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diff --git a/AutoCoverTool/online/inference_one.py b/AutoCoverTool/online/inference_one.py
index f60be54..d49e42e 100644
--- a/AutoCoverTool/online/inference_one.py
+++ b/AutoCoverTool/online/inference_one.py
@@ -1,711 +1,711 @@
"""
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",
"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))
def test_gene_svc():
base_dir = "/data/rsync/jianli.yang/AutoCoverTool/data/test"
- cid = "clean"
+ cid = "dzq"
work_dir = os.path.join(base_dir, cid)
st = time.time()
speaker = "1125899914308640"
speakers_model_path = "data/train_users/{}/logs/32k/G_2000.pth"
speakers_model_config = "data/train_users/{}/config/config.json"
model_path = speakers_model_path.format(speaker)
config_path = speakers_model_config.format(speaker)
# 缓存目录:
cache_dir = os.path.join(work_dir, "cache")
if os.path.exists(cache_dir):
shutil.rmtree(cache_dir)
os.makedirs(cache_dir)
song_path = os.path.join(cache_dir, "{}_{}.wav".format(cid, speaker))
vocal_path = os.path.join(work_dir, "vocal_32.wav")
inf(model_path, config_path, vocal_path, song_path, "prod")
print("finish....")
if __name__ == '__main__':
- test()
+ test_gene_svc()
diff --git a/AutoCoverTool/online/tone_shift_one.py b/AutoCoverTool/online/tone_shift_one.py
index 64f6236..cd9e556 100644
--- a/AutoCoverTool/online/tone_shift_one.py
+++ b/AutoCoverTool/online/tone_shift_one.py
@@ -1,216 +1,223 @@
"""
变调的方式做处理
1. 下载
2. 分离
3. 针对于人声变调+2，伴奏+1
4. 合成
"""
import os
import json
import shutil
import librosa
import logging
import numpy as np
from ref.music_remover.separate_interface import SeparateInterface
from online.inference_worker import upload_file2cos, gs_state_use, gs_state_finish, gs_state_default
from online.common import *
logging.basicConfig(filename='/tmp/tone_shift_one.log', level=logging.INFO)
gs_tone_shift_exe = "/opt/soft/bin/tone_shift_exe"
gs_simple_mixer_path = "/opt/soft/bin/simple_mixer"
gs_err_code_success = 0
gs_err_code_tone_shift = 1
gs_err_code_mix = 2
gs_err_code_transcode = 3
gs_err_code_upload = 4
gs_err_code_download = 5
gs_err_code_trans_to_mp3 = 6
gs_err_code_separate = 7
gs_err_code_duration_too_long = 8
gs_err_code_duration_no_vocal = 9
+gs_err_code_duration_err = 10
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)
# 返回秒
- return float(data["format"]["duration"])
+ if 'format' in data.keys() and 'duration' in data['format']:
+ return float(data["format"]["duration"])
+ return -1
def get_mean_power(audio_path):
sr = 44100
audio, sr = librosa.load(audio_path, sr=sr, mono=True)
mm = np.mean(np.abs(audio))
return mm
class ToneShift:
def __init__(self):
self.separate_inst = SeparateInterface()
def update_state(self, song_id, state):
sql = "update svc_queue_table set state={},update_time={} where song_id = {}". \
format(state, int(time.time()), song_id)
banned_user_map['db'] = "av_db"
update_db(sql, banned_user_map)
def get_one_data(self):
sql = "select song_id, url from svc_queue_table where state = 0 and song_src=3 order by create_time desc limit 1"
banned_user_map["db"] = "av_db"
data = get_data_by_mysql(sql, banned_user_map)
if len(data) == 0:
return None, None
song_id, song_url = data[0]
if song_id != "":
self.update_state(song_id, gs_state_use)
return str(song_id), song_url
def pre_process(self, work_dir, song_url):
"""
创建文件夹，下载数据
:return:
"""
+ if "?sign=" in song_url:
+ return gs_err_code_download
ext = str(song_url).split(".")[-1]
dst_file = "{}/src_origin.{}".format(work_dir, ext)
cmd = "wget {} -O {}".format(song_url, dst_file)
- print(cmd)
os.system(cmd)
if not os.path.exists(dst_file):
return gs_err_code_download
duration = get_d(dst_file)
+ if duration < 0:
+ return gs_err_code_duration_err
print("Duration:", dst_file, duration)
if duration > 20 * 60:
return gs_err_code_duration_too_long
dst_mp3_file = "{}/src.mp3".format(work_dir)
cmd = "ffmpeg -i {} -ar 44100 -ac 2 -y {} ".format(dst_file, dst_mp3_file)
os.system(cmd)
if not os.path.exists(dst_mp3_file):
return gs_err_code_trans_to_mp3
return gs_err_code_success
def tone_shift_one(self, in_file, dst_file, pitch):
cmd = "{} {} {} {}".format(gs_tone_shift_exe, in_file, dst_file, pitch)
os.system(cmd)
return os.path.exists(dst_file)
def mix(self, cid, vocal_path, acc_path, tp):
if tp == 1:
vocal_pitch = 2
acc_pitch = 0
else:
vocal_pitch = -2
acc_pitch = 0
vocal_path_2 = vocal_path.replace(".wav", "_{}.wav".format(vocal_pitch))
acc_path_2 = acc_path.replace(".wav", "_{}.wav".format(acc_pitch))
err = self.tone_shift_one(vocal_path, vocal_path_2, vocal_pitch)
if not err:
return gs_err_code_tone_shift, None
err = self.tone_shift_one(acc_path, acc_path_2, acc_pitch)
if not err:
return gs_err_code_tone_shift, None
base_dir = os.path.dirname(vocal_path)
mix_path = "{}/mix_{}_{}.wav".format(base_dir, vocal_pitch, acc_pitch)
cmd = "{} {} {} {}".format(gs_simple_mixer_path, vocal_path_2, acc_path_2, mix_path)
print("exec_cmd={}".format(cmd))
os.system(cmd)
if not os.path.exists(mix_path):
return gs_err_code_mix, None
# 转码
mix_path_mp3 = mix_path.replace(".wav", ".mp3")
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_transcode, None
# 上传到cos
mix_name = os.path.basename(mix_path_mp3)
key = "av_res/svc_res_tone_shift/{}/{}".format(str(cid), mix_name)
if not upload_file2cos(key, mix_path_mp3):
return gs_err_code_upload, None
return gs_err_code_success, key
def process_one(self, cid, work_dir):
"""
:param cid:
:param work_dir:
:return:
"""
src_mp3 = os.path.join(work_dir, "src.mp3")
vocal_path = os.path.join(work_dir, "vocal.wav")
acc_path = os.path.join(work_dir, "acc.wav")
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, []
# 当人声的平均能量小于一定值时，则认为无人声(0.01是经验值判定，样本分析来看)
# 无人声的样本[0.0056, 0.0003], 有人声的样本(目前最小)[0.046, 0.049]
print("power:{},{}".format(cid, get_mean_power(vocal_path)))
if get_mean_power(vocal_path) < 0.02:
return gs_err_code_duration_no_vocal, []
err, type1_mix_mp3 = self.mix(cid, vocal_path, acc_path, 1)
if err != gs_err_code_success:
return err, []
err, type2_mix_mp3 = self.mix(cid, vocal_path, acc_path, 2)
if err != gs_err_code_success:
return err, []
return gs_err_code_success, [type1_mix_mp3, type2_mix_mp3]
def process_worker(self):
logging.info("start process_worker .....")
base_dir = "/tmp/tone_shift_one"
if not os.path.exists(base_dir):
os.makedirs(base_dir)
while True:
worker_st = time.time()
cid, song_url = self.get_one_data()
if cid is None:
time.sleep(5)
logging.info("get one data is None ...")
continue
work_dir = os.path.join(base_dir, str(cid))
if os.path.exists(work_dir):
shutil.rmtree(work_dir)
os.makedirs(work_dir)
err = self.pre_process(work_dir, song_url)
if err != gs_err_code_success:
- return err
+ self.update_state(str(cid), -err)
+ continue
st = time.time()
err, data = self.process_one(str(cid), work_dir)
logging.info("process_finish,{},{}".format(cid, time.time() - st))
if err == gs_err_code_success and len(data) == 2:
sql = "update svc_queue_table set state={},update_time={},svc_url=\"{}\" where song_id = {}". \
format(gs_state_finish, int(time.time()), ",".join(data), str(cid))
banned_user_map['db'] = "av_db"
update_db(sql, banned_user_map)
else:
self.update_state(str(cid), -err)
shutil.rmtree(work_dir)
logging.info("process_finish,{},{}".format(cid, time.time() - worker_st))
if __name__ == '__main__':
ts = ToneShift()
ts.process_worker()
diff --git a/AutoCoverTool/script/get_song_url.py b/AutoCoverTool/script/get_song_url.py
index b3b5541..0c0cc8a 100644
--- a/AutoCoverTool/script/get_song_url.py
+++ b/AutoCoverTool/script/get_song_url.py
@@ -1,761 +1,143 @@
"""
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 limit 1".format(
+ 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 = [
- "611752105016642206",
- "611752105016665828",
- "611752105020332340",
- "611752105020332347",
- "611752105020390931",
- "611752105020417679",
- "611752105021442334",
- "611752105021459528",
- "611752105021810110",
- "611752105021916843",
- "611752105022312180",
- "611752105022614734",
- "611752105022615541",
- "611752105022615665",
- "611752105022616931",
+ "611752105020282582",
+ "611752105020293285",
+ "611752105020293290",
+ "611752105020317636",
+ "611752105020323419",
+ "611752105020323585",
+ "611752105020325121",
+ "611752105020325263",
+ "611752105020325611",
+ "611752105020334448",
+ "611752105020336884",
+ "611752105020336885",
+ "611752105020336886",
+ "611752105020336888",
+ "611752105020336902",
+ "611752105020336906",
+ "611752105020336920",
+ "611752105020336923",
+ "611752105020336927",
+ "611752105020336928",
+ "611752105020336936",
+ "611752105020336939",
+ "611752105020336941",
+ "611752105020336948",
+ "611752105020336952",
+ "611752105020336964",
+ "611752105020336970",
+ "611752105020336976",
+ "611752105020351094",
+ "611752105020351132",
+ "611752105020351146",
+ "611752105020351149",
+ "611752105020356228",
+ "611752105020394299",
+ "611752105020414473",
+ "611752105020414519",
+ "611752105020417490",
+ "611752105022614029",
+ "611752105022614689",
+ "611752105022647049",
+ "611752105022647057",
+ "611752105022647058",
"611752105022647066",
- "611752105022647087",
- "611752105022652047",
- "611752105022700847",
- "611752105022728649",
- "611752105022728653",
- "611752105022729255",
- "611752105022733605",
- "611752105022736029",
- "611752105022740011",
- "611752105022742166",
- "611752105022743986",
- "611752105022746848",
- "611752105022748944",
- "611752105022749272",
- "611752105022749768",
- "611752105022751902",
- "611752105022752248",
- "611752105022754267",
- "611752105022754702",
- "611752105022755405",
- "611752105022757577",
- "611752105022758309",
- "611752105022758395",
- "611752105022761145",
- "611752105022761151",
- "611752105022764435",
- "611752105022764965",
- "611752105022766106",
- "611752105022766675",
- "611752105022774258",
- "611752105022776257",
- "611752105022776285",
- "611752105022776577",
- "611752105022776846",
- "611752105022777151",
- "611752105022777306",
- "611752105022778977",
- "611752105022779055",
- "611752105022779169",
- "611752105022779962",
- "611752105022780648",
- "611752105022784727",
- "611752105022785048",
- "611752105022785179",
- "611752105022785551",
- "611752105022811718",
- "611752105022814368",
- "611752105022815931",
- "611752105022819519",
- "611752105022824948",
- "611752105022828998",
- "611752105022833822",
- "611752105022835250",
- "611752105022838357",
- "611752105022838544",
- "611752105022838589",
- "611752105022838666",
- "611752105022839015",
- "611752105022839300",
- "611752105022839468",
- "611752105022839559",
- "611752105022892354",
+ "611752105022647071",
+ "611752105022731222",
+ "611752105022735210",
+ "611752105022736491",
+ "611752105022745594",
+ "611752105022765634",
+ "611752105022765638",
+ "611752105022769491",
+ "611752105022773423",
+ "611752105022777020",
+ "611752105022839285",
+ "611752105022840130",
+ "611752105022840856",
+ "611752105022841683",
"611752105022911042",
- "611752105023134539",
- "611752105023142842",
- "611752105023588204",
- "611752105023588294",
- "611752105024204862",
- "611752105024546859",
- "611752105024598735",
- "611752105024608149",
- "611752105024728134",
- "611752105024938931",
- "611752105025198792",
- "611752105025327479",
- "611752105025458721",
- "611752105025458732",
- "611752105025458759",
- "611752105025458792",
- "611752105025458796",
- "611752105025458831",
- "611752105025534832",
- "611752105025538466",
- "611752105025565027",
- "611752105025584548",
- "611752105025586093",
- "611752105025720331",
- "611752105025741198",
- "611752105025741447",
- "611752105025817802",
- "611752105025835130",
- "611752105025879265",
- "611752105026152312",
- "611752105026205732",
- "611752105026281560",
- "611752105026343284",
- "611752105026388268",
- "611752105026421148",
- "611752105026433966",
- "611752105026536897",
- "611752105026536911",
- "611752105026580839",
- "611752105026648945",
- "611752105026663363",
- "611752105026736866",
- "611752105027067863",
- "611752105027112518",
- "611752105027186556",
- "611752105027189208",
- "611752105027189301",
- "611752105027326104",
- "611752105027460089",
- "611752105027484913",
- "611752105027588072",
- "611752105027611383",
- "611752105027690075",
- "611752105028183260",
- "611752105028470803",
- "611752105028507652",
- "611752105028528335",
- "611752105028809597",
- "611752105028815367",
- "611752105028820629",
- "611752105028820633",
- "611752105028827057",
- "611752105028837845",
- "611752105028878340",
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- "611752105030598665"
+ "611752105023177799",
+ "611752105023288035",
+ "611752105023301455"
]
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))
# 不在数据库中
sql = "select song_id from svc_queue_table where song_id={}".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 ({}, \"{}\",{}, {}, 3)" \
.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 1000
"""
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 = "replace 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()

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