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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 time import sleep |
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import torch, pdb, os,traceback,sys,warnings,shutil,numpy as np,faiss |
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|
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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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if(torch.cuda.is_available()==False or ngpu==0):if_gpu_ok=False |
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else: |
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if_gpu_ok = False |
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for i in range(ngpu): |
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gpu_name=torch.cuda.get_device_name(i) |
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if("16"in gpu_name or "MX"in gpu_name):continue |
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if("10"in gpu_name or "20"in gpu_name or "30"in gpu_name or "40"in gpu_name or "A50"in gpu_name.upper() or "70"in gpu_name or "80"in gpu_name or "90"in gpu_name or "M4"in gpu_name or "T4"in gpu_name or "TITAN"in gpu_name.upper()): |
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if_gpu_ok=True |
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gpu_infos.append("%s\t%s"%(i,gpu_name)) |
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gpu_info="\n".join(gpu_infos)if if_gpu_ok==True and len(gpu_infos)>0 else "很遗憾您这没有能用的显卡来支持您训练" |
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gpus="-".join([i[0]for i in gpu_infos]) |
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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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shutil.rmtree(tmp,ignore_errors=True) |
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os.makedirs(tmp,exist_ok=True) |
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os.makedirs(os.path.join(now_dir,"logs"),exist_ok=True) |
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os.makedirs(os.path.join(now_dir,"weights"),exist_ok=True) |
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os.environ["TEMP"]=tmp |
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warnings.filterwarnings("ignore") |
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torch.manual_seed(114514) |
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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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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 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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|
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logging.getLogger('numba').setLevel(logging.WARNING) |
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|
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class ToolButton(gr.Button, gr.components.FormComponent): |
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"""Small button with single emoji as text, fits inside gradio forms""" |
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def __init__(self, **kwargs): |
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super().__init__(variant="tool", **kwargs) |
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def get_block_name(self): |
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return "button" |
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|
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hubert_model=None |
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def load_hubert(): |
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global hubert_model |
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models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(["hubert_base.pt"],suffix="",) |
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hubert_model = models[0] |
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hubert_model = hubert_model.to(device) |
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if(is_half):hubert_model = hubert_model.half() |
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else:hubert_model = hubert_model.float() |
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hubert_model.eval() |
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|
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weight_root="weights" |
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weight_uvr5_root="uvr5_weights" |
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names=[] |
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for name in os.listdir(weight_root):names.append(name) |
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uvr5_names=[] |
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for name in os.listdir(weight_uvr5_root):uvr5_names.append(name.replace(".pth","")) |
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|
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def vc_single(sid,input_audio,f0_up_key,f0_file,f0_method,file_index,file_big_npy,index_rate): |
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global tgt_sr,net_g,vc,hubert_model |
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if input_audio is None:return "You need to upload an audio", None |
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f0_up_key = int(f0_up_key) |
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try: |
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audio=load_audio(input_audio,16000) |
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times = [0, 0, 0] |
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if(hubert_model==None):load_hubert() |
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if_f0 = cpt.get("f0", 1) |
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audio_opt=vc.pipeline(hubert_model,net_g,sid,audio,times,f0_up_key,f0_method,file_index,file_big_npy,index_rate,if_f0,f0_file=f0_file) |
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print(times) |
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return "Success", (tgt_sr, audio_opt) |
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except: |
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info=traceback.format_exc() |
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print(info) |
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return info,(None,None) |
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|
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def vc_multi(sid,dir_path,opt_root,paths,f0_up_key,f0_method,file_index,file_big_npy,index_rate): |
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try: |
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dir_path=dir_path.strip(" ") |
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opt_root=opt_root.strip(" ") |
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os.makedirs(opt_root, exist_ok=True) |
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try: |
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if(dir_path!=""):paths=[os.path.join(dir_path,name)for name in os.listdir(dir_path)] |
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else:paths=[path.name for path in paths] |
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except: |
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traceback.print_exc() |
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paths = [path.name for path in paths] |
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infos=[] |
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for path in paths: |
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info,opt=vc_single(sid,path,f0_up_key,None,f0_method,file_index,file_big_npy,index_rate) |
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if(info=="Success"): |
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try: |
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tgt_sr,audio_opt=opt |
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wavfile.write("%s/%s" % (opt_root, os.path.basename(path)), tgt_sr, audio_opt) |
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except: |
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info=traceback.format_exc() |
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infos.append("%s->%s"%(os.path.basename(path),info)) |
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yield "\n".join(infos) |
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yield "\n".join(infos) |
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except: |
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yield traceback.format_exc() |
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|
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def uvr(model_name,inp_root,save_root_vocal,paths,save_root_ins): |
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infos = [] |
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try: |
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inp_root = inp_root.strip(" ").strip("\n") |
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save_root_vocal = save_root_vocal.strip(" ").strip("\n") |
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save_root_ins = save_root_ins.strip(" ").strip("\n") |
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pre_fun = _audio_pre_(model_path=os.path.join(weight_uvr5_root,model_name+".pth"), device=device, is_half=is_half) |
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if (inp_root != ""):paths = [os.path.join(inp_root, name) for name in os.listdir(inp_root)] |
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else:paths = [path.name for path in paths] |
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for name in paths: |
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inp_path=os.path.join(inp_root,name) |
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try: |
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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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infos.append("%s->%s" % (os.path.basename(inp_path),traceback.format_exc())) |
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yield "\n".join(infos) |
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except: |
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infos.append(traceback.format_exc()) |
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yield "\n".join(infos) |
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finally: |
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try: |
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del pre_fun.model |
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del pre_fun |
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except: |
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traceback.print_exc() |
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print("clean_empty_cache") |
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torch.cuda.empty_cache() |
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yield "\n".join(infos) |
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|
|
|
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def get_vc(sid): |
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global n_spk,tgt_sr,net_g,vc,cpt |
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if(sid==""): |
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global hubert_model |
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print("clean_empty_cache") |
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del net_g, n_spk, vc, hubert_model,tgt_sr |
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hubert_model = net_g=n_spk=vc=hubert_model=tgt_sr=None |
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torch.cuda.empty_cache() |
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|
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if_f0 = cpt.get("f0", 1) |
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if (if_f0 == 1): |
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net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half) |
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else: |
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net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) |
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del net_g,cpt |
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torch.cuda.empty_cache() |
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cpt=None |
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return {"visible": False, "__type__": "update"} |
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person = "%s/%s" % (weight_root, sid) |
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print("loading %s"%person) |
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cpt = torch.load(person, map_location="cpu") |
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tgt_sr = cpt["config"][-1] |
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cpt["config"][-3]=cpt["weight"]["emb_g.weight"].shape[0] |
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if_f0=cpt.get("f0",1) |
|
if(if_f0==1): |
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net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half) |
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else: |
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net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) |
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del net_g.enc_q |
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print(net_g.load_state_dict(cpt["weight"], strict=False)) |
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net_g.eval().to(device) |
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if (is_half):net_g = net_g.half() |
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else:net_g = net_g.float() |
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vc = VC(tgt_sr, device, is_half) |
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n_spk=cpt["config"][-3] |
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return {"visible": True,"maximum": n_spk, "__type__": "update"} |
|
|
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def change_choices():return {"choices": sorted(list(os.listdir(weight_root))), "__type__": "update"} |
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def clean():return {"value": "", "__type__": "update"} |
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def change_f0(if_f0_3,sr2): |
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if(if_f0_3=="是"):return {"visible": True, "__type__": "update"},{"visible": True, "__type__": "update"},"pretrained/f0G%s.pth"%sr2,"pretrained/f0D%s.pth"%sr2 |
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return {"visible": False, "__type__": "update"}, {"visible": False, "__type__": "update"},"pretrained/G%s.pth"%sr2,"pretrained/D%s.pth"%sr2 |
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|
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sr_dict={ |
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"32k":32000, |
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"40k":40000, |
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"48k":48000, |
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} |
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|
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def if_done(done,p): |
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while 1: |
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if(p.poll()==None):sleep(0.5) |
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else:break |
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done[0]=True |
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|
|
|
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def if_done_multi(done,ps): |
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while 1: |
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|
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|
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flag=1 |
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for p in ps: |
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if(p.poll()==None): |
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flag = 0 |
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sleep(0.5) |
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break |
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if(flag==1):break |
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done[0]=True |
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|
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def preprocess_dataset(trainset_dir,exp_dir,sr,n_p=ncpu): |
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sr=sr_dict[sr] |
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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="runtime\python.exe trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s"%(trainset_dir,sr,n_p,now_dir,exp_dir) |
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print(cmd) |
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p = Popen(cmd, shell=True) |
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|
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done=[False] |
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threading.Thread(target=if_done,args=(done,p,)).start() |
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while(1): |
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with open("%s/logs/%s/preprocess.log"%(now_dir,exp_dir),"r")as f:yield(f.read()) |
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sleep(1) |
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if(done[0]==True):break |
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with open("%s/logs/%s/preprocess.log"%(now_dir,exp_dir), "r")as f:log = f.read() |
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print(log) |
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yield log |
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|
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def extract_f0_feature(gpus,n_p,f0method,if_f0,exp_dir): |
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gpus=gpus.split("-") |
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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/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="runtime\python.exe 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) |
|
|
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done=[False] |
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threading.Thread(target=if_done,args=(done,p,)).start() |
|
while(1): |
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with open("%s/logs/%s/extract_f0_feature.log"%(now_dir,exp_dir),"r")as f:yield(f.read()) |
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sleep(1) |
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if(done[0]==True):break |
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with open("%s/logs/%s/extract_f0_feature.log"%(now_dir,exp_dir), "r")as f:log = f.read() |
|
print(log) |
|
yield log |
|
|
|
''' |
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n_part=int(sys.argv[1]) |
|
i_part=int(sys.argv[2]) |
|
i_gpu=sys.argv[3] |
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exp_dir=sys.argv[4] |
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os.environ["CUDA_VISIBLE_DEVICES"]=str(i_gpu) |
|
''' |
|
leng=len(gpus) |
|
ps=[] |
|
for idx,n_g in enumerate(gpus): |
|
cmd="runtime\python.exe 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) |
|
ps.append(p) |
|
|
|
done = [False] |
|
threading.Thread(target=if_done_multi, args=(done, ps,)).start() |
|
while (1): |
|
with open("%s/logs/%s/extract_f0_feature.log"%(now_dir,exp_dir), "r")as f:yield (f.read()) |
|
sleep(1) |
|
if (done[0] == True): break |
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with open("%s/logs/%s/extract_f0_feature.log"%(now_dir,exp_dir), "r")as f:log = f.read() |
|
print(log) |
|
yield log |
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def change_sr2(sr2,if_f0_3): |
|
if(if_f0_3=="是"):return "pretrained/f0G%s.pth"%sr2,"pretrained/f0D%s.pth"%sr2 |
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else:return "pretrained/G%s.pth"%sr2,"pretrained/D%s.pth"%sr2 |
|
|
|
def click_train(exp_dir1,sr2,if_f0_3,spk_id5,save_epoch10,total_epoch11,batch_size12,if_save_latest13,pretrained_G14,pretrained_D15,gpus16,if_cache_gpu17): |
|
|
|
exp_dir="%s/logs/%s"%(now_dir,exp_dir1) |
|
os.makedirs(exp_dir,exist_ok=True) |
|
gt_wavs_dir="%s/0_gt_wavs"%(exp_dir) |
|
co256_dir="%s/3_feature256"%(exp_dir) |
|
if(if_f0_3=="是"): |
|
f0_dir = "%s/2a_f0" % (exp_dir) |
|
f0nsf_dir="%s/2b-f0nsf"%(exp_dir) |
|
names=set([name.split(".")[0]for name in os.listdir(gt_wavs_dir)])&set([name.split(".")[0]for name in os.listdir(co256_dir)])&set([name.split(".")[0]for name in os.listdir(f0_dir)])&set([name.split(".")[0]for name in os.listdir(f0nsf_dir)]) |
|
else: |
|
names=set([name.split(".")[0]for name in os.listdir(gt_wavs_dir)])&set([name.split(".")[0]for name in os.listdir(co256_dir)]) |
|
opt=[] |
|
for name in names: |
|
if (if_f0_3 == "是"): |
|
opt.append("%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s"%(gt_wavs_dir.replace("\\","\\\\"),name,co256_dir.replace("\\","\\\\"),name,f0_dir.replace("\\","\\\\"),name,f0nsf_dir.replace("\\","\\\\"),name,spk_id5)) |
|
else: |
|
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)) |
|
print("write filelist done") |
|
|
|
|
|
cmd = "runtime\python.exe 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() |
|
return "训练结束,您可查看控制台训练日志或实验文件夹下的train.log" |
|
|
|
def train_index(exp_dir1): |
|
exp_dir="%s/logs/%s"%(now_dir,exp_dir1) |
|
os.makedirs(exp_dir,exist_ok=True) |
|
feature_dir="%s/3_feature256"%(exp_dir) |
|
if(os.path.exists(feature_dir)==False):return "请先进行特征提取!" |
|
listdir_res=list(os.listdir(feature_dir)) |
|
if(len(listdir_res)==0):return "请先进行特征提取!" |
|
npys = [] |
|
for name in sorted(listdir_res): |
|
phone = np.load("%s/%s" % (feature_dir, name)) |
|
npys.append(phone) |
|
big_npy = np.concatenate(npys, 0) |
|
np.save("%s/total_fea.npy"%exp_dir, big_npy) |
|
n_ivf = big_npy.shape[0] // 39 |
|
infos=[] |
|
infos.append("%s,%s"%(big_npy.shape,n_ivf)) |
|
yield "\n".join(infos) |
|
index = faiss.index_factory(256, "IVF%s,Flat"%n_ivf) |
|
infos.append("training") |
|
yield "\n".join(infos) |
|
index_ivf = faiss.extract_index_ivf(index) |
|
index_ivf.nprobe = int(np.power(n_ivf,0.3)) |
|
index.train(big_npy) |
|
faiss.write_index(index, '%s/trained_IVF%s_Flat_nprobe_%s.index'%(exp_dir,n_ivf,index_ivf.nprobe)) |
|
infos.append("adding") |
|
yield "\n".join(infos) |
|
index.add(big_npy) |
|
faiss.write_index(index, '%s/added_IVF%s_Flat_nprobe_%s.index'%(exp_dir,n_ivf,index_ivf.nprobe)) |
|
infos.append("成功构建索引,added_IVF%s_Flat_nprobe_%s.index"%(n_ivf,index_ivf.nprobe)) |
|
yield "\n".join(infos) |
|
|
|
def train1key(exp_dir1, sr2, if_f0_3, trainset_dir4, spk_id5, gpus6, np7, f0method8, save_epoch10, total_epoch11, batch_size12, if_save_latest13, pretrained_G14, pretrained_D15, gpus16, if_cache_gpu17): |
|
infos=[] |
|
def get_info_str(strr): |
|
infos.append(strr) |
|
return "\n".join(infos) |
|
os.makedirs("%s/logs/%s"%(now_dir,exp_dir1),exist_ok=True) |
|
|
|
open("%s/logs/%s/preprocess.log"%(now_dir,exp_dir1), "w").close() |
|
cmd="runtime\python.exe trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s"%(trainset_dir4,sr_dict[sr2],ncpu,now_dir,exp_dir1) |
|
yield get_info_str("step1:正在处理数据") |
|
yield get_info_str(cmd) |
|
p = Popen(cmd, shell=True) |
|
p.wait() |
|
with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir1), "r")as f: print(f.read()) |
|
|
|
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="runtime\python.exe 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() |
|
with open("%s/logs/%s/extract_f0_feature.log"%(now_dir,exp_dir1), "r")as f:print(f.read()) |
|
else:yield get_info_str("step2a:无需提取音高") |
|
|
|
yield get_info_str("step2b:正在提取特征") |
|
gpus=gpus16.split("-") |
|
leng=len(gpus) |
|
ps=[] |
|
for idx,n_g in enumerate(gpus): |
|
cmd="runtime\python.exe 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) |
|
ps.append(p) |
|
for p in ps:p.wait() |
|
with open("%s/logs/%s/extract_f0_feature.log"%(now_dir,exp_dir1), "r")as f:print(f.read()) |
|
|
|
yield get_info_str("step3a:正在训练模型") |
|
|
|
exp_dir="%s/logs/%s"%(now_dir,exp_dir1) |
|
gt_wavs_dir="%s/0_gt_wavs"%(exp_dir) |
|
co256_dir="%s/3_feature256"%(exp_dir) |
|
if(if_f0_3=="是"): |
|
f0_dir = "%s/2a_f0" % (exp_dir) |
|
f0nsf_dir="%s/2b-f0nsf"%(exp_dir) |
|
names=set([name.split(".")[0]for name in os.listdir(gt_wavs_dir)])&set([name.split(".")[0]for name in os.listdir(co256_dir)])&set([name.split(".")[0]for name in os.listdir(f0_dir)])&set([name.split(".")[0]for name in os.listdir(f0nsf_dir)]) |
|
else: |
|
names=set([name.split(".")[0]for name in os.listdir(gt_wavs_dir)])&set([name.split(".")[0]for name in os.listdir(co256_dir)]) |
|
opt=[] |
|
for name in names: |
|
if (if_f0_3 == "是"): |
|
opt.append("%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s"%(gt_wavs_dir.replace("\\","\\\\"),name,co256_dir.replace("\\","\\\\"),name,f0_dir.replace("\\","\\\\"),name,f0nsf_dir.replace("\\","\\\\"),name,spk_id5)) |
|
else: |
|
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 = "runtime\python.exe 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() |
|
yield get_info_str("训练结束,您可查看控制台训练日志或实验文件夹下的train.log") |
|
|
|
feature_dir="%s/3_feature256"%(exp_dir) |
|
npys = [] |
|
listdir_res=list(os.listdir(feature_dir)) |
|
for name in sorted(listdir_res): |
|
phone = np.load("%s/%s" % (feature_dir, name)) |
|
npys.append(phone) |
|
big_npy = np.concatenate(npys, 0) |
|
np.save("%s/total_fea.npy"%exp_dir, big_npy) |
|
n_ivf = big_npy.shape[0] // 39 |
|
yield get_info_str("%s,%s"%(big_npy.shape,n_ivf)) |
|
index = faiss.index_factory(256, "IVF%s,Flat"%n_ivf) |
|
yield get_info_str("training index") |
|
index_ivf = faiss.extract_index_ivf(index) |
|
index_ivf.nprobe = int(np.power(n_ivf,0.3)) |
|
index.train(big_npy) |
|
faiss.write_index(index, '%s/trained_IVF%s_Flat_nprobe_%s.index'%(exp_dir,n_ivf,index_ivf.nprobe)) |
|
yield get_info_str("adding index") |
|
index.add(big_npy) |
|
faiss.write_index(index, '%s/added_IVF%s_Flat_nprobe_%s.index'%(exp_dir,n_ivf,index_ivf.nprobe)) |
|
yield get_info_str("成功构建索引,added_IVF%s_Flat_nprobe_%s.index"%(n_ivf,index_ivf.nprobe)) |
|
yield get_info_str("全流程结束!") |
|
|
|
|
|
def change_info_(ckpt_path): |
|
if(os.path.exists(ckpt_path.replace(os.path.basename(ckpt_path),"train.log"))==False):return {"__type__": "update"},{"__type__": "update"} |
|
try: |
|
with open(ckpt_path.replace(os.path.basename(ckpt_path),"train.log"),"r")as f: |
|
info=eval(f.read().strip("\n").split("\n")[0].split("\t")[-1]) |
|
sr,f0=info["sample_rate"],info["if_f0"] |
|
return sr,str(f0) |
|
except: |
|
traceback.print_exc() |
|
return {"__type__": "update"}, {"__type__": "update"} |
|
|
|
|
|
with gr.Blocks() as app: |
|
gr.Markdown(value=""" |
|
本软件以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。<br> |
|
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。详见根目录"使用需遵守的协议-LICENSE.txt"。 |
|
""") |
|
with gr.Tabs(): |
|
with gr.TabItem("模型推理"): |
|
with gr.Row(): |
|
sid0 = gr.Dropdown(label="推理音色", choices=names) |
|
refresh_button = gr.Button("刷新音色列表", variant="primary") |
|
refresh_button.click( |
|
fn=change_choices, |
|
inputs=[], |
|
outputs=[sid0] |
|
) |
|
clean_button = gr.Button("卸载音色省显存", variant="primary") |
|
spk_item = gr.Slider(minimum=0, maximum=2333, step=1, label='请选择说话人id', value=0, visible=False, interactive=True) |
|
clean_button.click( |
|
fn=clean, |
|
inputs=[], |
|
outputs=[sid0] |
|
) |
|
sid0.change( |
|
fn=get_vc, |
|
inputs=[sid0], |
|
outputs=[spk_item], |
|
) |
|
with gr.Group(): |
|
gr.Markdown(value=""" |
|
男转女推荐+12key,女转男推荐-12key,如果音域爆炸导致音色失真也可以自己调整到合适音域。 |
|
""") |
|
with gr.Row(): |
|
with gr.Column(): |
|
vc_transform0 = gr.Number(label="变调(整数,半音数量,升八度12降八度-12)", value=0) |
|
input_audio0 = gr.Textbox(label="输入待处理音频文件路径(默认是正确格式示例)",value="E:\codes\py39\\vits_vc_gpu_train\\todo-songs\冬之花clip1.wav") |
|
f0method0=gr.Radio(label="选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比", choices=["pm","harvest"],value="pm", interactive=True) |
|
with gr.Column(): |
|
file_index1 = gr.Textbox(label="特征检索库文件路径",value="E:\codes\py39\\vits_vc_gpu_train\logs\mi-test-1key\\added_IVF677_Flat_nprobe_7.index", interactive=True) |
|
file_big_npy1 = gr.Textbox(label="特征文件路径",value="E:\codes\py39\\vits_vc_gpu_train\logs\mi-test-1key\\total_fea.npy", interactive=True) |
|
index_rate1 = gr.Slider(minimum=0, maximum=1,label='检索特征占比', value=1,interactive=True) |
|
f0_file = gr.File(label="F0曲线文件,可选,一行一个音高,代替默认F0及升降调") |
|
but0=gr.Button("转换", variant="primary") |
|
with gr.Column(): |
|
vc_output1 = gr.Textbox(label="输出信息") |
|
vc_output2 = gr.Audio(label="输出音频(右下角三个点,点了可以下载)") |
|
but0.click(vc_single, [spk_item, input_audio0, vc_transform0,f0_file,f0method0,file_index1,file_big_npy1,index_rate1], [vc_output1, vc_output2]) |
|
with gr.Group(): |
|
gr.Markdown(value=""" |
|
批量转换,输入待转换音频文件夹,或上传多个音频文件,在指定文件夹(默认opt)下输出转换的音频。 |
|
""") |
|
with gr.Row(): |
|
with gr.Column(): |
|
vc_transform1 = gr.Number(label="变调(整数,半音数量,升八度12降八度-12)", value=0) |
|
opt_input = gr.Textbox(label="指定输出文件夹",value="opt") |
|
f0method1=gr.Radio(label="选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比", choices=["pm","harvest"],value="pm", interactive=True) |
|
with gr.Column(): |
|
file_index2 = gr.Textbox(label="特征检索库文件路径",value="E:\codes\py39\\vits_vc_gpu_train\logs\mi-test-1key\\added_IVF677_Flat_nprobe_7.index", interactive=True) |
|
file_big_npy2 = gr.Textbox(label="特征文件路径",value="E:\codes\py39\\vits_vc_gpu_train\logs\mi-test-1key\\total_fea.npy", interactive=True) |
|
index_rate2 = gr.Slider(minimum=0, maximum=1,label='检索特征占比', value=1,interactive=True) |
|
with gr.Column(): |
|
dir_input = gr.Textbox(label="输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)",value="E:\codes\py39\\vits_vc_gpu_train\\todo-songs") |
|
inputs = gr.File(file_count="multiple", label="也可批量输入音频文件,二选一,优先读文件夹") |
|
but1=gr.Button("转换", variant="primary") |
|
vc_output3 = gr.Textbox(label="输出信息") |
|
but1.click(vc_multi, [spk_item, dir_input,opt_input,inputs, vc_transform1,f0method1,file_index2,file_big_npy2,index_rate2], [vc_output3]) |
|
with gr.TabItem("伴奏人声分离"): |
|
with gr.Group(): |
|
gr.Markdown(value=""" |
|
人声伴奏分离批量处理,使用UVR5模型。<br> |
|
不带和声用HP2,带和声且提取的人声不需要和声用HP5<br> |
|
合格的文件夹路径格式举例:E:\codes\py39\\vits_vc_gpu\白鹭霜华测试样例(去文件管理器地址栏拷就行了) |
|
""") |
|
with gr.Row(): |
|
with gr.Column(): |
|
dir_wav_input = gr.Textbox(label="输入待处理音频文件夹路径",value="E:\codes\py39\\vits_vc_gpu_train\\todo-songs") |
|
wav_inputs = gr.File(file_count="multiple", label="也可批量输入音频文件,二选一,优先读文件夹") |
|
with gr.Column(): |
|
model_choose = gr.Dropdown(label="模型", choices=uvr5_names) |
|
opt_vocal_root = gr.Textbox(label="指定输出人声文件夹",value="opt") |
|
opt_ins_root = gr.Textbox(label="指定输出乐器文件夹",value="opt") |
|
but2=gr.Button("转换", variant="primary") |
|
vc_output4 = gr.Textbox(label="输出信息") |
|
but2.click(uvr, [model_choose, dir_wav_input,opt_vocal_root,wav_inputs,opt_ins_root], [vc_output4]) |
|
with gr.TabItem("训练"): |
|
gr.Markdown(value=""" |
|
step1:填写实验配置。实验数据放在logs下,每个实验一个文件夹,需手工输入实验名路径,内含实验配置,日志,训练得到的模型文件。 |
|
""") |
|
with gr.Row(): |
|
exp_dir1 = gr.Textbox(label="输入实验名",value="xxxx") |
|
sr2 = gr.Radio(label="目标采样率", choices=["32k","40k","48k"],value="40k", interactive=True) |
|
if_f0_3 = gr.Radio(label="模型是否带音高指导(唱歌一定要,语音可以不要)", choices=["是","否"],value="是", interactive=True) |
|
with gr.Group(): |
|
gr.Markdown(value=""" |
|
step2a:自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化,在实验目录下生成2个wav文件夹;暂时只支持单人训练。 |
|
""") |
|
with gr.Row(): |
|
trainset_dir4 = gr.Textbox(label="输入训练文件夹路径",value="E:\\xxx\\xxxxx") |
|
spk_id5 = gr.Slider(minimum=0, maximum=4, step=1, label='请指定说话人id', value=0,interactive=True) |
|
but1=gr.Button("处理数据", variant="primary") |
|
info1=gr.Textbox(label="输出信息",value="") |
|
but1.click(preprocess_dataset,[trainset_dir4,exp_dir1,sr2],[info1]) |
|
with gr.Group(): |
|
gr.Markdown(value=""" |
|
step2b:使用CPU提取音高(如果模型带音高),使用GPU提取特征(选择卡号) |
|
""") |
|
with gr.Row(): |
|
with gr.Column(): |
|
gpus6 = gr.Textbox(label="以-分隔输入使用的卡号,例如 0-1-2 使用卡0和卡1和卡2",value=gpus,interactive=True) |
|
gpu_info9 = gr.Textbox(label="显卡信息",value=gpu_info) |
|
with gr.Column(): |
|
np7 = gr.Slider(minimum=0, maximum=ncpu, step=1, label='提取音高使用的CPU进程数', value=ncpu,interactive=True) |
|
f0method8 = gr.Radio(label="选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢", choices=["pm", "harvest","dio"], value="harvest", interactive=True) |
|
but2=gr.Button("特征提取", variant="primary") |
|
info2=gr.Textbox(label="输出信息",value="",max_lines=8) |
|
but2.click(extract_f0_feature,[gpus6,np7,f0method8,if_f0_3,exp_dir1],[info2]) |
|
with gr.Group(): |
|
gr.Markdown(value=""" |
|
step3:填写训练设置,开始训练模型和索引 |
|
""") |
|
with gr.Row(): |
|
save_epoch10 = gr.Slider(minimum=0, maximum=50, step=1, label='保存频率save_every_epoch', value=5,interactive=True) |
|
total_epoch11 = gr.Slider(minimum=0, maximum=100, step=1, label='总训练轮数total_epoch', value=10,interactive=True) |
|
batch_size12 = gr.Slider(minimum=0, maximum=32, step=1, label='batch_size', value=4,interactive=True) |
|
if_save_latest13 = gr.Radio(label="是否仅保存最新的ckpt文件以节省硬盘空间", choices=["是", "否"], value="否", interactive=True) |
|
if_cache_gpu17 = gr.Radio(label="是否缓存所有训练集至显存。10min以下小数据可缓存以加速训练,大数据缓存会炸显存也加不了多少速", choices=["是", "否"], value="否", interactive=True) |
|
with gr.Row(): |
|
pretrained_G14 = gr.Textbox(label="加载预训练底模G路径", value="pretrained/f0G40k.pth",interactive=True) |
|
pretrained_D15 = gr.Textbox(label="加载预训练底模D路径", value="pretrained/f0D40k.pth",interactive=True) |
|
sr2.change(change_sr2, [sr2,if_f0_3], [pretrained_G14,pretrained_D15]) |
|
if_f0_3.change(change_f0, [if_f0_3, sr2], [np7, f0method8, pretrained_G14, pretrained_D15]) |
|
gpus16 = gr.Textbox(label="以-分隔输入使用的卡号,例如 0-1-2 使用卡0和卡1和卡2", value=gpus,interactive=True) |
|
but3 = gr.Button("训练模型", variant="primary") |
|
but4 = gr.Button("训练特征索引", variant="primary") |
|
but5 = gr.Button("一键训练", variant="primary") |
|
info3 = gr.Textbox(label="输出信息", value="",max_lines=10) |
|
but3.click(click_train,[exp_dir1,sr2,if_f0_3,spk_id5,save_epoch10,total_epoch11,batch_size12,if_save_latest13,pretrained_G14,pretrained_D15,gpus16,if_cache_gpu17],info3) |
|
but4.click(train_index,[exp_dir1],info3) |
|
but5.click(train1key,[exp_dir1,sr2,if_f0_3,trainset_dir4,spk_id5,gpus6,np7,f0method8,save_epoch10,total_epoch11,batch_size12,if_save_latest13,pretrained_G14,pretrained_D15,gpus16,if_cache_gpu17],info3) |
|
|
|
with gr.TabItem("ckpt处理"): |
|
with gr.Group(): |
|
gr.Markdown(value="""模型融合,可用于测试音色融合""") |
|
with gr.Row(): |
|
ckpt_a = gr.Textbox(label="A模型路径", value="", interactive=True) |
|
ckpt_b = gr.Textbox(label="B模型路径", value="", interactive=True) |
|
alpha_a = gr.Slider(minimum=0, maximum=1, label='A模型权重', value=0.5, interactive=True) |
|
with gr.Row(): |
|
sr_ = gr.Radio(label="目标采样率", choices=["32k","40k","48k"],value="40k", interactive=True) |
|
if_f0_ = gr.Radio(label="模型是否带音高指导", choices=["是","否"],value="是", interactive=True) |
|
info__ = gr.Textbox(label="要置入的模型信息", value="", max_lines=8, interactive=True) |
|
name_to_save0=gr.Textbox(label="保存的模型名不带后缀", value="", max_lines=1, interactive=True) |
|
with gr.Row(): |
|
but6 = gr.Button("融合", variant="primary") |
|
info4 = gr.Textbox(label="输出信息", value="", max_lines=8) |
|
but6.click(merge, [ckpt_a,ckpt_b,alpha_a,sr_,if_f0_,info__,name_to_save0], info4) |
|
with gr.Group(): |
|
gr.Markdown(value="修改模型信息(仅支持weights文件夹下提取的小模型文件)") |
|
with gr.Row(): |
|
ckpt_path0 = gr.Textbox(label="模型路径", value="", interactive=True) |
|
info_=gr.Textbox(label="要改的模型信息", value="", max_lines=8, interactive=True) |
|
name_to_save1=gr.Textbox(label="保存的文件名,默认空为和源文件同名", value="", max_lines=8, interactive=True) |
|
with gr.Row(): |
|
but7 = gr.Button("修改", variant="primary") |
|
info5 = gr.Textbox(label="输出信息", value="", max_lines=8) |
|
but7.click(change_info, [ckpt_path0,info_,name_to_save1], info5) |
|
with gr.Group(): |
|
gr.Markdown(value="查看模型信息(仅支持weights文件夹下提取的小模型文件)") |
|
with gr.Row(): |
|
ckpt_path1 = gr.Textbox(label="模型路径", value="", interactive=True) |
|
but8 = gr.Button("查看", variant="primary") |
|
info6 = gr.Textbox(label="输出信息", value="", max_lines=8) |
|
but8.click(show_info, [ckpt_path1], info6) |
|
with gr.Group(): |
|
gr.Markdown(value="模型提取(输入logs文件夹下大文件模型路径),适用于训一半不想训了模型没有自动提取保存小文件模型,或者想测试中间模型的情况") |
|
with gr.Row(): |
|
ckpt_path2 = gr.Textbox(label="模型路径", value="E:\codes\py39\logs\mi-test_f0_48k\\G_23333.pth", interactive=True) |
|
save_name = gr.Textbox(label="保存名", value="", interactive=True) |
|
sr__ = gr.Radio(label="目标采样率", choices=["32k","40k","48k"],value="40k", interactive=True) |
|
if_f0__ = gr.Radio(label="模型是否带音高指导,1是0否", choices=["1","0"],value="1", interactive=True) |
|
info___ = gr.Textbox(label="要置入的模型信息", value="", max_lines=8, interactive=True) |
|
but9 = gr.Button("提取", variant="primary") |
|
info7 = gr.Textbox(label="输出信息", value="", max_lines=8) |
|
ckpt_path2.change(change_info_,[ckpt_path2],[sr__,if_f0__]) |
|
but9.click(extract_small_model, [ckpt_path2,save_name,sr__,if_f0__,info___], info7) |
|
|
|
with gr.TabItem("招募音高曲线前端编辑器"): |
|
gr.Markdown(value="""加开发群联系我647947694""") |
|
with gr.TabItem("招募实时变声插件开发"): |
|
gr.Markdown(value="""加开发群联系我647947694""") |
|
with gr.TabItem("点击查看交流、问题反馈群号"): |
|
gr.Markdown(value="""259421308""") |
|
|
|
|
|
app.queue(concurrency_count=511, max_size=1022).launch(server_name="127.0.0.1",inbrowser=True,server_port=7865,quiet=True,share=True) |