fix: train step2a & add arg --port --pycmd --noparallel
This commit is contained in:
源文雨
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.gitignore
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@ -3,3 +3,4 @@ __pycache__
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/TEMP
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*.pyd
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hubert_base.pt
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/logs
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14
config.py
14
config.py
@ -1,3 +1,10 @@
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument("--port", type=int, default=7865, help="Listen port")
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parser.add_argument("--pycmd", type=str, default="python", help="Python command")
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parser.add_argument("--colab", action='store_true', help="Launch in colab")
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parser.add_argument("--noparallel", action='store_true', help="Disable parallel processing")
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cmd_opts = parser.parse_args()
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############离线VC参数
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inp_root=r"白鹭霜华长条"#对输入目录下所有音频进行转换，别放非音频文件
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opt_root=r"opt"#输出目录
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@ -7,10 +14,15 @@ person=r"weights\洛天依v3.pt"#目前只有洛天依v3
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device = "cuda:0"#填写cuda:x或cpu，x指代第几张卡，只支持N卡加速
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is_half=True#9-10-20-30-40系显卡无脑True，不影响质量，>=20显卡开启有加速
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n_cpu=0#默认0用上所有线程，写数字限制CPU资源使用
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############python命令路径
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python_cmd=cmd_opts.pycmd
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listen_port=cmd_opts.port
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iscolab=cmd_opts.colab
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noparallel=cmd_opts.noparallel
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############下头别动
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import torch
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if(torch.cuda.is_available()==False):
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print("没有发现支持的N卡，使用CPU进行推理")
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print("没有发现支持的N卡, 使用CPU进行推理")
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device="cpu"
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is_half=False
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if(device!="cpu"):
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35
infer-web.py
35
infer-web.py
@ -1,9 +1,10 @@
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from multiprocessing import cpu_count
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import threading
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from time import sleep
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from subprocess import Popen,PIPE,run as runn
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from subprocess import Popen
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from time import sleep
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import torch, pdb, os,traceback,sys,warnings,shutil,numpy as np,faiss
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import torch, os,traceback,sys,warnings,shutil,numpy as np
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import faiss
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#判断是否有能用来训练和加速推理的N卡
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ncpu=cpu_count()
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ngpu=torch.cuda.device_count()
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@ -33,11 +34,9 @@ from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFs
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from scipy.io import wavfile
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from fairseq import checkpoint_utils
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import gradio as gr
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import librosa
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import logging
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from vc_infer_pipeline import VC
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import soundfile as sf
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from config import is_half,device,is_half
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from config import is_half,device,is_half,python_cmd,listen_port,iscolab,noparallel
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from infer_uvr5 import _audio_pre_
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from my_utils import load_audio
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from train.process_ckpt import show_info,change_info,merge,extract_small_model
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@ -222,7 +221,7 @@ def preprocess_dataset(trainset_dir,exp_dir,sr,n_p=ncpu):
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os.makedirs("%s/logs/%s"%(now_dir,exp_dir),exist_ok=True)
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f = open("%s/logs/%s/preprocess.log"%(now_dir,exp_dir), "w")
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f.close()
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cmd="python trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s"%(trainset_dir,sr,n_p,now_dir,exp_dir)
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cmd=python_cmd + " trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s "%(trainset_dir,sr,n_p,now_dir,exp_dir)+str(noparallel)
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print(cmd)
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p = Popen(cmd, shell=True)#, stdin=PIPE, stdout=PIPE,stderr=PIPE,cwd=now_dir
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###煞笔gr，popen read都非得全跑完了再一次性读取，不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
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@ -242,7 +241,7 @@ def extract_f0_feature(gpus,n_p,f0method,if_f0,exp_dir):
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f = open("%s/logs/%s/extract_f0_feature.log"%(now_dir,exp_dir), "w")
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f.close()
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if(if_f0=="是"):
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cmd="python extract_f0_print.py %s/logs/%s %s %s"%(now_dir,exp_dir,n_p,f0method)
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cmd=python_cmd + " extract_f0_print.py %s/logs/%s %s %s"%(now_dir,exp_dir,n_p,f0method)
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print(cmd)
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p = Popen(cmd, shell=True,cwd=now_dir)#, stdin=PIPE, stdout=PIPE,stderr=PIPE
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###煞笔gr，popen read都非得全跑完了再一次性读取，不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
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@ -266,7 +265,7 @@ def extract_f0_feature(gpus,n_p,f0method,if_f0,exp_dir):
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leng=len(gpus)
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ps=[]
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for idx,n_g in enumerate(gpus):
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cmd="python extract_feature_print.py %s %s %s %s/logs/%s"%(leng,idx,n_g,now_dir,exp_dir)
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cmd=python_cmd + " extract_feature_print.py %s %s %s %s/logs/%s"%(leng,idx,n_g,now_dir,exp_dir)
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print(cmd)
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p = Popen(cmd, shell=True, cwd=now_dir)#, shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
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ps.append(p)
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@ -305,8 +304,8 @@ def click_train(exp_dir1,sr2,if_f0_3,spk_id5,save_epoch10,total_epoch11,batch_si
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with open("%s/filelist.txt"%exp_dir,"w")as f:f.write("\n".join(opt))
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print("write filelist done")
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#生成config#无需生成config
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# cmd = "python train_nsf_sim_cache_sid_load_pretrain.py -e mi-test -sr 40k -f0 1 -bs 4 -g 0 -te 10 -se 5 -pg pretrained/f0G40k.pth -pd pretrained/f0D40k.pth -l 1 -c 0"
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cmd = "python train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s -pg %s -pd %s -l %s -c %s" % (exp_dir1,sr2,1 if if_f0_3=="是"else 0,batch_size12,gpus16,total_epoch11,save_epoch10,pretrained_G14,pretrained_D15,1 if if_save_latest13=="是"else 0,1 if if_cache_gpu17=="是"else 0)
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# cmd = python_cmd + " train_nsf_sim_cache_sid_load_pretrain.py -e mi-test -sr 40k -f0 1 -bs 4 -g 0 -te 10 -se 5 -pg pretrained/f0G40k.pth -pd pretrained/f0D40k.pth -l 1 -c 0"
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cmd = python_cmd + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s -pg %s -pd %s -l %s -c %s" % (exp_dir1,sr2,1 if if_f0_3=="是"else 0,batch_size12,gpus16,total_epoch11,save_epoch10,pretrained_G14,pretrained_D15,1 if if_save_latest13=="是"else 0,1 if if_cache_gpu17=="是"else 0)
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print(cmd)
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p = Popen(cmd, shell=True, cwd=now_dir)
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p.wait()
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@ -351,7 +350,7 @@ def train1key(exp_dir1, sr2, if_f0_3, trainset_dir4, spk_id5, gpus6, np7, f0meth
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os.makedirs("%s/logs/%s"%(now_dir,exp_dir1),exist_ok=True)
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#########step1:处理数据
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open("%s/logs/%s/preprocess.log"%(now_dir,exp_dir1), "w").close()
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cmd="python trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s"%(trainset_dir4,sr_dict[sr2],ncpu,now_dir,exp_dir1)
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cmd=python_cmd + " trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s "%(trainset_dir4,sr_dict[sr2],ncpu,now_dir,exp_dir1)+str(noparallel)
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yield get_info_str("step1:正在处理数据")
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yield get_info_str(cmd)
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p = Popen(cmd, shell=True)
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@ -361,7 +360,7 @@ def train1key(exp_dir1, sr2, if_f0_3, trainset_dir4, spk_id5, gpus6, np7, f0meth
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open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir1), "w")
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if(if_f0_3=="是"):
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yield get_info_str("step2a:正在提取音高")
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cmd="python extract_f0_print.py %s/logs/%s %s %s"%(now_dir,exp_dir1,np7,f0method8)
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cmd=python_cmd + " extract_f0_print.py %s/logs/%s %s %s"%(now_dir,exp_dir1,np7,f0method8)
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yield get_info_str(cmd)
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p = Popen(cmd, shell=True,cwd=now_dir)
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p.wait()
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@ -373,7 +372,7 @@ def train1key(exp_dir1, sr2, if_f0_3, trainset_dir4, spk_id5, gpus6, np7, f0meth
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leng=len(gpus)
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ps=[]
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for idx,n_g in enumerate(gpus):
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cmd="python extract_feature_print.py %s %s %s %s/logs/%s"%(leng,idx,n_g,now_dir,exp_dir1)
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cmd=python_cmd + " extract_feature_print.py %s %s %s %s/logs/%s"%(leng,idx,n_g,now_dir,exp_dir1)
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yield get_info_str(cmd)
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p = Popen(cmd, shell=True, cwd=now_dir)#, shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
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ps.append(p)
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@ -399,7 +398,7 @@ def train1key(exp_dir1, sr2, if_f0_3, trainset_dir4, spk_id5, gpus6, np7, f0meth
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opt.append("%s/%s.wav|%s/%s.npy|%s"%(gt_wavs_dir.replace("\\","\\\\"),name,co256_dir.replace("\\","\\\\"),name,spk_id5))
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with open("%s/filelist.txt"%exp_dir,"w")as f:f.write("\n".join(opt))
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yield get_info_str("write filelist done")
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cmd = "python train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s -pg %s -pd %s -l %s -c %s" % (exp_dir1,sr2,1 if if_f0_3=="是"else 0,batch_size12,gpus16,total_epoch11,save_epoch10,pretrained_G14,pretrained_D15,1 if if_save_latest13=="是"else 0,1 if if_cache_gpu17=="是"else 0)
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cmd = python_cmd + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s -pg %s -pd %s -l %s -c %s" % (exp_dir1,sr2,1 if if_f0_3=="是"else 0,batch_size12,gpus16,total_epoch11,save_epoch10,pretrained_G14,pretrained_D15,1 if if_save_latest13=="是"else 0,1 if if_cache_gpu17=="是"else 0)
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yield get_info_str(cmd)
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p = Popen(cmd, shell=True, cwd=now_dir)
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p.wait()
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@ -630,11 +629,7 @@ with gr.Blocks() as app:
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with gr.TabItem("点击查看交流、问题反馈群号"):
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gr.Markdown(value="""xxxxx""")
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument("--colab", action='store_true', help="Launch in colab")
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cmd_opts = parser.parse_args()
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if cmd_opts.colab:
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if iscolab:
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app.queue(concurrency_count=511, max_size=1022).launch(share=True)
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else:
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app.queue(concurrency_count=511, max_size=1022).launch(server_name="0.0.0.0",inbrowser=True,server_port=7865,quiet=True)
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app.queue(concurrency_count=511, max_size=1022).launch(server_name="0.0.0.0",inbrowser=True,server_port=listen_port,quiet=True)
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@ -10,10 +10,7 @@ def load_audio(file,sr):
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.output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sr)
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.run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
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)
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except ffmpeg.Error as e:
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raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
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except Exception as e:
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raise RuntimeError(f"Failed to load audio: {e}")
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return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
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if __name__=='__main__' :
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print(load_audio(r"C:\CloudMusic\宮野幸子,森下唯 - 月夜に謳う君 -LUNA-.mp3",16000).shape)
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@ -4,7 +4,6 @@ import numpy as np
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# This function is obtained from librosa.
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def get_rms(
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y,
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*,
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frame_length=2048,
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hop_length=512,
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pad_mode="constant",
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@ -1,4 +1,4 @@
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import sys,os,pdb,multiprocessing
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import sys,os,multiprocessing
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now_dir=os.getcwd()
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sys.path.append(now_dir)
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@ -6,20 +6,15 @@ inp_root = sys.argv[1]
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sr = int(sys.argv[2])
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n_p = int(sys.argv[3])
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exp_dir = sys.argv[4]
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import numpy as np,ffmpeg,os,traceback
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noparallel = sys.argv[5] == "True"
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import numpy as np,os,traceback
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from slicer2 import Slicer
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from joblib import Parallel, delayed
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import librosa,traceback
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from scipy.io import wavfile
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import multiprocessing
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from my_utils import load_audio
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from time import sleep
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f = open("%s/preprocess.log"%exp_dir, "a+")
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def printt(strr):
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print(strr)
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f.write("%s\n" % strr)
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f.flush()
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mutex = multiprocessing.Lock()
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class PreProcess():
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def __init__(self,sr,exp_dir):
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@ -40,10 +35,18 @@ class PreProcess():
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self.exp_dir=exp_dir
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self.gt_wavs_dir="%s/0_gt_wavs"%exp_dir
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self.wavs16k_dir="%s/1_16k_wavs"%exp_dir
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self.f = open("%s/preprocess.log"%exp_dir, "a+")
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os.makedirs(self.exp_dir,exist_ok=True)
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os.makedirs(self.gt_wavs_dir,exist_ok=True)
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os.makedirs(self.wavs16k_dir,exist_ok=True)
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def print(self, strr):
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mutex.acquire()
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print(strr)
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self.f.write("%s\n" % strr)
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self.f.flush()
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mutex.release()
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def norm_write(self,tmp_audio,idx0,idx1):
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tmp_audio = (tmp_audio / np.abs(tmp_audio).max() * (self.max * self.alpha)) + (1 - self.alpha) * tmp_audio
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wavfile.write("%s/%s_%s.wav" % (self.gt_wavs_dir, idx0, idx1), self.sr, (tmp_audio*32768).astype(np.int16))
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@ -67,9 +70,9 @@ class PreProcess():
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tmp_audio = audio[start:]
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break
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self.norm_write(tmp_audio, idx0, idx1)
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printt("%s->Suc."%path)
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self.print("%s->Suc."%path)
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except:
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printt("%s->%s"%(path,traceback.format_exc()))
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self.print("%s->%s"%(path,traceback.format_exc()))
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def pipeline_mp(self,infos):
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for path, idx0 in infos:
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@ -78,27 +81,24 @@ class PreProcess():
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def pipeline_mp_inp_dir(self,inp_root,n_p):
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try:
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infos = [("%s/%s" % (inp_root, name), idx) for idx, name in enumerate(sorted(list(os.listdir(inp_root))))]
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ps=[]
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for i in range(n_p):
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p=multiprocessing.Process(target=self.pipeline_mp,args=(infos[i::n_p],))
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p.start()
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ps.append(p)
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for p in ps:p.join()
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if noparallel:
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for i in range(n_p): self.pipeline_mp(infos[i::n_p])
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else:
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ps=[]
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for i in range(n_p):
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p=multiprocessing.Process(target=self.pipeline_mp,args=(infos[i::n_p],))
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p.start()
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ps.append(p)
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for p in ps:p.join()
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except:
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printt("Fail. %s"%traceback.format_exc())
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self.print("Fail. %s"%traceback.format_exc())
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def preprocess_trainset(inp_root, sr, n_p, exp_dir):
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pp=PreProcess(sr,exp_dir)
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pp.print("start preprocess")
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pp.print(sys.argv)
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pp.pipeline_mp_inp_dir(inp_root,n_p)
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pp.print("end preprocess")
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if __name__=='__main__':
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# f = open("logs/log_preprocess.log", "w")
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printt(sys.argv)
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######################################################
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# inp_root=r"E:\语音音频+标注\米津玄师\src"
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# inp_root=r"E:\codes\py39\vits_vc_gpu_train\todo-songs"
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# sr=40000
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# n_p = 6
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# exp_dir=r"E:\codes\py39\dataset\mi-test"
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######################################################
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printt("start preprocess")
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pp=PreProcess(sr,exp_dir)
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pp.pipeline_mp_inp_dir(inp_root,n_p)
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printt("end preprocess")
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preprocess_trainset(inp_root, sr, n_p, exp_dir)
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@ -1,6 +1,7 @@
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MIT License
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Copyright (c) 2023 liujing04
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Copyright (c) 2023 源文雨
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本软件及其相关代码以MIT协议开源，作者不对软件具备任何控制力，使用软件者、传播软件导出的声音者自负全责。
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如不认可该条款，则不能使用或引用软件包内任何代码和文件。
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