")
if torch.cuda.is_available():
i_device = int(self.device.split(":")[-1])
self.gpu_name = torch.cuda.get_device_name(i_device)
if (
("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
or "P40" in self.gpu_name.upper()
or "1060" in self.gpu_name
or "1070" in self.gpu_name
or "1080" in self.gpu_name
):
print("16系/10系显卡和P40强制单精度")
self.is_half = False
for config_file in ["32k.json", "40k.json", "48k.json"]:
- with open(f"configs/{config_file}", "r") as f:
+ with open(f"{config_path}/{config_file}", "r") as f:
strr = f.read().replace("true", "false")
- with open(f"configs/{config_file}", "w") as f:
+ with open(f"{config_path}/{config_file}", "w") as f:
f.write(strr)
- with open("trainset_preprocess_pipeline_print.py", "r") as f:
+ with open(f"{current_dir}/trainset_preprocess_pipeline_print.py", "r") as f:
strr = f.read().replace("3.7", "3.0")
- with open("trainset_preprocess_pipeline_print.py", "w") as f:
+ with open(f"{current_dir}/trainset_preprocess_pipeline_print.py", "w") as f:
f.write(strr)
else:
self.gpu_name = None
self.gpu_mem = int(
torch.cuda.get_device_properties(i_device).total_memory
/ 1024
/ 1024
/ 1024
+ 0.4
)
if self.gpu_mem <= 4:
- with open("trainset_preprocess_pipeline_print.py", "r") as f:
+ with open(f"{current_dir}/trainset_preprocess_pipeline_print.py", "r") as f:
strr = f.read().replace("3.7", "3.0")
- with open("trainset_preprocess_pipeline_print.py", "w") as f:
+ with open(f"{current_dir}/trainset_preprocess_pipeline_print.py", "w") as f:
f.write(strr)
elif torch.backends.mps.is_available():
print("没有发现支持的N卡, 使用MPS进行推理")
self.device = "mps"
else:
print("没有发现支持的N卡, 使用CPU进行推理")
self.device = "cpu"
self.is_half = True
if self.n_cpu == 0:
self.n_cpu = cpu_count()
if self.is_half:
# 6G显存配置
x_pad = 3
x_query = 10
x_center = 80 #60
x_max = 85#65
else:
# 5G显存配置
x_pad = 1
x_query = 6
x_center = 38
x_max = 41
if self.gpu_mem != None and self.gpu_mem <= 4:
x_pad = 1
x_query = 5
x_center = 30
x_max = 32
return x_pad, x_query, x_center, x_max
index_path="./logs/xusong_v2_org_version_multispk_charlie_puth_embed_in_dec_muloss_show/added_IVF614_Flat_nprobe_1_xusong_v2_org_version_multispk_charlie_puth_embed_in_dec_show_v2.index"
# f0method="rmvpe" #harvest or pm
index_rate=float("0.0") #index rate
device="cuda:0"
is_half=True
filter_radius=int(3) ##3
resample_sr=int(0) # 0
rms_mix_rate=float(1) # rms混合比例 1,不等于1混合
protect=float(0.33 )## ??? 0.33 fang
#print(sys.argv)
config=Config(device,is_half)
now_dir=os.getcwd()
sys.path.append(now_dir)
from vc_infer_pipeline_org_embed import VC
from lib.infer_pack.models_embed_in_dec_diff_fi import (
SynthesizerTrnMs256NSFsid,
SynthesizerTrnMs256NSFsid_nono,
SynthesizerTrnMs768NSFsid,
SynthesizerTrnMs768NSFsid_nono,
)
from lib.audio import load_audio
from fairseq import checkpoint_utils
from scipy.io import wavfile
from AIMeiSheng.docker_demo.common import gs_hubert_model_path
# hubert_model=None
def load_hubert():
# global hubert_model
models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task([gs_hubert_model_path],suffix="",)
#models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(["checkpoint_best_legacy_500.pt"],suffix="",)
hubert_model = models[0]
hubert_model = hubert_model.to(device)
if(is_half):hubert_model = hubert_model.half()
else:hubert_model = hubert_model.float()
hubert_model.eval()
return hubert_model
def vc_single(sid,input_audio,f0_up_key,f0_file,f0_method,file_index,index_rate,hubert_model,paras):
global tgt_sr,net_g,vc,version
if input_audio is None:return "You need to upload an audio", None
f0_up_key = int(f0_up_key)
# print("@@xxxf0_up_key:",f0_up_key)
audio = load_audio(input_audio,16000)
if paras != None:
st = int(paras['tst'] * 16000/1000)
en = len(audio)
if paras['tnd'] != None:
en = min(en,int(paras['tnd'] * 16000/1000))
audio = audio[st:en]
times = [0, 0, 0]
if(hubert_model==None):
hubert_model = load_hubert()
if_f0 = cpt.get("f0", 1)
audio_opt=vc.pipeline_mulprocess(hubert_model,net_g,sid,audio,input_audio,times,f0_up_key,f0_method,file_index,index_rate,if_f0,filter_radius,tgt_sr,resample_sr,rms_mix_rate,version,protect,f0_file=f0_file)
#print(times)
#print("@@using multi process")
return audio_opt
def get_vc_core(model_path,is_half):
#print("loading pth %s" % model_path)
cpt = torch.load(model_path, map_location="cpu")
tgt_sr = cpt["config"][-1]
cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
if_f0 = cpt.get("f0", 1)
version = cpt.get("version", "v1")
if version == "v1":
if if_f0 == 1:
net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
else:
net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
elif version == "v2":
if if_f0 == 1: #
net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=is_half)
else:
net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
#print("load model finished")
del net_g.enc_q
net_g.load_state_dict(cpt["weight"], strict=False)
#print("load net_g finished")
return tgt_sr,net_g,cpt,version
def get_vc1(model_path,is_half):
tgt_sr, net_g, cpt, version = get_vc_core(model_path, is_half)
net_g.eval().to(device)
if (is_half):net_g = net_g.half()
else:net_g = net_g.float()
vc = VC(tgt_sr, config)
n_spk=cpt["config"][-3]
return
def get_rmvpe(model_path="rmvpe.pt"):
from lib.rmvpe import RMVPE
global f0_method
#print("loading rmvpe model")
f0_method = RMVPE(model_path, is_half=True, device='cuda')
return f0_method
def get_vc(model_path):
global n_spk,tgt_sr,net_g,vc,cpt,device,is_half,version
tgt_sr, net_g, cpt, version = get_vc_core(model_path, is_half)
net_g.eval().to(device)
if (is_half):net_g = net_g.half()
else:net_g = net_g.float()
vc = VC(tgt_sr, config)
n_spk=cpt["config"][-3]
# return {"visible": True,"maximum": n_spk, "__type__": "update"}
# return net_g
def svc_main(input_path,opt_path,sid_embed,f0up_key=0,hubert_model=None, paras=None):
#print("sid_embed: ",sid_embed)
wav_opt = vc_single(sid_embed,input_path,f0up_key,None,f0_method,index_path,index_rate,hubert_model,paras)
#print("out_path: ",opt_path)
wavfile.write(opt_path, tgt_sr, wav_opt)
diff --git a/AutoCoverTool/script/common.py b/AutoCoverTool/script/common.py
index ab4b5a4..cb472a0 100644
--- a/AutoCoverTool/script/common.py
+++ b/AutoCoverTool/script/common.py
@@ -1,181 +1,181 @@
# -*-encoding=utf8-*-
import time
import pymysql
import logging
import pandas as pd
# from impala.dbapi import connect
# from sqlalchemy import create_engine
# from sqlalchemy.types import NVARCHAR, Float, Integer
banned_user_map = {
"host": "sg-songbook00.db.starmaker.co",
"user": "worker",
"passwd": "gRYppQtdTpP3nFzH",
"db": "starmaker"
}
banned_user_map_v1 = {
"host": "sg-starmaker-device-r2.db.starmaker.co",
"user": "worker",
"passwd": "gRYppQtdTpP3nFzH",
"db": "mis"
}
banned_user_map_v2 = {
"host": "sg-sm-img-r1.starmaker.co",
"user": "worker",
"passwd": "gRYppQtdTpP3nFzH",
"db": "sm"
}
# 做一下shared库的查询依赖
shard_map = {
"shard_sm_12": "sg-shard02-r2.db.starmaker.co",
"shard_sm_13": "sg-shard02-r2.db.starmaker.co",
"shard_sm_14": "sg-shard02-r2.db.starmaker.co",
"shard_sm_15": "sg-shard02-r2.db.starmaker.co",
"shard_sm_30": "sg-shard02-r2.db.starmaker.co",
"shard_sm_31": "sg-shard02-r2.db.starmaker.co",
"shard_sm_20": "sg-shard02-r2.db.starmaker.co",
"shard_sm_21": "sg-shard02-r2.db.starmaker.co",
"shard_sm_22": "sg-shard03-r2.db.starmaker.co",
"shard_sm_23": "sg-shard03-r2.db.starmaker.co",
"shard_sm_24": "sg-shard03-r2.db.starmaker.co",
"shard_sm_25": "sg-shard03-r2.db.starmaker.co",
"shard_sm_26": "sg-shard03-r2.db.starmaker.co",
"shard_sm_27": "sg-shard03-r2.db.starmaker.co",
"shard_sm_28": "sg-shard03-r2.db.starmaker.co",
"shard_sm_29": "sg-shard03-r2.db.starmaker.co",
"shard_sm_0": "sg-shard00-r2.db.starmaker.co",
"shard_sm_1": "sg-shard00-r2.db.starmaker.co",
"shard_sm_2": "sg-shard00-r2.db.starmaker.co",
"shard_sm_3": "sg-shard00-r2.db.starmaker.co",
"shard_sm_4": "sg-shard00-r2.db.starmaker.co",
"shard_sm_5": "sg-shard00-r2.db.starmaker.co",
"shard_sm_16": "sg-shard00-r2.db.starmaker.co",
"shard_sm_17": "sg-shard00-r2.db.starmaker.co",
"shard_sm_6": "sg-shard01-r2.db.starmaker.co",
"shard_sm_7": "sg-shard01-r2.db.starmaker.co",
"shard_sm_8": "sg-shard01-r2.db.starmaker.co",
"shard_sm_9": "sg-shard01-r2.db.starmaker.co",
"shard_sm_10": "sg-shard01-r2.db.starmaker.co",
"shard_sm_11": "sg-shard01-r2.db.starmaker.co",
"shard_sm_18": "sg-shard01-r2.db.starmaker.co",
"shard_sm_19": "sg-shard01-r2.db.starmaker.co",
"shard_sm_32": "sg-shard04-r2.db.starmaker.co",
"shard_sm_33": "sg-shard04-r2.db.starmaker.co",
"shard_sm_34": "sg-shard04-r2.db.starmaker.co",
"shard_sm_35": "sg-shard04-r2.db.starmaker.co",
"shard_sm_36": "sg-shard04-r2.db.starmaker.co",
"shard_sm_37": "sg-shard04-r2.db.starmaker.co",
"shard_sm_38": "sg-shard04-r2.db.starmaker.co",
"shard_sm_39": "sg-shard04-r2.db.starmaker.co",
"shard_sm_40": "sg-shard05-r2.db.starmaker.co",
"shard_sm_41": "sg-shard05-r2.db.starmaker.co",
"shard_sm_42": "sg-shard05-r2.db.starmaker.co",
"shard_sm_43": "sg-shard05-r2.db.starmaker.co",
"shard_sm_44": "sg-shard05-r2.db.starmaker.co",
"shard_sm_45": "sg-shard05-r2.db.starmaker.co",
"shard_sm_46": "sg-shard05-r2.db.starmaker.co",
"shard_sm_47": "sg-shard05-r2.db.starmaker.co",
"shard_sm_48": "sg-shard05-r2.db.starmaker.co",
"shard_sm_49": "sg-shard05-r2.db.starmaker.co",
"shard_sm_50": "sg-shard05-r2.db.starmaker.co",
"name": "shard_sm_{}",
"port": 3306,
"user": "readonly",
"passwd": "JKw6woZgRXsveegL"
}
def connect_db(host="research-db-r1.starmaker.co", port=3306, user="root", passwd="Qrdl1130", db=""):
- print("connect mysql host={} port={} user={} passwd={} db={}".format(host, port, user, passwd, db))
+ # print("connect mysql host={} port={} user={} passwd={} db={}".format(host, port, user, passwd, db))
return pymysql.connect(host=host, port=port, user=user, passwd=passwd, db=db)
def get_data_by_mysql(sql, ban=banned_user_map):
db = connect_db(host=ban["host"], passwd=ban["passwd"], user=ban["user"],
db=ban["db"])
db_cursor = db.cursor()
if len(sql) < 100:
print("execute = {}".format(sql))
else:
print("execute = {}...".format(sql[:100]))
db_cursor.execute(sql)
res = db_cursor.fetchall()
db_cursor.close()
db.close()
print("res size={}".format(len(res)))
return res
def get_shard_db(user_id):
return int(float(user_id)) >> 48
def get_shard_data_by_sql(sql, user_id):
shard_id = get_shard_db(user_id)
db_name = shard_map["name"].format(shard_id)
host = shard_map[db_name]
db = connect_db(host=host, passwd=shard_map["passwd"], user=shard_map["user"], db=db_name)
db_cursor = db.cursor()
- if len(sql) < 100:
- print("execute = {}".format(sql))
- else:
- print("execute = {}...".format(sql[:100]))
+ # if len(sql) < 100:
+ # print("execute = {}".format(sql))
+ # else:
+ # print("execute = {}...".format(sql[:100]))
db_cursor.execute(sql)
res = db_cursor.fetchall()
db_cursor.close()
db.close()
- print("res size={}".format(len(res)))
+ # print("res size={}".format(len(res)))
return res
# def get_data_by_hql(sql):
# logging.info(sql)
# ntime = time.time()
# conn = connect(host='sg-hive.starmaker.co', port=7001, auth_mechanism='PLAIN', timeout=3600, user="hadoop",
# password="7396&pagesize")
# cur = conn.cursor()
# cur.execute(sql)
# data = cur.fetchall()
# cur.close()
# conn.close()
# logging.info("get sql: eps={}".format(time.time() - ntime))
# return data
def read_file(in_file):
with open(in_file, "r") as f:
lines = f.readlines()
return lines
def write2file(file_path, data):
with open(file_path, "w") as f:
for line in data:
line += "\n"
f.write(line)
# def map_types(df):
# dtypedict = {}
# for i, j in zip(df.columns, df.dtypes):
# if "object" in str(j):
# dtypedict.update({i: NVARCHAR(length=255)})
# if "float" in str(j):
# dtypedict.update({i: Float(precision=2, asdecimal=True)})
# if "int" in str(j):
# dtypedict.update({i: Integer()})
# return dtypedict
#
#
# def write2db(filename, tablename):
# engine = create_engine("mysql+mysqldb://{}:{}@{}/{}".format('root', '', 'localhost:3306', 'starmaker'))
# con = engine.connect()
# df = pd.read_csv(filename)
# dtypedict = map_types(df)
# df.to_sql(name=tablename, con=con, if_exists='append', index=False, dtype=dtypedict)
\ No newline at end of file
diff --git a/AutoCoverTool/script/get_user_recordings.py b/AutoCoverTool/script/get_user_recordings.py
index e589dc8..3b92a50 100644
--- a/AutoCoverTool/script/get_user_recordings.py
+++ b/AutoCoverTool/script/get_user_recordings.py
@@ -1,184 +1,257 @@
"""
获取用户数据
"""
import os
import time
import glob
import json
import librosa
import soundfile
from script.common import *
def exec_cmd(cmd):
r = os.popen(cmd)
text = r.read()
r.close()
return text
def get_d(audio_path):
cmd = "ffprobe -v quiet -print_format json -show_format -show_streams {}".format(audio_path)
data = exec_cmd(cmd)
data = json.loads(data)
if "format" in data.keys():
if "duration" in data['format']:
return float(data["format"]["duration"])
return 0
def get_user_recordings(user_id):
sql = "select id, recording_url from recording where user_id={} and created_on > {} and is_public = 1 and is_deleted = 0 and media_type in (1, 2, 3, 4, 9, 10) ".format(
user_id,
time.time() - 86400 * 30)
res = get_shard_data_by_sql(sql, user_id)
true_num = 0
for id, url in res:
if download_url(url, user_id, str(id)):
true_num += 1
if true_num > 15:
break
def download_url(url, uid, rid):
url = str(url).replace("master.mp4", "origin_master.mp4")
c_dir = "/data/rsync/jianli.yang/AutoCoverTool/data/train_users/0414_0514/{}".format(uid)
if not os.path.exists(c_dir):
os.makedirs(c_dir)
c_dir = os.path.join(c_dir, "src")
if not os.path.exists(c_dir):
os.makedirs(c_dir)
cmd = "wget {} -O {}/{}.mp4".format(url, c_dir, rid)
os.system(cmd)
# 转码为44k双声道音频
in_path = os.path.join(c_dir, rid + ".mp4")
if os.path.exists(in_path):
duration = get_d(in_path)
print("duration={}".format(duration))
if duration > 30:
dst_path = in_path.replace(".mp4", ".wav")
cmd = "ffmpeg -i {} -ar 44100 -ac 1 -y {}".format(in_path, dst_path)
print("exec={}".format(cmd))
os.system(cmd)
return os.path.exists(dst_path)
return False
def split_to_idx(ppath, dst_path, user_id):
frame_len = 32000 * 15
files = glob.glob(os.path.join(ppath, "*mp4"))
mmax = 0
for file in files:
try:
audio, sr = librosa.load(file, sr=32000, mono=True)
except Exception as ex:
continue
print("audio_len:={}".format(audio.shape))
for i in range(0, len(audio), frame_len):
if i + frame_len > len(audio):
break
cur_data = audio[i:i + frame_len]
out_path = os.path.join(dst_path, "{}_{}.wav".format(user_id, mmax))
print("save to {}".format(out_path))
# librosa.output.write_wav(out_path, cur_data, 32000)
soundfile.write(out_path, cur_data, 32000, format="wav")
mmax += 1
def process():
from online.beanstalk_helper import BeanstalkHelper
config = {"addr": "sg-test-common-box-1:11300", "consumer": "auto_cover_tool_download_user"}
bean_helper = BeanstalkHelper(config)
bean = bean_helper.get_beanstalkd()
bean.watch(config["consumer"])
while True:
payload = bean.reserve(5)
if not payload:
logging.info("bean sleep...")
continue
in_data = json.loads(payload.body)
user_id = in_data["user_id"]
try:
user_id_int = int(float(user_id))
get_user_recordings(in_data["user_id"])
except Exception as ex:
pass
payload.delete()
def put_data(file_path):
lines = []
with open(file_path, "r") as f:
while True:
line = f.readline().strip()
if not line:
break
lines.append(line)
from online.beanstalk_helper import BeanstalkHelper
config = {"addr": "sg-test-common-box-1:11300", "consumer": "auto_cover_tool_download_user"}
bean_helper = BeanstalkHelper(config)
for idx, line in enumerate(lines):
if idx == 0:
continue
user_id = line.split(",")[0]
message = json.dumps({"user_id": str(user_id)})
bean_helper.put_payload_to_beanstalk(config["consumer"], message)
def copy_data():
base_dir = "/data/rsync/jianli.yang/AutoCoverTool/data/train_users/0414_0514"
dst_dir = "/data/rsync/jianli.yang/AutoCoverTool/data/train_users/0414_0514_finish"
# 只要10首干声以及以上的
dirs = glob.glob(os.path.join(base_dir, "*"))
for cur_dir in dirs:
cur_name = cur_dir.split("/")[-1]
cur_mp4_files = glob.glob(os.path.join(cur_dir, "src/*wav"))
if len(cur_mp4_files) > 10:
print("mv {} {}".format(cur_dir, os.path.join(dst_dir, cur_name)))
+import urllib.request
+
+url = "https://testtest.mp4"
+
+try:
+ status = urllib.request.urlopen(url).code
+ print(status)
+except Exception as err:
+ print(err)
+
+
+def check_exists(url):
+ try:
+ status = urllib.request.urlopen(url).code
+ return status == 200
+ except Exception as err:
+ return False
+
+
+def get_vocal_url():
+ arr = []
+ with open("new.txt") as f:
+ while True:
+ line = f.readline()
+ line = line.strip()
+ if not line:
+ break
+ arr.append(line.split("\t"))
+
+ first_line = arr[0][:3].copy()
+ for i in range(3, len(arr[0])):
+ first_line.append(arr[0][i])
+ first_line.append("vocal_url")
+ first_line.append("delay_time")
+ out_arr = [first_line]
+
+ st = time.time()
+ for i in range(1, 30):
+ cur_line = arr[0][:3].copy()
+ female_cnt = 0
+ male_cnt = 0
+ for j in range(3, len(arr[i])):
+ cur_line.append(arr[i][j])
+ if arr[i][j].isdigit():
+ rid = arr[i][j]
+ sql = f"select id, recording_url, created_on from recording where id={rid}"
+ res = get_shard_data_by_sql(sql, rid)
+ if len(res) > 0:
+ origin_master_url = res[0][1].replace("master.mp4", "origin_master.mp4").replace("recordings", "recordings_origin")
+ sql = f"select recording_id, delay_time from recording_extra_info where recording_id={rid}"
+ res1 = get_shard_data_by_sql(sql, rid)
+ # print(origin_master_url)
+ if check_exists(origin_master_url):
+ cur_line.append(origin_master_url)
+ cur_line.append(str(res1[0][1]))
+ if j < 8:
+ male_cnt += 1
+ else:
+ female_cnt += 1
+ continue
+ cur_line.append("-1")
+ cur_line.append("-1")
+ if male_cnt > 0 and female_cnt > 0:
+ out_arr.append(cur_line)
+ if i % 10 == 0:
+ print(f"percent={i}/{len(arr)} sp = {time.time() - st}, cnt={len(out_arr)}")
+
+ with open("new_vocal.txt", "w") as f:
+ for line in out_arr:
+ f.write(",".join(line) + "\n")
+
+
if __name__ == '__main__':
- process()
+ get_vocal_url()
+ # process()
# put_data("res/0414_0514.csv")
# arr = [
# "5348024335101054",
# "4222124657245641",
# "5629499489117674",
# "12384898975368914",
# "5629499489839033",
# "5348024336648185",
# "5910973794961321",
# "3635518643",
# "844424937670811",
# "4785074600577375",
# "6755399442719465",
# "4785074603156924",
# "11540474053041727",
# "6473924129711210",
# "7036874421386111",
# "7599824376482810",
# "6755399447475416",
# "8444249306118343",
# "3377699721107378",
# "12947848931397021",
# "7599824374449011",
# "3096224748076687",
# "12103424006572822",
# "1125899914308640",
# "12666373952417962",
# "281474982845813",
# "11821949029679778",
# "12947848937379499",
# "12947848936090348",
# "3096224747262571",
# "2814749767432467",
# "5066549357604730",
# "3096224751151928"
# ]
# for uuid in arr:
# get_user_recordings(uuid)
# print("finish =={} ".format(uuid))
# copy_data()

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