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", - 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"611752105030597505", - "611752105030598550", - "611752105030598562", - "611752105030598634", - "611752105030598642", - "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()