Format code (#188)
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52
config.py
52
config.py
@ -64,25 +64,43 @@ if not torch.cuda.is_available():
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device = "cpu"
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is_half = False
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gpu_mem=None
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gpu_mem = None
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if device not in ["cpu", "mps"]:
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i_device=int(device.split(":")[-1])
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i_device = int(device.split(":")[-1])
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gpu_name = torch.cuda.get_device_name(i_device)
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if "16" in gpu_name or "P40"in gpu_name.upper() or "1070"in gpu_name or "1080"in gpu_name:
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if (
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"16" in gpu_name
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or "P40" in gpu_name.upper()
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or "1070" in gpu_name
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or "1080" in gpu_name
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):
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print("16系显卡强制单精度")
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is_half = False
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with open("configs/32k.json","r")as f:strr=f.read().replace("true","false")
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with open("configs/32k.json","w")as f:f.write(strr)
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with open("configs/40k.json","r")as f:strr=f.read().replace("true","false")
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with open("configs/40k.json","w")as f:f.write(strr)
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with open("configs/48k.json","r")as f:strr=f.read().replace("true","false")
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with open("configs/48k.json","w")as f:f.write(strr)
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with open("trainset_preprocess_pipeline_print.py","r")as f:strr=f.read().replace("3.7","3.0")
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with open("trainset_preprocess_pipeline_print.py","w")as f:f.write(strr)
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gpu_mem=int(torch.cuda.get_device_properties(i_device).total_memory/1024/1024/1024+0.4)
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if(gpu_mem<=4):
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with open("trainset_preprocess_pipeline_print.py","r")as f:strr=f.read().replace("3.7","3.0")
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with open("trainset_preprocess_pipeline_print.py","w")as f:f.write(strr)
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with open("configs/32k.json", "r") as f:
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strr = f.read().replace("true", "false")
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with open("configs/32k.json", "w") as f:
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f.write(strr)
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with open("configs/40k.json", "r") as f:
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strr = f.read().replace("true", "false")
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with open("configs/40k.json", "w") as f:
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f.write(strr)
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with open("configs/48k.json", "r") as f:
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strr = f.read().replace("true", "false")
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with open("configs/48k.json", "w") as f:
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f.write(strr)
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with open("trainset_preprocess_pipeline_print.py", "r") as f:
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strr = f.read().replace("3.7", "3.0")
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with open("trainset_preprocess_pipeline_print.py", "w") as f:
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f.write(strr)
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gpu_mem = int(
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torch.cuda.get_device_properties(i_device).total_memory / 1024 / 1024 / 1024
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+ 0.4
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)
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if gpu_mem <= 4:
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with open("trainset_preprocess_pipeline_print.py", "r") as f:
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strr = f.read().replace("3.7", "3.0")
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with open("trainset_preprocess_pipeline_print.py", "w") as f:
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f.write(strr)
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from multiprocessing import cpu_count
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if n_cpu == 0:
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@ -99,8 +117,8 @@ else:
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x_query = 6
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x_center = 38
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x_max = 41
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if(gpu_mem!=None and gpu_mem<=4):
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if gpu_mem != None and gpu_mem <= 4:
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x_pad = 1
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x_query = 5
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x_center = 30
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x_max = 32
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x_max = 32
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8
gui.py
8
gui.py
@ -375,9 +375,7 @@ class GUI:
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self.crossfade_frame = int(self.config.crossfade_time * self.config.samplerate)
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self.sola_search_frame = int(0.012 * self.config.samplerate)
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self.delay_frame = int(0.01 * self.config.samplerate) # 往前预留0.02s
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self.extra_frame = int(
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self.config.extra_time * self.config.samplerate
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)
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self.extra_frame = int(self.config.extra_time * self.config.samplerate)
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self.rvc = None
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self.rvc = RVC(
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self.config.pitch,
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@ -408,7 +406,9 @@ class GUI:
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orig_freq=self.config.samplerate, new_freq=16000, dtype=torch.float32
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)
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self.resampler2 = tat.Resample(
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orig_freq=self.rvc.tgt_sr, new_freq=self.config.samplerate, dtype=torch.float32
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orig_freq=self.rvc.tgt_sr,
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new_freq=self.config.samplerate,
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dtype=torch.float32,
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)
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thread_vc = threading.Thread(target=self.soundinput)
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thread_vc.start()
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76
infer-web.py
76
infer-web.py
@ -1,11 +1,12 @@
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from multiprocessing import cpu_count
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import threading,pdb,librosa
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import threading, pdb, librosa
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from time import sleep
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from subprocess import Popen
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from time import sleep
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import torch, os, traceback, sys, warnings, shutil, numpy as np
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import faiss
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from random import shuffle
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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tmp = os.path.join(now_dir, "TEMP")
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@ -24,7 +25,7 @@ i18n = I18nAuto()
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ncpu = cpu_count()
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ngpu = torch.cuda.device_count()
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gpu_infos = []
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mem=[]
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mem = []
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if (not torch.cuda.is_available()) or ngpu == 0:
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if_gpu_ok = False
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else:
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@ -50,13 +51,21 @@ else:
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): # A10#A100#V100#A40#P40#M40#K80#A4500
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if_gpu_ok = True # 至少有一张能用的N卡
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gpu_infos.append("%s\t%s" % (i, gpu_name))
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mem.append(int(torch.cuda.get_device_properties(i).total_memory/1024/1024/1024+0.4))
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mem.append(
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int(
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torch.cuda.get_device_properties(i).total_memory
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/ 1024
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/ 1024
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/ 1024
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+ 0.4
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)
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)
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if if_gpu_ok == True and len(gpu_infos) > 0:
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gpu_info ="\n".join(gpu_infos)
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default_batch_size=min(mem)//2
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gpu_info = "\n".join(gpu_infos)
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default_batch_size = min(mem) // 2
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else:
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gpu_info = "很遗憾您这没有能用的显卡来支持您训练"
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default_batch_size=1
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default_batch_size = 1
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gpus = "-".join([i[0] for i in gpu_infos])
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from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
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from scipy.io import wavfile
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@ -236,7 +245,7 @@ def vc_multi(
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yield traceback.format_exc()
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def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins,agg):
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def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg):
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infos = []
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try:
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inp_root = inp_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
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@ -258,23 +267,30 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins,agg):
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paths = [path.name for path in paths]
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for path in paths:
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inp_path = os.path.join(inp_root, path)
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need_reformat=1
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done=0
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need_reformat = 1
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done = 0
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try:
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info = ffmpeg.probe(inp_path, cmd="ffprobe")
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if(info["streams"][0]["channels"]==2 and info["streams"][0]["sample_rate"]=="44100"):
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need_reformat=0
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if (
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info["streams"][0]["channels"] == 2
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and info["streams"][0]["sample_rate"] == "44100"
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):
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need_reformat = 0
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pre_fun._path_audio_(inp_path, save_root_ins, save_root_vocal)
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done=1
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done = 1
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except:
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need_reformat = 1
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traceback.print_exc()
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if(need_reformat==1):
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tmp_path="%s/%s.reformatted.wav"%(tmp,os.path.basename(inp_path))
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os.system("ffmpeg -i %s -vn -acodec pcm_s16le -ac 2 -ar 44100 %s -y"%(inp_path,tmp_path))
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inp_path=tmp_path
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if need_reformat == 1:
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tmp_path = "%s/%s.reformatted.wav" % (tmp, os.path.basename(inp_path))
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os.system(
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"ffmpeg -i %s -vn -acodec pcm_s16le -ac 2 -ar 44100 %s -y"
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% (inp_path, tmp_path)
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)
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inp_path = tmp_path
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try:
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if(done==0):pre_fun._path_audio_(inp_path, save_root_ins, save_root_vocal)
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if done == 0:
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pre_fun._path_audio_(inp_path, save_root_ins, save_root_vocal)
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infos.append("%s->Success" % (os.path.basename(inp_path)))
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yield "\n".join(infos)
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except:
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@ -660,11 +676,11 @@ def train_index(exp_dir1):
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big_npy = np.concatenate(npys, 0)
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# np.save("%s/total_fea.npy" % exp_dir, big_npy)
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# n_ivf = big_npy.shape[0] // 39
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n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])),big_npy.shape[0]// 39)
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infos=[]
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infos.append("%s,%s"%(big_npy.shape,n_ivf))
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n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
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infos = []
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infos.append("%s,%s" % (big_npy.shape, n_ivf))
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yield "\n".join(infos)
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index = faiss.index_factory(256, "IVF%s,Flat"%n_ivf)
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index = faiss.index_factory(256, "IVF%s,Flat" % n_ivf)
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# index = faiss.index_factory(256, "IVF%s,PQ128x4fs,RFlat"%n_ivf)
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infos.append("training")
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yield "\n".join(infos)
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@ -672,13 +688,19 @@ def train_index(exp_dir1):
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# index_ivf.nprobe = int(np.power(n_ivf,0.3))
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index_ivf.nprobe = 1
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index.train(big_npy)
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faiss.write_index(index, '%s/trained_IVF%s_Flat_nprobe_%s.index'%(exp_dir,n_ivf,index_ivf.nprobe))
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faiss.write_index(
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index,
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"%s/trained_IVF%s_Flat_nprobe_%s.index" % (exp_dir, n_ivf, index_ivf.nprobe),
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)
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# faiss.write_index(index, '%s/trained_IVF%s_Flat_FastScan.index'%(exp_dir,n_ivf))
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infos.append("adding")
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yield "\n".join(infos)
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index.add(big_npy)
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faiss.write_index(index, '%s/added_IVF%s_Flat_nprobe_%s.index'%(exp_dir,n_ivf,index_ivf.nprobe))
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infos.append("成功构建索引，added_IVF%s_Flat_nprobe_%s.index"%(n_ivf,index_ivf.nprobe))
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faiss.write_index(
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index,
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"%s/added_IVF%s_Flat_nprobe_%s.index" % (exp_dir, n_ivf, index_ivf.nprobe),
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)
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infos.append("成功构建索引，added_IVF%s_Flat_nprobe_%s.index" % (n_ivf, index_ivf.nprobe))
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# faiss.write_index(index, '%s/added_IVF%s_Flat_FastScan.index'%(exp_dir,n_ivf))
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# infos.append("成功构建索引，added_IVF%s_Flat_FastScan.index"%(n_ivf))
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yield "\n".join(infos)
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@ -876,7 +898,7 @@ def train1key(
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big_npy = np.concatenate(npys, 0)
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# np.save("%s/total_fea.npy" % exp_dir, big_npy)
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# n_ivf = big_npy.shape[0] // 39
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n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])),big_npy.shape[0]// 39)
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n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
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yield get_info_str("%s,%s" % (big_npy.shape, n_ivf))
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index = faiss.index_factory(256, "IVF%s,Flat" % n_ivf)
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yield get_info_str("training index")
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@ -1171,7 +1193,7 @@ with gr.Blocks() as app:
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label="人声提取激进程度",
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value=10,
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interactive=True,
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visible=False#先不开放调整
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visible=False, # 先不开放调整
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)
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opt_vocal_root = gr.Textbox(
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label=i18n("指定输出人声文件夹"), value="opt"
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@ -1187,7 +1209,7 @@ with gr.Blocks() as app:
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opt_vocal_root,
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wav_inputs,
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opt_ins_root,
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agg
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agg,
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],
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[vc_output4],
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)
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@ -13,7 +13,7 @@ from scipy.io import wavfile
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class _audio_pre_:
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def __init__(self, agg,model_path, device, is_half):
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def __init__(self, agg, model_path, device, is_half):
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self.model_path = model_path
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self.device = device
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self.data = {
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@ -139,7 +139,9 @@ class _audio_pre_:
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wav_instrument = spec_utils.cmb_spectrogram_to_wave(y_spec_m, self.mp)
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print("%s instruments done" % name)
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wavfile.write(
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os.path.join(ins_root, "instrument_{}_{}.wav".format(name,self.data["agg"])),
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os.path.join(
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ins_root, "instrument_{}_{}.wav".format(name, self.data["agg"])
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),
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self.mp.param["sr"],
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(np.array(wav_instrument) * 32768).astype("int16"),
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) #
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@ -155,7 +157,9 @@ class _audio_pre_:
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wav_vocals = spec_utils.cmb_spectrogram_to_wave(v_spec_m, self.mp)
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print("%s vocals done" % name)
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wavfile.write(
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os.path.join(vocal_root, "vocal_{}_{}.wav".format(name,self.data["agg"])),
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os.path.join(
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vocal_root, "vocal_{}_{}.wav".format(name, self.data["agg"])
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),
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self.mp.param["sr"],
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(np.array(wav_vocals) * 32768).astype("int16"),
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)
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