mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2026-01-19 18:41:52 +00:00
Format code (#188)
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
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commit
9068d5283e
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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