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	diff --git a/AutoCoverTool/online/inference_one.py b/AutoCoverTool/online/inference_one.py
	index 8113e10..a73d87d 100644
	--- a/AutoCoverTool/online/inference_one.py
	+++ b/AutoCoverTool/online/inference_one.py
	@@ -1,688 +1,689 @@
	"""
	单个处理的逻辑
	song_id:
	---src.mp3 // 源数据，需要提前放进去
	---cache
	---vocal.wav // 分离之后产生
	---acc.wav // 分离之后产生
	---vocal_32.wav // 分离之后产生
	---song_id_sp1.wav // 合成之后产生
	---song_id_sp2.wav // 合成之后产生
	---song_id_sp2_d.wav // 降噪之后生成
	---song_id_sp2_dv.wav // 降噪+拉伸之后产生 [占比太高的不产生]
	---song_id_sp2_dve442.wav // 手动调整之后产生
	---song_id_sp2_dve442_replace.wav // 替换之后产生
	---song_id_sp2_dve442_replace_mix.wav // 人声+伴奏混合之后产生
	---song_id
	--acc.mp3 // 44k双声道320k
	--vocal.mp3 // 44k双声道320k
	--src.mp3 // 44k双声道320k
	--song_id_sp2_dv.mp3 // 44k单声道320k
	---song_id_out // 对外输出
	--src.mp3 // 原始音频
	--song_id_sp2_dv_replace_mix.mp3 // 制作完成的音频

	环境安装:
	conda create -n auto_song_cover python=3.9
	# 安装demucs环境[进入到ref.music_remover 执行pip install -r requirements.txt]
	# 安装so_vits_svc环境[进入到ref.so_vits_svc 执行pip install -r requirements.txt]
	pip install librosa
	pip install scikit-maad
	pip install praat-parselmouth
	pip install matplotlib
	pip install torchvision
	pip install madmom
	pip install torchstat
	环境设置:
	export PATH=$PATH:/data/gpu_env_common/env/bin/ffmpeg/bin
	export PYTHONPATH=$PWD:$PWD/ref/music_remover/demucs:$PWD/ref/so_vits_svc:$PWD/ref/split_dirty_frame
	"""

	import os
	import time
	import shutil
	import random
	import logging
	import librosa

	logging.basicConfig(filename='/tmp/inference.log', level=logging.INFO)

	gs_err_code_success = 0
	gs_err_code_no_src_mp3 = 1
	gs_err_code_separate = 2
	gs_err_code_trans_32 = 3
	gs_err_code_encode_err = 4
	gs_err_code_replace_err = 5
	gs_err_code_replace_trans_err = 6
	gs_err_code_mix_err = 7
	gs_err_code_mix_transcode_err = 8
	gs_err_code_no_src_dir = 9
	gs_err_code_volume_err = 10
	gs_err_code_trans2_442 = 11
	gs_err_code_reverb = 12
	gs_err_code_no_good_choice = 13
	gs_err_code_preprocess_vocal = 14
	gs_err_code_replace_except_err = 15

	gs_denoise_exe = "/opt/soft/bin/denoise_exe"
	gs_draw_volume_exe = "/opt/soft/bin/draw_volume"
	gs_simple_mixer_path = "/opt/soft/bin/simple_mixer"
	gs_rever_path = "/opt/soft/bin/dereverbrate"

	from ref.music_remover.separate_interface import SeparateInterface
	from ref.so_vits_svc.inference_main import *
	from ref.split_dirty_frame.script.process_one import ReplaceVocalFrame, construct_power_fragment


	class SongCoverInference:
	def __init__(self):
	self.work_dir = None
	self.cache_dir = None
	self.cid = None
	self.src_mp3 = None
	self.vocal_path = None
	self.vocal_32_path = None
	self.acc_path = None
	self.speakers = [
	10414574138721494,
	10414574140317353,
	1688849864840588,
	3634463651,
	5629499489839033,
	5910973794723621,
	6755399374234747,
	8162774327817435,
	8162774329368194,
	1125899914308640, # 以下为男声,包括这个
	12384898975368914,
	12947848931397021,
	3096224748076687,
	3096224751151928,
	5066549357604730,
	5348024335101054,
	6755399442719465,
	7036874421386111
	]

	self.speakers2gender = {
	10414574138721494: 2,
	10414574140317353: 2,
	1688849864840588: 2,
	3634463651: 2,
	5629499489839033: 2,
	5910973794723621: 2,
	6755399374234747: 2,
	8162774327817435: 2,
	8162774329368194: 2,
	1125899914308640: 1, # 1是男
	12384898975368914: 1,
	12947848931397021: 1,
	3096224748076687: 1,
	3096224751151928: 1,
	5066549357604730: 1,
	5348024335101054: 1,
	6755399442719465: 1,
	7036874421386111: 1
	}
	self.speakers_model_path = "data/train_users/{}/logs/32k/G_2000.pth"
	self.speakers_model_config = "data/train_users/{}/config/config.json"

	st = time.time()
	self.separate_inst = None
	logging.info("post process ... ReplaceVocalFrame init sp={}".format(time.time() - st))
	self.replace_vocal_frame_inst = None
	logging.info("SongCoverInference init sp={}".format(time.time() - st))

	def separate(self, cid, src_mp3, vocal_path, acc_path):
	"""
	人声伴奏分离
	:param cid:
	:param src_mp3:
	:param vocal_path:
	:param acc_path:
	:return:
	"""
	st = time.time()
	if self.separate_inst is None:
	self.separate_inst = SeparateInterface()
	if not self.separate_inst.process(cid, src_mp3, vocal_path, acc_path):
	return gs_err_code_separate
	if not os.path.exists(vocal_path) or not os.path.exists(acc_path):
	return gs_err_code_separate

	# 转码出一个32k单声道的数据
	cmd = "ffmpeg -i {} -ar 32000 -ac 1 -y {} -loglevel fatal".format(vocal_path, self.vocal_32_path)
	os.system(cmd)
	if not os.path.exists(self.vocal_32_path):
	return gs_err_code_trans_32
	print("separate:cid={}\|sp={}".format(cid, time.time() - st))
	return gs_err_code_success

	def get_start_ms(self, vocal_path):
	"""
	给定原始音频，找一段连续10s的音频
	:param vocal_path:
	:return:
	"""
	audio, sr = librosa.load(vocal_path, sr=16000)
	audio = librosa.util.normalize(audio)
	# 帧长100ms,帧移10ms,计算能量
	power_arr = []
	for i in range(0, len(audio) - 1600, 160):
	power_arr.append(np.sum(np.abs(audio[i:i + 160])) / 160)
	# 将能量小于等于10的部分做成段
	power_arr = construct_power_fragment(power_arr)
	fragments = []
	last_pos = 0
	for idx, line in enumerate(power_arr):
	start = round(float(line[0]) * 0.01, 3)
	duration = round(float(line[1]) * 0.01, 3)
	fragments.append([last_pos, start - last_pos])
	last_pos = start + duration
	if last_pos < len(audio) / sr:
	fragments.append([last_pos, len(audio) / sr - last_pos])

	# 合并数据，两者间隔在50ms以内的合并起来
	idx = 0
	while idx < len(fragments) - 1:
	if fragments[idx + 1][0] - (fragments[idx][0] + fragments[idx][1]) < 0.05:
	fragments[idx][1] = fragments[idx + 1][0] + fragments[idx + 1][1] - fragments[idx][0]
	del fragments[idx + 1]
	idx -= 1
	idx += 1

	# out_file = vocal_path + "_power.csv"
	# with open(out_file, "w") as f:
	# f.write("Name\tStart\tDuration\tTime Format\tType\n")
	# for fragment in fragments:
	# start = round(float(fragment[0]), 3)
	# duration = round(float(fragment[1]), 3)
	# strr = "{}\t{}\t{}\t{}\n".format("11", start, duration, "decimal\tCue\t")
	# f.write(strr)

	# 筛选出开始的位置
	# 1. 连续时长大于10s，当前段长度大于3s
	# 2. 不可用
	# 从0到fragments[idx], 包含idx其中人声段的总和
	tot_vocal_duration = [fragments[0][1]]
	for i in range(1, len(fragments)):
	tot_vocal_duration.append(tot_vocal_duration[i - 1] + fragments[i][1])

	# 计算出任意两段之间非人声占比
	for i in range(0, len(fragments)):
	if fragments[i][1] >= 3:
	now_tot = 0
	if i > 0:
	now_tot = tot_vocal_duration[i - 1]
	for j in range(i + 1, len(fragments)):
	cur_rate = tot_vocal_duration[j] - now_tot
	cur_rate = cur_rate / (fragments[j][1] + fragments[j][0] - fragments[i][0])
	if cur_rate > 0.1:
	return fragments[i][0]
	return -1

	def inference_speaker(self):
	"""
	推理生成合成后的音频
	随机取5个干声,选择占比最小的，并且要求占比小于0.3
	:return:
	"""
	st = time.time()
	out_speakers = random.sample(self.speakers, 15)
	out_songs_dict = {}
	for speaker in out_speakers:
	model_path = self.speakers_model_path.format(speaker)
	config_path = self.speakers_model_config.format(speaker)
	song_path = os.path.join(self.cache_dir, "{}_{}.wav".format(self.cid, speaker))
	try:
	inf(model_path, config_path, self.vocal_32_path, song_path, "prod")
	except Exception as ex:
	logging.info("cid={}, inference_speaker err={}".format(self.cid, ex))
	continue
	if os.path.exists(song_path):
	if self.replace_vocal_frame_inst is None:
	self.replace_vocal_frame_inst = ReplaceVocalFrame(
	"data/models/split_dirty_frame_v5_3_epoch3_852.pth")
	rate = self.replace_vocal_frame_inst.get_rate(song_path)
	if rate < 0.3:
	out_songs_dict[song_path] = rate

	# 从内部选择占比最低的
	out_songs = []
	if len(out_songs_dict.keys()) > 0:
	st_sec = self.get_start_ms(self.vocal_path)
	song_msg = sorted(out_songs_dict.items(), key=lambda kv: kv[1])[0]
	out_songs = [song_msg[0]]
	logging.info("GetRate:cid={},song={},rate={},st_tm={}".format(self.cid, song_msg[0], round(song_msg[1], 2),
	round(st_sec, 3)))
	print("GetRate:cid={},song={},rate={},st_tm={}".format(self.cid, song_msg[0], round(song_msg[1], 2),
	round(st_sec, 3)))
	# logging.info("inference_speaker len = {} finish sp = {}".format(len(out_songs), time.time() - st))
	print("inference_speaker len = {} finish sp = {}".format(len(out_songs), time.time() - st))
	return out_songs

	def get_new_vocal_rate(self, songs):
	"""
	获取人声的比率
	:param songs:
	:return:
	"""
	st = time.time()
	need_to_process_song = []
	for song in songs:
	if self.replace_vocal_frame_inst is None:
	self.replace_vocal_frame_inst = ReplaceVocalFrame("data/models/split_dirty_frame_v5_3_epoch3_852.pth")
	rate = self.replace_vocal_frame_inst.get_rate(song)
	logging.info("{} {} replace_rate={}".format(self.cid, song, rate))
	if rate < 1.0:
	need_to_process_song.append(song)
	logging.info(
	"get_new_vocal_rate belen = {} len = {} finish sp = {}".format(len(songs), len(need_to_process_song),
	time.time() - st))
	return need_to_process_song

	def preprocess_vocal(self, songs, vocal_path):
	"""
	1. 降噪
	2. 拉伸
	:param songs:
	:param vocal_path: 参考的音频信号
	:return:
	"""
	st = time.time()
	dv_out_list = []
	for song in songs:
	denoise_path = str(song).replace(".wav", "_d.wav")
	cmd = "{} {} {}".format(gs_denoise_exe, song, denoise_path)
	os.system(cmd)
	if not os.path.exists(denoise_path):
	print("{} {} ERROR denoise".format(self.cid, song))
	continue
	# 拉伸
	volume_path = str(song).replace(".wav", "_dv.wav")
	cmd = "{} {} {} {}".format(gs_draw_volume_exe, denoise_path, vocal_path, volume_path)
	os.system(cmd)
	if not os.path.exists(volume_path):
	print("{} {} ERROR denoise".format(self.cid, volume_path))
	continue
	dv_out_list.append(volume_path)
	print(
	"preprocess_vocal belen = {} len = {} finish sp = {}".format(len(songs), len(dv_out_list),
	time.time() - st))
	return dv_out_list

	def output(self, dv_out_list):
	"""
	对外输出数据
	:param dv_out_list:
	:return:
	"""
	st = time.time()
	out_dir = os.path.join(self.work_dir, self.cid)
	if os.path.exists(out_dir):
	shutil.rmtree(out_dir)
	os.makedirs(out_dir)

	# 拷贝数据
	dst_mp3_path = os.path.join(out_dir, "src_mp3")
	dst_acc_path = os.path.join(out_dir, "acc.mp3")
	dst_vocal_path = os.path.join(out_dir, "vocal.mp3")
	shutil.copyfile(self.src_mp3, dst_mp3_path)
	cmd = "ffmpeg -i {} -ab 320k -y {} -loglevel fatal".format(self.acc_path, dst_acc_path)
	os.system(cmd)
	if not os.path.exists(dst_acc_path):
	return gs_err_code_encode_err
	cmd = "ffmpeg -i {} -ab 320k -y {} -loglevel fatal".format(self.vocal_path, dst_vocal_path)
	os.system(cmd)
	if not os.path.exists(dst_vocal_path):
	return gs_err_code_encode_err

	# 将所有数据放到out_dir中，用于给人工标注
	for dv_wav in dv_out_list:
	dv_wav_name = str(dv_wav).split("/")[-1].replace(".wav", "_441.mp3")
	dst_dv_path = os.path.join(out_dir, dv_wav_name)

	cmd = "ffmpeg -i {} -ar 44100 -ac 1 -ab 320k -y {} -loglevel fatal".format(dv_wav, dst_dv_path)
	os.system(cmd)
	if not os.path.exists(dst_dv_path):
	print("{} encode err!".format(cmd))
	continue
	logging.info(
	"preprocess_vocal output sp = {}".format(time.time() - st))

	def process_one(self, cid, work_dir, enable_output=False):
	logging.info("\nstart:cid={},work_dir={}----------------------->>>>>>>>".format(cid, work_dir))
	self.cid = cid
	self.work_dir = work_dir

	# 所有不对外交付的，全部放到这里
	self.cache_dir = os.path.join(work_dir, "cache")
	if os.path.exists(self.cache_dir):
	shutil.rmtree(self.cache_dir)
	os.makedirs(self.cache_dir)

	self.src_mp3 = os.path.join(self.work_dir, "src.mp3")
	if not os.path.exists(self.src_mp3):
	return gs_err_code_no_src_mp3
	self.vocal_path = os.path.join(self.cache_dir, "vocal.wav")
	self.vocal_32_path = os.path.join(self.cache_dir, "vocal_32.wav")
	self.acc_path = os.path.join(self.cache_dir, "acc.wav")

	if not os.path.exists(self.vocal_32_path):
	logging.info("start separate ... {} {} {}".format(self.src_mp3, self.vocal_path, self.acc_path))
	err = self.separate(cid, self.src_mp3, self.vocal_path, self.acc_path)
	if err != gs_err_code_success:
	return err, None, None
	logging.info("start inference_speaker ...")
	out_songs = self.inference_speaker()
	dv_out_list = self.preprocess_vocal(out_songs, self.vocal_path)
	if len(dv_out_list) == 0:
	return gs_err_code_no_good_choice, None, None

	mix_mp3_path = None
	gender = -1
	if enable_output:
	self.output(dv_out_list)
	else:
	# 默认全部处理一遍
	for dv_out_path in dv_out_list:
	src_path = dv_out_path.replace("_dv.wav", ".wav")
	err, mix_mp3_path = self.after_process(self.cid, self.work_dir, src_path, dv_out_path, self.vocal_path,
	self.acc_path,
	True, False)
	if err != gs_err_code_success:
	logging.info("after_process err {}".format(err))

	# 取出性别属性
	if err == gs_err_code_success and mix_mp3_path is not None:
	gender = self.speakers2gender[int(str(os.path.basename(mix_mp3_path)).split("_")[1])]
	logging.info("finish:cid={},work_dir={}----------------------->>>>>>>>".format(cid, work_dir))
	return gs_err_code_success, mix_mp3_path, gender

	def reverb_by_vocal(self, file):
	st = time.time()
	file_442 = file.replace(".wav", "_442.wav")
	if not os.path.exists(file_442):
	cmd = "ffmpeg -i {} -ar 44100 -ac 2 -y {}".format(file, file_442)
	os.system(cmd)
	if not os.path.exists(file_442):
	return gs_err_code_trans2_442, None

	file_dst = file.replace(".wav", "_442_dr.wav")
	cmd = "{} {} {} {}".format(gs_rever_path, self.vocal_path, file_442, file_dst)
	os.system(cmd)
	if not os.path.exists(file_dst):
	return gs_err_code_reverb, None
	print("cid = {}, reverb_by_vocal sp={}".format(self.cid, time.time() - st))
	return gs_err_code_success, file_dst

	def after_process(self, cid, work_dir, in_file, effect_file, vocal_file, acc_file, need_draw=True,
	need_reverb=True):
	"""
	后处理逻辑
	将处理好的音频进行替换，然后和伴奏进行混合，最后进行编码
	:return:
	"""
	if need_reverb:
	# 抓取混响
	err, effect_file = self.reverb_by_vocal(in_file)
	if err != gs_err_code_success:
	return err, None

	if need_draw:
	# 增加一个拉伸的步骤
	volume_path = str(effect_file).replace(".wav", "_dv.wav")
	cmd = "{} {} {} {}".format(gs_draw_volume_exe, effect_file, vocal_file, volume_path)
	print(cmd)
	os.system(cmd)
	if not os.path.exists(volume_path):
	print("{} {} ERROR draw volume".format(self.cid, volume_path))
	return gs_err_code_volume_err, None
	effect_file = volume_path

	st = time.time()
	self.cid = cid
	self.work_dir = work_dir
	self.src_mp3 = os.path.join(self.work_dir, "src.mp3")
	if not os.path.exists(self.work_dir):
	return gs_err_code_no_src_dir
	self.replace_vocal_frame_inst.process(in_file, effect_file, vocal_file)
	dst_path = effect_file + "_replace.wav"
	if not os.path.exists(dst_path):
	return gs_err_code_replace_err, None
	print("replace_vocal_frame_inst sp = {}".format(time.time() - st))

	# 转码
	dst_path_442 = dst_path.replace("_replace.wav", "_replace442.wav")
	cmd = "ffmpeg -i {} -ar 44100 -ac 2 -y {} -loglevel fatal".format(dst_path, dst_path_442)
	os.system(cmd)
	if not os.path.exists(dst_path_442):
	return gs_err_code_replace_trans_err, None

	# 合并转码后再做一次拉伸，保证响度
	volume_path = str(dst_path_442).replace(".wav", "_dv.wav")
	cmd = "{} {} {} {}".format(gs_draw_volume_exe, dst_path_442, vocal_file, volume_path)
	print(cmd)
	os.system(cmd)
	if not os.path.exists(volume_path):
	print("{} {} ERROR draw volume".format(self.cid, volume_path))
	return gs_err_code_volume_err, None
	dst_path_442 = volume_path

	# 混合
	mix_path = dst_path_442.replace("_replace442.wav", "_replace442_mix.wav")
	cmd = "{} {} {} {}".format(gs_simple_mixer_path, dst_path_442, acc_file, mix_path)
	print("{}".format(cmd))
	os.system(cmd)
	if not os.path.exists(mix_path):
	return gs_err_code_mix_err, None

	# 编码为mp3
	output_dir = os.path.join(self.work_dir, self.cid + "_out")
	if not os.path.exists(output_dir):
	os.makedirs(output_dir)
	name = str(mix_path).replace("_replace442_mix.wav", "_replace442_mix.mp3").split("/")[-1]
	mix_path_mp3 = os.path.join(output_dir, name)
	cmd = "ffmpeg -i {} -ab 320k -y {} -loglevel fatal".format(mix_path, mix_path_mp3)
	os.system(cmd)
	if not os.path.exists(mix_path_mp3):
	return gs_err_code_mix_transcode_err, None

	# 拷贝src到output_dir
	# shutil.copyfile(self.src_mp3, os.path.join(output_dir, "src.mp3"))
	# logging.info("after_process sp = {}".format(time.time() - st))
	return gs_err_code_success, mix_path_mp3

	####################################新对外接口############################################################
	def prepare_env(self, cid, work_dir, create_dir=False):
	self.cid = cid
	self.work_dir = work_dir

	# 所有不对外交付的，全部放到这里
	self.cache_dir = os.path.join(work_dir, "cache")
	if create_dir:
	if os.path.exists(self.cache_dir):
	shutil.rmtree(self.cache_dir)
	os.makedirs(self.cache_dir)

	self.src_mp3 = os.path.join(self.work_dir, "src.mp3")
	if not os.path.exists(self.src_mp3):
	return gs_err_code_no_src_mp3
	self.vocal_path = os.path.join(self.cache_dir, "vocal.wav")
	self.vocal_32_path = os.path.join(self.cache_dir, "vocal_32.wav")
	self.acc_path = os.path.join(self.cache_dir, "acc.wav")
	return gs_err_code_success

	def generate_svc_file(self, cid, work_dir):
	"""
	:param cid:
	:param work_dir:
	:return:err_code, 生成出的svc的文件名称
	"""
	err = self.prepare_env(cid, work_dir, create_dir=True)
	if err != gs_err_code_success:
	return err, None

	# 音源分离
	if not os.path.exists(self.vocal_32_path):
	st = time.time()
	err = self.separate(cid, self.src_mp3, self.vocal_path, self.acc_path)
	logging.info("cid={},separate,sp={}".format(self.cid, time.time() - st))
	if err != gs_err_code_success:
	return err, None

	# 生成svc,只保留一个最佳的
	st = time.time()
	out_songs = self.inference_speaker()
	if len(out_songs) == 0:
	return gs_err_code_no_good_choice, None
	logging.info("cid={},inference_speaker,{},sp={}".format(self.cid, out_songs[0], time.time() - st))
	return gs_err_code_success, out_songs[0]

	def effect(self, cid, work_dir, svc_file):
	st = time.time()
	err = self.prepare_env(cid, work_dir)
	if err != gs_err_code_success:
	return err, None
	logging.info("cid={},effect_and_mix,{},sp={}".format(self.cid, svc_file, time.time() - st))

	# 预处理人声
	dv_out_list = self.preprocess_vocal([svc_file], self.vocal_path)
	if len(dv_out_list) == 0:
	return gs_err_code_preprocess_vocal, None
	svc_file = dv_out_list[0]

	# 做音效
	st = time.time()
	err, effect_file = self.reverb_by_vocal(svc_file)
	if err != gs_err_code_success:
	return err, None
	logging.info("cid={},reverb_by_vocal,{},sp={}".format(self.cid, svc_file, time.time() - st))
	return err, effect_file

	def mix(self, cid, work_dir, svc_file, effect_file):
	"""
	做音效以及合并
	:param cid:
	:param work_dir:
	:param svc_file:
	:param effect_file:
	:return: err_code, 完成的mp3文件
	"""
	st = time.time()
	err = self.prepare_env(cid, work_dir)
	if err != gs_err_code_success:
	return err, None
	logging.info("cid={},effect_and_mix,{},sp={}".format(self.cid, svc_file, time.time() - st))

	# 拉伸
	st = time.time()
	volume_path = str(effect_file).replace(".wav", "_dv.wav")
	cmd = "{} {} {} {}".format(gs_draw_volume_exe, effect_file, self.vocal_path, volume_path)
	os.system(cmd)
	if not os.path.exists(volume_path):
	print("{} {} ERROR draw volume".format(self.cid, volume_path))
	return gs_err_code_volume_err, None
	effect_file = volume_path
	logging.info("cid={},draw_volume,{},sp={}".format(self.cid, svc_file, time.time() - st))

	# 替换
	st = time.time()
	try:
	if self.replace_vocal_frame_inst is None:
	self.replace_vocal_frame_inst = ReplaceVocalFrame("data/models/split_dirty_frame_v5_3_epoch3_852.pth")
	self.replace_vocal_frame_inst.process(svc_file, effect_file, self.vocal_path)
	except Exception as ex:
	logging.info("{},replace_vocal_frame_inst, {}", self.cid, ex)
	return gs_err_code_replace_except_err, None
	dst_path = effect_file + "_replace.wav"
	if not os.path.exists(dst_path):
	return gs_err_code_replace_err, None
	logging.info("cid={},replace_vocal_frame_inst,{},sp={}".format(self.cid, svc_file, time.time() - st))

	# 转码
	st = time.time()
	dst_path_442 = dst_path.replace("_replace.wav", "_replace442.wav")
	cmd = "ffmpeg -i {} -ar 44100 -ac 2 -y {} -loglevel fatal".format(dst_path, dst_path_442)
	os.system(cmd)
	if not os.path.exists(dst_path_442):
	return gs_err_code_replace_trans_err, None
	logging.info("cid={},transcode,{},sp={}".format(self.cid, svc_file, time.time() - st))

	# 合并转码后再做一次拉伸，保证响度
	st = time.time()
	volume_path = str(dst_path_442).replace("_replace442.wav", "_replace442_dv.wav")
	cmd = "{} {} {} {}".format(gs_draw_volume_exe, dst_path_442, self.vocal_path, volume_path)
	os.system(cmd)
	if not os.path.exists(volume_path):
	print("{} {} ERROR draw volume".format(self.cid, volume_path))
	return gs_err_code_volume_err, None
	dst_path_442 = volume_path
	logging.info("cid={},draw_volume2,{},sp={}".format(self.cid, svc_file, time.time() - st))

	# 混合
	st = time.time()
	mix_path = dst_path_442.replace("_replace442_dv.wav", "_replace442_dv_mix.wav")
	cmd = "{} {} {} {}".format(gs_simple_mixer_path, dst_path_442, self.acc_path, mix_path)
	os.system(cmd)
	if not os.path.exists(mix_path):
	return gs_err_code_mix_err, None
	logging.info("cid={},mixer,{},sp={}".format(self.cid, svc_file, time.time() - st))

	# 编码为mp3
	st = time.time()
	output_dir = os.path.join(self.work_dir, self.cid + "_out")
	if not os.path.exists(output_dir):
	os.makedirs(output_dir)
	name = str(mix_path).replace("_replace442_dv_mix.wav", "_replace442_dv_mix.mp3").split("/")[-1]
	mix_path_mp3 = os.path.join(output_dir, name)
	cmd = "ffmpeg -i {} -ab 320k -y {} -loglevel fatal".format(mix_path, mix_path_mp3)
	print(cmd)
	os.system(cmd)
	if not os.path.exists(mix_path_mp3):
	return gs_err_code_mix_transcode_err, None
	logging.info("cid={},encode,{},sp={}".format(self.cid, svc_file, time.time() - st))
	return gs_err_code_success, mix_path_mp3

	def get_gender(self, svc_file):
	return self.speakers2gender[int(os.path.basename(svc_file.replace(".wav", "")).split("_")[1])]

	def process_one_logic(self, cid, work_dir):
	"""
	搞成两部分:
	1. 分离数据+5次推理，获取最佳结果,并保存
	2. 利用最佳结果做音效以及合并
	:return:
	"""
	err, svc_file = self.generate_svc_file(cid, work_dir)
	gender = -1
	if err != gs_err_code_success:
	return err, svc_file, gender,
	gender = self.get_gender(svc_file)
	err, effect_file = self.effect(cid, work_dir, svc_file)
	if err != gs_err_code_success:
	return err, svc_file, gender
	err, mix_mp3_path = self.mix(cid, work_dir, svc_file, effect_file)
	return err, mix_mp3_path, gender


	def test():
	arr = [
	# "611752105020343687",
	# "611752105023532439",
	# "611752105030419688",
	# "611752105030485748",
	- "611752105030485685"
	+ # "611752105030485685",
	+ "dzq",
	]
	base_dir = "/data/rsync/jianli.yang/AutoCoverTool/data/test"
	s_inst = SongCoverInference()
	for cid in arr:
	st = time.time()
	# err, mix_mp3, gender = s_inst.process_one(cid, os.path.join(base_dir, cid), False)
	err, mix_mp3, gender = s_inst.process_one_logic(cid, os.path.join(base_dir, cid))
	print(mix_mp3, gender)
	print("cid={} RealFinish err={} sp={}".format(cid, err, time.time() - st))


	if __name__ == '__main__':
	test()
	diff --git a/AutoCoverTool/script/shuffle_music.py b/AutoCoverTool/script/shuffle_music.py
	index 81b3e60..b3496d2 100644
	--- a/AutoCoverTool/script/shuffle_music.py
	+++ b/AutoCoverTool/script/shuffle_music.py
	@@ -1,263 +1,429 @@
	"""
	载入人声，将人声的频谱进行向上平移
	"""
	import librosa
	import soundfile
	import numpy as np
	from copy import deepcopy


	def local_maxium(x):
	"""
	求序列的极大值
	:param x:
	:return:
	"""
	d = np.diff(x)
	l_d = len(d)
	maxium = []
	loc = []
	for i in range(l_d - 1):
	if d[i] > 0 and d[i + 1] <= 0:
	maxium.append(x[i + 1])
	loc.append(i + 1)
	return maxium, loc


	def Formant_Cepst(u, cepstL):
	"""
	来源: https://github.com/taw19960426/-Speech-signal-processing-experiment-tutorial-_python/blob/master/%E5%85%B1%E6%8C%AF%E5%B3%B0%E4%BC%B0%E8%AE%A1%E5%87%BD%E6%95%B0.py
	倒谱法共振峰估计函数
	:param u:输入信号
	:param cepstL:🔪频率上窗函数的宽度
	:return: val共振峰幅值
	:return: loc共振峰位置
	:return: spec包络线
	"""
	wlen2 = len(u) // 2
	u_fft = np.fft.fft(u) # 按式（2-1）计算
	U = np.log(np.abs(u_fft[:wlen2]))
	Cepst = np.fft.ifft(U) # 按式（2-2）计算
	cepst = np.zeros(wlen2, dtype=np.complex)
	cepst[:cepstL] = Cepst[:cepstL] # 按式（2-3）计算
	cepst[-cepstL + 1:] = Cepst[-cepstL + 1:] # 取第二个式子的相反
	spec = np.real(np.fft.fft(cepst))
	val, loc = local_maxium(spec) # 在包络线上寻找极大值
	return val, loc, spec


	def get_ref_stft():
	sr = 44100
	audio, sr = librosa.load(
	"/Users/yangjianli/starmaker-work/research/tmp_code/消音相关/test_out/ins_main_out/test2/tot/3/vocal_ref.wav", \
	sr=sr, mono=True)
	stft = librosa.stft(audio, n_fft=2048)
	stft = stft.transpose()
	print(stft.shape)
	data = np.mean(np.abs(stft), axis=0)
	data = data / np.max(data)
	return data


	def test(in_vocal):
	import matplotlib.pyplot as plt

	sr = 44100
	audio, sr = librosa.load(in_vocal, sr=sr, mono=True)

	stft = librosa.stft(audio, n_fft=2048)
	stft = stft.transpose()
	new_stft = np.zeros_like(stft)
	w1 = get_ref_stft()
	data = np.mean(np.abs(stft), axis=0)
	data = data / np.max(data)
	w = w1 / data

	for ii in range(0, len(stft)):
	# 第一种，整体向上+3
	# for i in range(0, 3):
	# new_stft[ii][i] = stft[ii][i]
	# for i in range(0, len(stft[ii]) - 3):
	# dst_i = i + 3
	# new_stft[ii][dst_i] = stft[ii][i]

	# 第二种,整体向上拉伸1.12倍[2个音高]
	# for i in range(0, 1):
	# new_stft[ii][i] = stft[ii][i]
	# for i in range(1, len(stft[ii])):
	# dst_i = int(i * 1.12 + 0.5)
	# if dst_i >= len(stft[ii]):
	# break
	# new_stft[ii][dst_i] += stft[ii][i]

	# 第三种，第一共振峰部分不移动,其他部分移动
	# power = np.abs(stft[ii])
	# power = power / (np.max(power))
	#
	# x = np.array(list(range(0, len(stft[ii]))))
	# y = power
	#
	# new_x = []
	# new_y = []
	# for i in range(1, len(x) - 1, 1):
	# if y[i - 1] < y[i] > y[i + 1] and y[i] > 0.01:
	# new_x.append(x[i])
	# new_y.append(y[i])
	#
	# # 前后100hz的合并
	# x = new_x
	# y = new_y
	# new_x = []
	# new_y = []
	# for i in range(1, len(x) - 1, 1):
	# if y[i - 1] < y[i] > y[i + 1]:
	# if x[i] - x[i - 1] > 5:
	# new_x.append(x[i - 1])
	# new_y.append(y[i - 1])
	# new_x.append(x[i])
	# new_y.append(y[i])
	# if x[i + 1] - x[i] > 5:
	# new_x.append(x[i + 1])
	# new_y.append(y[i + 1])
	#
	# if len(new_x) <= 1:
	# new_stft[ii] = deepcopy(stft[ii])
	# continue
	#
	# # 从第一共振峰开始向上加
	# st_freq_idx = new_x[1]
	# for i in range(st_freq_idx, len(stft[ii])):
	# dst_i = int(i * 1.12 + 0.5)
	# if dst_i >= len(stft[ii]):
	# continue
	# new_stft[ii][dst_i] = stft[ii][i]
	# new_stft[ii][0] = stft[ii][0]
	# for i in range(0, st_freq_idx):
	# new_stft[ii][i] = stft[ii][i]
	- new_stft[ii] = stft[ii] * w
	+ # new_stft[ii] = stft[ii] * w
	+ pass

	new_stft = new_stft.transpose()
	istft = librosa.istft(new_stft)
	soundfile.write(str(in_vocal).replace(".wav", "_out.wav"), istft, 44100, format="wav")


	def test_v5(vocal, vocal_ref, vocal_ref2):
	sr = 44100
	audio, sr = librosa.load(vocal, sr=sr, mono=True)
	stft = librosa.stft(audio, n_fft=2048)
	stft = stft.transpose()
	new_stft = np.zeros_like(stft)

	audio_ref, sr = librosa.load(vocal_ref, sr=sr, mono=True)
	stft_ref = librosa.stft(audio_ref, n_fft=2048)
	stft_ref = stft_ref.transpose()

	audio_ref2, sr = librosa.load(vocal_ref2, sr=sr, mono=True)
	stft_ref2 = librosa.stft(audio_ref2, n_fft=2048)
	stft_ref2 = stft_ref2.transpose()

	w1 = np.ones(len(stft[0]))
	for i in range(0, 800):
	w1[i] = i / 800
	w2 = 1.0 - w1
	for i in range(0, min(len(stft), len(stft_ref2), len(stft_ref))):
	# new_stft[i] = stft_ref2[i] * w2 + stft[i] * w1
	w = np.abs(stft_ref2[i]) / np.abs(stft[i])
	new_stft[i] = w * stft[i]

	new_stft = new_stft.transpose()
	istft = librosa.istft(new_stft)
	soundfile.write(str(vocal).replace(".wav", "_out5.wav"), istft, 44100, format="wav")


	def ttt(path):
	from scipy.signal import lfilter
	import matplotlib.pyplot as plt
	# path="C4_3_y.wav"
	# data, fs = soundBase('C4_3_y.wav').audioread()
	data, fs = librosa.load(path, sr=44100, mono=True) # sr=None声音保持原采样频率， mono=False声音保持原通道数
	# 预处理-预加重
	u = lfilter([1, -0.99], [1], data)

	cepstL = 7
	wlen = len(u)
	wlen2 = wlen // 2
	print("帧长={}".format(wlen))
	print("帧移={}".format(wlen2))
	# wlen = 256
	# wlen2 = 256//2
	# 预处理-加窗
	u2 = np.multiply(u, np.hamming(wlen))
	# 预处理-FFT,取对数获得频域图像取一半
	U_abs = np.log(np.abs(np.fft.fft(u2))[:wlen2])
	# 4.3.1
	freq = [i * fs / wlen for i in range(wlen2)]
	# print(freq)
	# val共振峰幅值 loc共振峰位置 spec包络线
	val, loc, spec = Formant_Cepst(u, cepstL)
	plt.subplot(2, 1, 1)
	plt.plot(freq, U_abs, 'k')
	plt.xlabel('频率/Hz') # 设置x，y轴的标签
	plt.ylabel('幅值')
	plt.title('男性a的发音频谱')
	plt.subplot(2, 1, 2)
	plt.plot(freq, spec, 'k')
	plt.xlabel('频率/Hz') # 设置x，y轴的标签
	plt.ylabel('幅值')
	plt.title('倒谱法共振峰估计')
	for i in range(len(loc)):
	plt.subplot(2, 1, 2)
	plt.plot([freq[loc[i]], freq[loc[i]]], [np.min(spec), spec[loc[i]]], '-.k')
	plt.text(freq[loc[i]], spec[loc[i]], 'Freq={}'.format(int(freq[loc[i]])))

	# plt.savefig('images/共振峰估计.png')
	plt.show()
	plt.close()


	def main(path):
	import numpy as np
	import pyworld as pw
	from scipy.signal import freqz
	import librosa
	import math

	"""
	思路:
	先变调，再轻微调整共振峰进行合成
	"""

	base_rate = 1.05946
	pitch = 0

	fs = 44100
	x, sr = librosa.load(path, sr=fs, mono=True)
	x = x.reshape(-1).astype(np.float)
	f0, t = pw.dio(x, fs)
	f0 = pw.stonemask(x, f0, t, fs)
	sp = pw.cheaptrick(x, f0, t, fs)
	sp2 = np.zeros_like(sp)
	cur_rate = 1
	for i in range(sp.shape[1]):
	sp2[:, i] = sp[:, min(int(i * 1 / cur_rate), sp.shape[1] - 1)]
	ap = pw.d4c(x, f0, t, fs)
	rate = math.pow(base_rate, pitch)
	out = pw.synthesize(f0 * rate, sp2, ap, fs).reshape(-1, 1)
	soundfile.write(path.replace(".wav", "_out2.wav"), out, fs)


	+def test_v4(in_file, ref_file):
	+ freqs = get_pitch_freq(in_file)
	+ # 将其转变为每毫秒一帧
	+ new_freqs = np.zeros(len(freqs) * 5)
	+ for i in range(0, len(new_freqs)):
	+ new_freqs[i] = freqs[int(i / 5)]
	+
	+ freqs_ref = get_pitch_freq(ref_file)
	+ # 将其转变为每毫秒一帧
	+ new_freqs_ref = np.zeros(len(freqs_ref) * 5)
	+ for i in range(0, len(new_freqs_ref)):
	+ new_freqs_ref[i] = freqs_ref[int(i / 5)]
	+
	+ sr = 44100
	+ audio, sr = librosa.load(in_file, sr=sr, mono=True)
	+ stft = librosa.stft(audio, n_fft=2048)
	+ stft = stft.transpose()
	+ new_stft = np.zeros_like(stft)
	+
	+ sr = 44100
	+ audio_ref, sr = librosa.load(ref_file, sr=sr, mono=True)
	+ stft_ref = librosa.stft(audio_ref, n_fft=2048)
	+ stft_ref = stft_ref.transpose()
	+ # 按照上面的频率段找到基频，然后先打印出来看看
	+ for i in range(min(len(stft), len(stft_ref))):
	+ cur_tm = int(i * 11.61)
	+ cur_pitch = max(new_freqs[cur_tm], new_freqs_ref[cur_tm])
	+
	+ # # 方案1
	+ new_stft[i] = stft_ref[i]
	+ if cur_pitch < 10:
	+ continue
	+ # cur_pitch = int(cur_pitch / (44100 / 2048) * 1.5 + 0.5)
	+ # # 保证前后总能量基本一致
	+ # rate = np.sum(np.abs(new_stft[i][:cur_pitch])) / np.sum(np.abs(stft[i][:cur_pitch]))
	+ # new_stft[i][:cur_pitch] = stft[i][:cur_pitch] * rate
	+
	+ # 方案2
	+ vocal_pitch = int(new_freqs[cur_tm] / (44100 / 2048) * 1.5 + 0.5)
	+ ref_vocal_pitch = int(new_freqs_ref[cur_tm] / (44100 / 2048) * 1.5 + 0.5)
	+ base_vocal_pitch = vocal_pitch
	+ base_ref_vocal_pitch = ref_vocal_pitch
	+ # 穿插，第一共振峰用vocal,第二用ref,第三用vocal,第四用ref,第五用vocal,之后全用ref
	+ # 第一共振峰
	+ new_stft[i][vocal_pitch - 5:vocal_pitch + 5] = stft[i][vocal_pitch - 5:vocal_pitch + 5]
	+ new_stft[i][ref_vocal_pitch - 5:ref_vocal_pitch + 5] = stft[i][ref_vocal_pitch - 5:ref_vocal_pitch + 5]
	+
	+ # 第二共振峰
	+ vocal_pitch = base_vocal_pitch * 2
	+ ref_vocal_pitch = base_ref_vocal_pitch * 2
	+ new_stft[i][vocal_pitch - 5:vocal_pitch + 5] = stft[i][vocal_pitch - 5:vocal_pitch + 5]
	+ new_stft[i][ref_vocal_pitch - 5:ref_vocal_pitch + 5] = stft[i][ref_vocal_pitch - 5:ref_vocal_pitch + 5]
	+
	+ # 第三共振峰
	+ vocal_pitch = base_vocal_pitch * 3
	+ ref_vocal_pitch = base_ref_vocal_pitch * 3
	+ new_stft[i][vocal_pitch - 5:vocal_pitch + 5] = stft[i][vocal_pitch - 5:vocal_pitch + 5]
	+ new_stft[i][ref_vocal_pitch - 5:ref_vocal_pitch + 5] = stft[i][ref_vocal_pitch - 5:ref_vocal_pitch + 5]
	+
	+ # 方案3
	+
	+ # 第五共振峰
	+ # vocal_pitch = int(vocal_pitch / 3 * 5)
	+ # ref_vocal_pitch = int(vocal_pitch / 3 * 5)
	+ # new_stft[i][vocal_pitch - 5:] = stft[i][vocal_pitch - 5:]
	+ # new_stft[i][ref_vocal_pitch - 5:] = stft[i][ref_vocal_pitch - 5:]
	+
	+ new_stft = new_stft.transpose()
	+ istft = librosa.istft(new_stft)
	+ soundfile.write(str(in_file).replace(".wav", "_out7.wav"), istft, 44100, format="wav")
	+
	+
	+def get_pitch_freq(in_file):
	+ # 1 转码到16k单声道
	+ # 2 提取音高
	+ import os
	+
	+ gs_ffmpeg = "/usr/local/bin/ffmpeg"
	+ in_16k_wav = in_file + "_16k.wav"
	+ cmd = "{} -i {} -ar 16000 -ac 1 -y {}".format(gs_ffmpeg, in_file, in_16k_wav)
	+ os.system(cmd)
	+ if not os.path.exists(in_16k_wav):
	+ return np.array([])
	+
	+ gs_pitch_exe = "/Users/yangjianli/linux/opt/soft/bin/dpitch"
	+ out_path = in_file + ".cache"
	+ cmd = "{} {} {}".format(gs_pitch_exe, in_16k_wav, out_path)
	+ print("exec:{}\n".format(cmd))
	+ os.system(cmd)
	+ if not os.path.exists(out_path):
	+ return np.array([])
	+
	+ midi_arr = []
	+ with open(out_path, "r") as f:
	+ while True:
	+ line = f.readline()
	+ line = line.strip()
	+ if not line:
	+ break
	+ freq = float(line)
	+ midi_arr.append(freq) # 5ms一帧
	+ return np.array(midi_arr)
	+
	+
	+def test_v5(in_file):
	+ sr = 44100
	+ audio, sr = librosa.load(in_file, sr=sr, mono=True)
	+ stft = librosa.stft(audio, n_fft=2048)
	+ stft = stft.transpose()
	+
	+ w = np.ones(1025) * 0.1
	+ for mid in [23, 23 * 2, 23 * 3, 23 * 4]:
	+ for i in range(0, 5):
	+ rate = i / 5
	+ w[mid + 5 - i] = rate * (10 - 0.1) + 0.1
	+ w[mid - 5 + i] = rate * (10 - 0.1) + 0.1
	+ stft = stft * w
	+ stft = stft.transpose()
	+ istft = librosa.istft(stft)
	+ soundfile.write(str(in_file).replace(".wav", "_out8.wav"), istft, 44100, format="wav")
	+
	+
	+def test_v6(in_file, ref_file):
	+ sr = 44100
	+ audio, sr = librosa.load(in_file, sr=sr, mono=True)
	+ stft = librosa.stft(audio, n_fft=2048)
	+ stft = stft.transpose()
	+
	+ sr = 44100
	+ audio_ref, sr = librosa.load(ref_file, sr=sr, mono=True)
	+ stft_ref = librosa.stft(audio_ref, n_fft=2048)
	+ stft_ref = stft_ref.transpose()
	+
	+ # 由A映射到B的情况
	+ new_stft = np.zeros_like(stft)
	+ step = 85
	+ new_stft[:step] = stft_ref[:step]
	+ a1 = np.mean(np.abs(stft), axis=0)
	+ a2 = np.mean(np.abs(stft_ref), axis=0)
	+ w = a2 / a1
	+ print(np.max(w), np.min(w), np.mean(w))
	+ for i in range(step, min(len(stft_ref), len(stft))):
	+ a1 = np.mean(np.abs(stft[i - step:i]), axis=0) # 原声
	+ a2 = np.mean(np.abs(stft_ref[i - step:i]), axis=0) # 变调之后
	+ # w = a2 - a1
	+ w = a2 / a1
	+ # w[w > 2] = 2
	+ # w[w < 0.5] = 0.5
	+ # 微调，乘以幅度谱
	+ new_stft[i] = (np.abs(stft[i]) * w) * np.exp(1j * np.angle(stft[i]))
	+
	+ new_stft = new_stft.transpose()
	+ istft = librosa.istft(new_stft)
	+ soundfile.write(str(in_file).replace(".wav", "_out8.wav"), istft, 44100, format="wav")
	+
	+
	if __name__ == '__main__':
	# vc = VoiceChanger()
	# vc.process("/Users/yangjianli/starmaker-work/research/tmp_code/消音相关/test_out/ins_main_out/test2/tot/3/vocal.wav",
	# "/Users/yangjianli/starmaker-work/research/tmp_code/消音相关/test_out/ins_main_out/test2/tot/3/vocal_out1.wav")
	-
	+ #
	# test(
	- # "/Users/yangjianli/starmaker-work/research/tmp_code/消音相关/test_out/ins_main_out/test2/tot/3/vocal.wav")
	+ # "/Users/yangjianli/starmaker-work/research/tmp_code/消音相关/test_out/ins_main_out/test2/tot/3/vocal.wav")

	- vocal_pp = "/Users/yangjianli/starmaker-work/research/tmp_code/消音相关/test_out/ins_main_out/test2/tot/3/vocal_p1.wav"
	- vocal_p2 = "/Users/yangjianli/starmaker-work/research/tmp_code/消音相关/test_out/ins_main_out/test2/tot/3/vocal.wav"
	- vocal_p3 = "/Users/yangjianli/starmaker-work/research/tmp_code/消音相关/test_out/ins_main_out/test2/tot/3/vocal_p2.wav"
	- test_v5(vocal_pp, vocal_p2, vocal_p3)
	+ # vocal_pp = "/Users/yangjianli/starmaker-work/research/tmp_code/消音相关/test_out/ins_main_out/test2/tot/3/vocal_p1.wav"
	+ # vocal_p2 = "/Users/yangjianli/starmaker-work/research/tmp_code/消音相关/test_out/ins_main_out/test2/tot/3/vocal.wav"
	+ # vocal_p3 = "/Users/yangjianli/starmaker-work/research/tmp_code/消音相关/test_out/ins_main_out/test2/tot/3/vocal_p2.wav"
	+ # test_v5(vocal_pp, vocal_p2, vocal_p3)

	# main("/Users/yangjianli/starmaker-work/research/tmp_code/消音相关/test_out/ins_main_out/test2/tot/3/vocal_p2.wav")
	# ttt("/Users/yangjianli/starmaker-work/research/tmp_code/消音相关/test_out/ins_main_out/test2/tot/3/vocal_02_01.wav")
	+
	+ # vv = "/Users/yangjianli/starmaker-work/research/tmp_code/消音相关/test_out/ins_main_out/test2/tot/3/vocal.wav"
	+ # vref = "/Users/yangjianli/starmaker-work/research/tmp_code/消音相关/test_out/ins_main_out/test2/tot/3/vocal_p2.wav"
	+ # # test_v4(vv, vref)
	+ # test_v5(vv)
	+
	+ vv = "/Users/yangjianli/starmaker-work/research/tmp_code/消音相关/test_out/ins_main_out/test2/tot/3/out1/vocal.wav"
	+ vref = "/Users/yangjianli/starmaker-work/research/tmp_code/消音相关/test_out/ins_main_out/test2/tot/3/out1/vocal_p2.wav"
	+ test_v6(vv, vref)

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