fix: train step2a & add arg --port --pycmd --noparallel

.gitignore

@@ -3,3 +3,4 @@ __pycache__
 /TEMP
 *.pyd
 hubert_base.pt
+/logs

config.py

@@ -1,3 +1,10 @@
+import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument("--port", type=int, default=7865, help="Listen port")
+parser.add_argument("--pycmd", type=str, default="python", help="Python command")
+parser.add_argument("--colab", action='store_true', help="Launch in colab")
+parser.add_argument("--noparallel", action='store_true', help="Disable parallel processing")
+cmd_opts = parser.parse_args()
 ############离线VC参数
 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
 opt_root=r"opt"#输出目录
@@ -7,10 +14,15 @@ person=r"weights\洛天依v3.pt"#目前只有洛天依v3
 device = "cuda:0"#填写cuda:x或cpu，x指代第几张卡，只支持N卡加速
 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
 n_cpu=0#默认0用上所有线程，写数字限制CPU资源使用
+############python命令路径
+python_cmd=cmd_opts.pycmd
+listen_port=cmd_opts.port
+iscolab=cmd_opts.colab
+noparallel=cmd_opts.noparallel
 ############下头别动
 import torch
 if(torch.cuda.is_available()==False):
-    print("没有发现支持的N卡，使用CPU进行推理")
+    print("没有发现支持的N卡, 使用CPU进行推理")
     device="cpu"
     is_half=False
 if(device!="cpu"):

infer-web.py

@@ -1,9 +1,10 @@
 from multiprocessing import cpu_count
 import threading
 from time import sleep
-from subprocess import Popen,PIPE,run as runn
+from subprocess import Popen
 from time import sleep
-import torch, pdb, os,traceback,sys,warnings,shutil,numpy as np,faiss
+import torch, os,traceback,sys,warnings,shutil,numpy as np
+import faiss
 #判断是否有能用来训练和加速推理的N卡
 ncpu=cpu_count()
 ngpu=torch.cuda.device_count()
@@ -33,11 +34,9 @@ from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFs
 from scipy.io import wavfile
 from fairseq import checkpoint_utils
 import gradio as gr
-import librosa
 import logging
 from vc_infer_pipeline import VC
-import soundfile as sf
+from config import is_half,device,is_half,python_cmd,listen_port,iscolab,noparallel
-from config import is_half,device,is_half
 from infer_uvr5 import _audio_pre_
 from my_utils import load_audio
 from train.process_ckpt import show_info,change_info,merge,extract_small_model
@@ -222,7 +221,7 @@ def preprocess_dataset(trainset_dir,exp_dir,sr,n_p=ncpu):
     os.makedirs("%s/logs/%s"%(now_dir,exp_dir),exist_ok=True)
     f = open("%s/logs/%s/preprocess.log"%(now_dir,exp_dir), "w")
     f.close()
-    cmd="python trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s"%(trainset_dir,sr,n_p,now_dir,exp_dir)
+    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)
     print(cmd)
     p = Popen(cmd, shell=True)#, stdin=PIPE, stdout=PIPE,stderr=PIPE,cwd=now_dir
     ###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
@@ -242,7 +241,7 @@ def extract_f0_feature(gpus,n_p,f0method,if_f0,exp_dir):
     f = open("%s/logs/%s/extract_f0_feature.log"%(now_dir,exp_dir), "w")
     f.close()
     if(if_f0=="是"):
-        cmd="python extract_f0_print.py %s/logs/%s %s %s"%(now_dir,exp_dir,n_p,f0method)
+        cmd=python_cmd + " extract_f0_print.py %s/logs/%s %s %s"%(now_dir,exp_dir,n_p,f0method)
         print(cmd)
         p = Popen(cmd, shell=True,cwd=now_dir)#, stdin=PIPE, stdout=PIPE,stderr=PIPE
         ###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
@@ -266,7 +265,7 @@ def extract_f0_feature(gpus,n_p,f0method,if_f0,exp_dir):
     leng=len(gpus)
     ps=[]
     for idx,n_g in enumerate(gpus):
-        cmd="python extract_feature_print.py %s %s %s %s/logs/%s"%(leng,idx,n_g,now_dir,exp_dir)
+        cmd=python_cmd + " extract_feature_print.py %s %s %s %s/logs/%s"%(leng,idx,n_g,now_dir,exp_dir)
         print(cmd)
         p = Popen(cmd, shell=True, cwd=now_dir)#, shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
         ps.append(p)
@@ -305,8 +304,8 @@ def click_train(exp_dir1,sr2,if_f0_3,spk_id5,save_epoch10,total_epoch11,batch_si
     with open("%s/filelist.txt"%exp_dir,"w")as f:f.write("\n".join(opt))
     print("write filelist done")
     #生成config#无需生成config
-    # 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"
+    # 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"
-    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)
+    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)
     print(cmd)
     p = Popen(cmd, shell=True, cwd=now_dir)
     p.wait()
@@ -351,7 +350,7 @@ def train1key(exp_dir1, sr2, if_f0_3, trainset_dir4, spk_id5, gpus6, np7, f0meth
     os.makedirs("%s/logs/%s"%(now_dir,exp_dir1),exist_ok=True)
     #########step1:处理数据
     open("%s/logs/%s/preprocess.log"%(now_dir,exp_dir1), "w").close()
-    cmd="python trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s"%(trainset_dir4,sr_dict[sr2],ncpu,now_dir,exp_dir1)
+    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)
     yield get_info_str("step1:正在处理数据")
     yield get_info_str(cmd)
     p = Popen(cmd, shell=True)
@@ -361,7 +360,7 @@ def train1key(exp_dir1, sr2, if_f0_3, trainset_dir4, spk_id5, gpus6, np7, f0meth
     open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir1), "w")
     if(if_f0_3=="是"):
         yield get_info_str("step2a:正在提取音高")
-        cmd="python extract_f0_print.py %s/logs/%s %s %s"%(now_dir,exp_dir1,np7,f0method8)
+        cmd=python_cmd + " extract_f0_print.py %s/logs/%s %s %s"%(now_dir,exp_dir1,np7,f0method8)
         yield get_info_str(cmd)
         p = Popen(cmd, shell=True,cwd=now_dir)
         p.wait()
@@ -373,7 +372,7 @@ def train1key(exp_dir1, sr2, if_f0_3, trainset_dir4, spk_id5, gpus6, np7, f0meth
     leng=len(gpus)
     ps=[]
     for idx,n_g in enumerate(gpus):
-        cmd="python extract_feature_print.py %s %s %s %s/logs/%s"%(leng,idx,n_g,now_dir,exp_dir1)
+        cmd=python_cmd + " extract_feature_print.py %s %s %s %s/logs/%s"%(leng,idx,n_g,now_dir,exp_dir1)
         yield get_info_str(cmd)
         p = Popen(cmd, shell=True, cwd=now_dir)#, shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
         ps.append(p)
@@ -399,7 +398,7 @@ def train1key(exp_dir1, sr2, if_f0_3, trainset_dir4, spk_id5, gpus6, np7, f0meth
             opt.append("%s/%s.wav|%s/%s.npy|%s"%(gt_wavs_dir.replace("\\","\\\\"),name,co256_dir.replace("\\","\\\\"),name,spk_id5))
     with open("%s/filelist.txt"%exp_dir,"w")as f:f.write("\n".join(opt))
     yield get_info_str("write filelist done")
-    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)
+    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)
     yield get_info_str(cmd)
     p = Popen(cmd, shell=True, cwd=now_dir)
     p.wait()
@@ -630,11 +629,7 @@ with gr.Blocks() as app:
         with gr.TabItem("点击查看交流、问题反馈群号"):
             gr.Markdown(value="""xxxxx""")
 
-    import argparse
+    if iscolab:
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--colab", action='store_true', help="Launch in colab")
-    cmd_opts = parser.parse_args()
-    if cmd_opts.colab:
         app.queue(concurrency_count=511, max_size=1022).launch(share=True)
     else:
-        app.queue(concurrency_count=511, max_size=1022).launch(server_name="0.0.0.0",inbrowser=True,server_port=7865,quiet=True)
+        app.queue(concurrency_count=511, max_size=1022).launch(server_name="0.0.0.0",inbrowser=True,server_port=listen_port,quiet=True)

my_utils.py

@@ -10,10 +10,7 @@ def load_audio(file,sr):
             .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sr)
             .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
         )
-    except ffmpeg.Error as e:
+    except Exception as e:
-        raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
+        raise RuntimeError(f"Failed to load audio: {e}")
 
     return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
-
-if __name__=='__main__' :
-    print(load_audio(r"C:\CloudMusic\宮野幸子,森下唯 - 月夜に謳う君 -LUNA-.mp3",16000).shape)

slicer2.py

@@ -4,7 +4,6 @@ import numpy as np
 # This function is obtained from librosa.
 def get_rms(
     y,
-    *,
     frame_length=2048,
     hop_length=512,
     pad_mode="constant",

trainset_preprocess_pipeline_print.py

@@ -1,4 +1,4 @@
-import sys,os,pdb,multiprocessing
+import sys,os,multiprocessing
 now_dir=os.getcwd()
 sys.path.append(now_dir)
 
@@ -6,20 +6,15 @@ inp_root = sys.argv[1]
 sr = int(sys.argv[2])
 n_p = int(sys.argv[3])
 exp_dir = sys.argv[4]
-import numpy as np,ffmpeg,os,traceback
+noparallel = sys.argv[5] == "True"
+import numpy as np,os,traceback
 from slicer2 import Slicer
-from joblib import Parallel, delayed
 import librosa,traceback
 from  scipy.io import wavfile
 import multiprocessing
 from my_utils import load_audio
-from time import sleep
 
-f = open("%s/preprocess.log"%exp_dir, "a+")
+mutex = multiprocessing.Lock()
-def printt(strr):
-    print(strr)
-    f.write("%s\n" % strr)
-    f.flush()
 
 class PreProcess():
     def __init__(self,sr,exp_dir):
@@ -40,10 +35,18 @@ class PreProcess():
         self.exp_dir=exp_dir
         self.gt_wavs_dir="%s/0_gt_wavs"%exp_dir
         self.wavs16k_dir="%s/1_16k_wavs"%exp_dir
+        self.f = open("%s/preprocess.log"%exp_dir, "a+")
         os.makedirs(self.exp_dir,exist_ok=True)
         os.makedirs(self.gt_wavs_dir,exist_ok=True)
         os.makedirs(self.wavs16k_dir,exist_ok=True)
 
+    def print(self, strr):
+        mutex.acquire()
+        print(strr)
+        self.f.write("%s\n" % strr)
+        self.f.flush()
+        mutex.release()
+
     def norm_write(self,tmp_audio,idx0,idx1):
         tmp_audio = (tmp_audio / np.abs(tmp_audio).max() * (self.max * self.alpha)) + (1 - self.alpha) * tmp_audio
         wavfile.write("%s/%s_%s.wav" % (self.gt_wavs_dir, idx0, idx1), self.sr, (tmp_audio*32768).astype(np.int16))
@@ -67,9 +70,9 @@ class PreProcess():
                         tmp_audio = audio[start:]
                         break
                 self.norm_write(tmp_audio, idx0, idx1)
-            printt("%s->Suc."%path)
+            self.print("%s->Suc."%path)
         except:
-            printt("%s->%s"%(path,traceback.format_exc()))
+            self.print("%s->%s"%(path,traceback.format_exc()))
 
     def pipeline_mp(self,infos):
         for path, idx0 in infos:
@@ -78,27 +81,24 @@ class PreProcess():
     def pipeline_mp_inp_dir(self,inp_root,n_p):
         try:
             infos = [("%s/%s" % (inp_root, name), idx) for idx, name in enumerate(sorted(list(os.listdir(inp_root))))]
-            ps=[]
+            if noparallel:
-            for i in range(n_p):
+                for i in range(n_p): self.pipeline_mp(infos[i::n_p])
-                p=multiprocessing.Process(target=self.pipeline_mp,args=(infos[i::n_p],))
+            else:
-                p.start()
+                ps=[]
-                ps.append(p)
+                for i in range(n_p):
-            for p in ps:p.join()
+                    p=multiprocessing.Process(target=self.pipeline_mp,args=(infos[i::n_p],))
+                    p.start()
+                    ps.append(p)
+                    for p in ps:p.join()
         except:
-            printt("Fail. %s"%traceback.format_exc())
+            self.print("Fail. %s"%traceback.format_exc())
+
+def preprocess_trainset(inp_root, sr, n_p, exp_dir):
+    pp=PreProcess(sr,exp_dir)
+    pp.print("start preprocess")
+    pp.print(sys.argv)
+    pp.pipeline_mp_inp_dir(inp_root,n_p)
+    pp.print("end preprocess")
 
 if __name__=='__main__':
-    # f = open("logs/log_preprocess.log", "w")
+    preprocess_trainset(inp_root, sr, n_p, exp_dir)
-    printt(sys.argv)
-    ######################################################
-    # inp_root=r"E:\语音音频+标注\米津玄师\src"
-    # inp_root=r"E:\codes\py39\vits_vc_gpu_train\todo-songs"
-    # sr=40000
-    # n_p = 6
-    # exp_dir=r"E:\codes\py39\dataset\mi-test"
-
-    ######################################################
-    printt("start preprocess")
-    pp=PreProcess(sr,exp_dir)
-    pp.pipeline_mp_inp_dir(inp_root,n_p)
-    printt("end preprocess")

使用需遵守的协议-LICENSE.txt

@@ -1,6 +1,7 @@
 MIT License
 
 Copyright (c) 2023 liujing04
+Copyright (c) 2023 源文雨
 
         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。
         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。