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EasierGUI.py

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+import subprocess, torch, os, traceback, sys, warnings, shutil, numpy as np
+from mega import Mega
+os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1"
+import threading
+from time import sleep
+from subprocess import Popen
+import faiss
+from random import shuffle
+import json, datetime, requests
+from gtts import gTTS
+now_dir = os.getcwd()
+sys.path.append(now_dir)
+tmp = os.path.join(now_dir, "TEMP")
+shutil.rmtree(tmp, ignore_errors=True)
+shutil.rmtree("%s/runtime/Lib/site-packages/infer_pack" % (now_dir), ignore_errors=True)
+os.makedirs(tmp, exist_ok=True)
+os.makedirs(os.path.join(now_dir, "logs"), exist_ok=True)
+os.makedirs(os.path.join(now_dir, "weights"), exist_ok=True)
+os.environ["TEMP"] = tmp
+warnings.filterwarnings("ignore")
+torch.manual_seed(114514)
+from i18n import I18nAuto
+
+import signal
+
+import math
+
+from my_utils import load_audio, CSVutil
+
+global DoFormant, Quefrency, Timbre
+
+if not os.path.isdir('csvdb/'):
+ os.makedirs('csvdb')
+ frmnt, stp = open("csvdb/formanting.csv", 'w'), open("csvdb/stop.csv", 'w')
+ frmnt.close()
+ stp.close()
+
+try:
+ DoFormant, Quefrency, Timbre = CSVutil('csvdb/formanting.csv', 'r', 'formanting')
+ DoFormant = (
+ lambda DoFormant: True if DoFormant.lower() == 'true' else (False if DoFormant.lower() == 'false' else DoFormant)
+ )(DoFormant)
+except (ValueError, TypeError, IndexError):
+ DoFormant, Quefrency, Timbre = False, 1.0, 1.0
+ CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, Quefrency, Timbre)
+
+#from MDXNet import MDXNetDereverb
+
+# Check if we're in a Google Colab environment
+if os.path.exists('/content/'):
+ print("\n-------------------------------\nRVC v2 Easy GUI (Colab Edition)\n-------------------------------\n")
+
+ print("-------------------------------")
+ # Check if the file exists at the specified path
+ if os.path.exists('/content/Retrieval-based-Voice-Conversion-WebUI/hubert_base.pt'):
+ # If the file exists, print a statement saying so
+ print("File /content/Retrieval-based-Voice-Conversion-WebUI/hubert_base.pt already exists. No need to download.")
+ else:
+ # If the file doesn't exist, print a statement saying it's downloading
+ print("File /content/Retrieval-based-Voice-Conversion-WebUI/hubert_base.pt does not exist. Starting download.")
+
+ # Make a request to the URL
+ response = requests.get('https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt')
+
+ # Ensure the request was successful
+ if response.status_code == 200:
+ # If the response was a success, save the content to the specified file path
+ with open('/content/Retrieval-based-Voice-Conversion-WebUI/hubert_base.pt', 'wb') as f:
+ f.write(response.content)
+ print("Download complete. File saved to /content/Retrieval-based-Voice-Conversion-WebUI/hubert_base.pt.")
+ else:
+ # If the response was a failure, print an error message
+ print("Failed to download file. Status code: " + str(response.status_code) + ".")
+else:
+ print("\n-------------------------------\nRVC v2 Easy GUI (Local Edition)\n-------------------------------\n")
+ print("-------------------------------\nNot running on Google Colab, skipping download.")
+
+def formant_apply(qfrency, tmbre):
+ Quefrency = qfrency
+ Timbre = tmbre
+ DoFormant = True
+ CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, qfrency, tmbre)
+ 
+ return ({"value": Quefrency, "__type__": "update"}, {"value": Timbre, "__type__": "update"})
+
+def get_fshift_presets():
+ fshift_presets_list = []
+ for dirpath, _, filenames in os.walk("./formantshiftcfg/"):
+ for filename in filenames:
+ if filename.endswith(".txt"):
+ fshift_presets_list.append(os.path.join(dirpath,filename).replace('\\','/'))
+ 
+ if len(fshift_presets_list) > 0:
+ return fshift_presets_list
+ else:
+ return ''
+
+
+
+def formant_enabled(cbox, qfrency, tmbre, frmntapply, formantpreset, formant_refresh_button):
+ 
+ if (cbox):
+
+ DoFormant = True
+ CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, qfrency, tmbre)
+ #print(f"is checked? - {cbox}\ngot {DoFormant}")
+ 
+ return (
+ {"value": True, "__type__": "update"},
+ {"visible": True, "__type__": "update"},
+ {"visible": True, "__type__": "update"},
+ {"visible": True, "__type__": "update"},
+ {"visible": True, "__type__": "update"},
+ {"visible": True, "__type__": "update"},
+ )
+ 
+ 
+ else:
+ 
+ DoFormant = False
+ CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, qfrency, tmbre)
+ 
+ #print(f"is checked? - {cbox}\ngot {DoFormant}")
+ return (
+ {"value": False, "__type__": "update"},
+ {"visible": False, "__type__": "update"},
+ {"visible": False, "__type__": "update"},
+ {"visible": False, "__type__": "update"},
+ {"visible": False, "__type__": "update"},
+ {"visible": False, "__type__": "update"},
+ {"visible": False, "__type__": "update"},
+ )
+ 
+
+
+def preset_apply(preset, qfer, tmbr):
+ if str(preset) != '':
+ with open(str(preset), 'r') as p:
+ content = p.readlines()
+ qfer, tmbr = content[0].split('\n')[0], content[1]
+ 
+ formant_apply(qfer, tmbr)
+ else:
+ pass
+ return ({"value": qfer, "__type__": "update"}, {"value": tmbr, "__type__": "update"})
+
+def update_fshift_presets(preset, qfrency, tmbre):
+ 
+ qfrency, tmbre = preset_apply(preset, qfrency, tmbre)
+ 
+ if (str(preset) != ''):
+ with open(str(preset), 'r') as p:
+ content = p.readlines()
+ qfrency, tmbre = content[0].split('\n')[0], content[1]
+ 
+ formant_apply(qfrency, tmbre)
+ else:
+ pass
+ return (
+ {"choices": get_fshift_presets(), "__type__": "update"},
+ {"value": qfrency, "__type__": "update"},
+ {"value": tmbre, "__type__": "update"},
+ )
+
+i18n = I18nAuto()
+#i18n.print()
+# 判断是否有能用来训练和加速推理的N卡
+ngpu = torch.cuda.device_count()
+gpu_infos = []
+mem = []
+if (not torch.cuda.is_available()) or ngpu == 0:
+ if_gpu_ok = False
+else:
+ if_gpu_ok = False
+ for i in range(ngpu):
+ gpu_name = torch.cuda.get_device_name(i)
+ if (
+ "10" in gpu_name
+ or "16" in gpu_name
+ or "20" in gpu_name
+ or "30" in gpu_name
+ or "40" in gpu_name
+ or "A2" in gpu_name.upper()
+ or "A3" in gpu_name.upper()
+ or "A4" in gpu_name.upper()
+ or "P4" in gpu_name.upper()
+ or "A50" in gpu_name.upper()
+ or "A60" in gpu_name.upper()
+ or "70" in gpu_name
+ or "80" in gpu_name
+ or "90" in gpu_name
+ or "M4" in gpu_name.upper()
+ or "T4" in gpu_name.upper()
+ or "TITAN" in gpu_name.upper()
+ ): # A10#A100#V100#A40#P40#M40#K80#A4500
+ if_gpu_ok = True # 至少有一张能用的N卡
+ gpu_infos.append("%s\t%s" % (i, gpu_name))
+ mem.append(
+ int(
+ torch.cuda.get_device_properties(i).total_memory
+ / 1024
+ / 1024
+ / 1024
+ + 0.4
+ )
+ )
+if if_gpu_ok == True and len(gpu_infos) > 0:
+ gpu_info = "\n".join(gpu_infos)
+ default_batch_size = min(mem) // 2
+else:
+ gpu_info = i18n("很遗憾您这没有能用的显卡来支持您训练")
+ default_batch_size = 1
+gpus = "-".join([i[0] for i in gpu_infos])
+from lib.infer_pack.models import (
+ SynthesizerTrnMs256NSFsid,
+ SynthesizerTrnMs256NSFsid_nono,
+ SynthesizerTrnMs768NSFsid,
+ SynthesizerTrnMs768NSFsid_nono,
+)
+import soundfile as sf
+from fairseq import checkpoint_utils
+import gradio as gr
+import logging
+from vc_infer_pipeline import VC
+from config import Config
+
+config = Config()
+# from trainset_preprocess_pipeline import PreProcess
+logging.getLogger("numba").setLevel(logging.WARNING)
+
+hubert_model = None
+
+def load_hubert():
+ global hubert_model
+ models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
+ ["hubert_base.pt"],
+ suffix="",
+ )
+ hubert_model = models[0]
+ hubert_model = hubert_model.to(config.device)
+ if config.is_half:
+ hubert_model = hubert_model.half()
+ else:
+ hubert_model = hubert_model.float()
+ hubert_model.eval()
+
+
+weight_root = "weights"
+index_root = "logs"
+names = []
+for name in os.listdir(weight_root):
+ if name.endswith(".pth"):
+ names.append(name)
+index_paths = []
+for root, dirs, files in os.walk(index_root, topdown=False):
+ for name in files:
+ if name.endswith(".index") and "trained" not in name:
+ index_paths.append("%s/%s" % (root, name))
+
+
+
+def vc_single(
+ sid,
+ input_audio_path,
+ f0_up_key,
+ f0_file,
+ f0_method,
+ file_index,
+ #file_index2,
+ # file_big_npy,
+ index_rate,
+ filter_radius,
+ resample_sr,
+ rms_mix_rate,
+ protect,
+ crepe_hop_length,
+): # spk_item, input_audio0, vc_transform0,f0_file,f0method0
+ global tgt_sr, net_g, vc, hubert_model, version
+ if input_audio_path is None:
+ return "You need to upload an audio", None
+ f0_up_key = int(f0_up_key)
+ try:
+ audio = load_audio(input_audio_path, 16000, DoFormant, Quefrency, Timbre)
+ audio_max = np.abs(audio).max() / 0.95
+ if audio_max > 1:
+ audio /= audio_max
+ times = [0, 0, 0]
+ if hubert_model == None:
+ load_hubert()
+ if_f0 = cpt.get("f0", 1)
+ file_index = (
+ (
+ file_index.strip(" ")
+ .strip('"')
+ .strip("\n")
+ .strip('"')
+ .strip(" ")
+ .replace("trained", "added")
+ )
+ ) # 防止小白写错，自动帮他替换掉
+ # file_big_npy = (
+ # file_big_npy.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
+ # )
+ audio_opt = vc.pipeline(
+ hubert_model,
+ net_g,
+ sid,
+ audio,
+ input_audio_path,
+ times,
+ f0_up_key,
+ f0_method,
+ file_index,
+ # file_big_npy,
+ index_rate,
+ if_f0,
+ filter_radius,
+ tgt_sr,
+ resample_sr,
+ rms_mix_rate,
+ version,
+ protect,
+ crepe_hop_length,
+ f0_file=f0_file,
+ )
+ if resample_sr >= 16000 and tgt_sr != resample_sr:
+ tgt_sr = resample_sr
+ index_info = (
+ "Using index:%s." % file_index
+ if os.path.exists(file_index)
+ else "Index not used."
+ )
+ return "Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss" % (
+ index_info,
+ times[0],
+ times[1],
+ times[2],
+ ), (tgt_sr, audio_opt)
+ except:
+ info = traceback.format_exc()
+ print(info)
+ return info, (None, None)
+
+
+def vc_multi(
+ sid,
+ dir_path,
+ opt_root,
+ paths,
+ f0_up_key,
+ f0_method,
+ file_index,
+ file_index2,
+ # file_big_npy,
+ index_rate,
+ filter_radius,
+ resample_sr,
+ rms_mix_rate,
+ protect,
+ format1,
+ crepe_hop_length,
+):
+ try:
+ dir_path = (
+ dir_path.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
+ ) # 防止小白拷路径头尾带了空格和"和回车
+ opt_root = opt_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
+ os.makedirs(opt_root, exist_ok=True)
+ try:
+ if dir_path != "":
+ paths = [os.path.join(dir_path, name) for name in os.listdir(dir_path)]
+ else:
+ paths = [path.name for path in paths]
+ except:
+ traceback.print_exc()
+ paths = [path.name for path in paths]
+ infos = []
+ for path in paths:
+ info, opt = vc_single(
+ sid,
+ path,
+ f0_up_key,
+ None,
+ f0_method,
+ file_index,
+ # file_big_npy,
+ index_rate,
+ filter_radius,
+ resample_sr,
+ rms_mix_rate,
+ protect,
+ crepe_hop_length
+ )
+ if "Success" in info:
+ try:
+ tgt_sr, audio_opt = opt
+ if format1 in ["wav", "flac"]:
+ sf.write(
+ "%s/%s.%s" % (opt_root, os.path.basename(path), format1),
+ audio_opt,
+ tgt_sr,
+ )
+ else:
+ path = "%s/%s.wav" % (opt_root, os.path.basename(path))
+ sf.write(
+ path,
+ audio_opt,
+ tgt_sr,
+ )
+ if os.path.exists(path):
+ os.system(
+ "ffmpeg -i %s -vn %s -q:a 2 -y"
+ % (path, path[:-4] + ".%s" % format1)
+ )
+ except:
+ info += traceback.format_exc()
+ infos.append("%s->%s" % (os.path.basename(path), info))
+ yield "\n".join(infos)
+ yield "\n".join(infos)
+ except:
+ yield traceback.format_exc()
+
+# 一个选项卡全局只能有一个音色
+def get_vc(sid):
+ global n_spk, tgt_sr, net_g, vc, cpt, version
+ if sid == "" or sid == []:
+ global hubert_model
+ if hubert_model != None: # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的
+ print("clean_empty_cache")
+ del net_g, n_spk, vc, hubert_model, tgt_sr # ,cpt
+ hubert_model = net_g = n_spk = vc = hubert_model = tgt_sr = None
+ if torch.cuda.is_available():
+ torch.cuda.empty_cache()
+ ###楼下不这么折腾清理不干净
+ if_f0 = cpt.get("f0", 1)
+ version = cpt.get("version", "v1")
+ if version == "v1":
+ if if_f0 == 1:
+ net_g = SynthesizerTrnMs256NSFsid(
+ *cpt["config"], is_half=config.is_half
+ )
+ else:
+ net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
+ elif version == "v2":
+ if if_f0 == 1:
+ net_g = SynthesizerTrnMs768NSFsid(
+ *cpt["config"], is_half=config.is_half
+ )
+ else:
+ net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
+ del net_g, cpt
+ if torch.cuda.is_available():
+ torch.cuda.empty_cache()
+ cpt = None
+ return {"visible": False, "__type__": "update"}
+ person = "%s/%s" % (weight_root, sid)
+ print("loading %s" % person)
+ cpt = torch.load(person, map_location="cpu")
+ tgt_sr = cpt["config"][-1]
+ cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
+ if_f0 = cpt.get("f0", 1)
+ version = cpt.get("version", "v1")
+ if version == "v1":
+ if if_f0 == 1:
+ net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
+ else:
+ net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
+ elif version == "v2":
+ if if_f0 == 1:
+ net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
+ else:
+ net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
+ del net_g.enc_q
+ print(net_g.load_state_dict(cpt["weight"], strict=False))
+ net_g.eval().to(config.device)
+ if config.is_half:
+ net_g = net_g.half()
+ else:
+ net_g = net_g.float()
+ vc = VC(tgt_sr, config)
+ n_spk = cpt["config"][-3]
+ return {"visible": False, "maximum": n_spk, "__type__": "update"}
+
+
+def change_choices():
+ names = []
+ for name in os.listdir(weight_root):
+ if name.endswith(".pth"):
+ names.append(name)
+ index_paths = []
+ for root, dirs, files in os.walk(index_root, topdown=False):
+ for name in files:
+ if name.endswith(".index") and "trained" not in name:
+ index_paths.append("%s/%s" % (root, name))
+ return {"choices": sorted(names), "__type__": "update"}, {
+ "choices": sorted(index_paths),
+ "__type__": "update",
+ }
+
+
+def clean():
+ return {"value": "", "__type__": "update"}
+
+
+sr_dict = {
+ "32k": 32000,
+ "40k": 40000,
+ "48k": 48000,
+}
+
+
+def if_done(done, p):
+ while 1:
+ if p.poll() == None:
+ sleep(0.5)
+ else:
+ break
+ done[0] = True
+
+
+def if_done_multi(done, ps):
+ while 1:
+ # poll==None代表进程未结束
+ # 只要有一个进程未结束都不停
+ flag = 1
+ for p in ps:
+ if p.poll() == None:
+ flag = 0
+ sleep(0.5)
+ break
+ if flag == 1:
+ break
+ done[0] = True
+
+
+def preprocess_dataset(trainset_dir, exp_dir, sr, n_p):
+ sr = sr_dict[sr]
+ 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 = (
+ config.python_cmd
+ + " trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s "
+ % (trainset_dir, sr, n_p, now_dir, exp_dir)
+ + str(config.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
+ done = [False]
+ threading.Thread(
+ target=if_done,
+ args=(
+ done,
+ p,
+ ),
+ ).start()
+ while 1:
+ with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f:
+ yield (f.read())
+ sleep(1)
+ if done[0] == True:
+ break
+ with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f:
+ log = f.read()
+ print(log)
+ yield log
+
+# but2.click(extract_f0,[gpus6,np7,f0method8,if_f0_3,trainset_dir4],[info2])
+def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, echl):
+ gpus = gpus.split("-")
+ os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True)
+ f = open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "w")
+ f.close()
+ if if_f0:
+ cmd = config.python_cmd + " extract_f0_print.py %s/logs/%s %s %s %s" % (
+ now_dir,
+ exp_dir,
+ n_p,
+ f0method,
+ echl,
+ )
+ 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
+ done = [False]
+ threading.Thread(
+ target=if_done,
+ args=(
+ done,
+ p,
+ ),
+ ).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
+ with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
+ log = f.read()
+ print(log)
+ yield log
+ ####对不同part分别开多进程
+ """
+ n_part=int(sys.argv[1])
+ i_part=int(sys.argv[2])
+ i_gpu=sys.argv[3]
+ exp_dir=sys.argv[4]
+ os.environ["CUDA_VISIBLE_DEVICES"]=str(i_gpu)
+ """
+ leng = len(gpus)
+ ps = []
+ for idx, n_g in enumerate(gpus):
+ cmd = (
+ config.python_cmd
+ + " extract_feature_print.py %s %s %s %s %s/logs/%s %s"
+ % (
+ config.device,
+ leng,
+ idx,
+ n_g,
+ now_dir,
+ exp_dir,
+ version19,
+ )
+ )
+ print(cmd)
+ p = Popen(
+ cmd, shell=True, cwd=now_dir
+ ) # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
+ ps.append(p)
+ ###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
+ 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
+ with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
+ log = f.read()
+ print(log)
+ yield log
+
+
+def change_sr2(sr2, if_f0_3, version19):
+ path_str = "" if version19 == "v1" else "_v2"
+ f0_str = "f0" if if_f0_3 else ""
+ if_pretrained_generator_exist = os.access("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK)
+ if_pretrained_discriminator_exist = os.access("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK)
+ if (if_pretrained_generator_exist == False):
+ print("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model")
+ if (if_pretrained_discriminator_exist == False):
+ print("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model")
+ return (
+ ("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_generator_exist else "",
+ ("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_discriminator_exist else "",
+ {"visible": True, "__type__": "update"}
+ )
+
+def change_version19(sr2, if_f0_3, version19):
+ path_str = "" if version19 == "v1" else "_v2"
+ f0_str = "f0" if if_f0_3 else ""
+ if_pretrained_generator_exist = os.access("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK)
+ if_pretrained_discriminator_exist = os.access("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK)
+ if (if_pretrained_generator_exist == False):
+ print("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model")
+ if (if_pretrained_discriminator_exist == False):
+ print("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model")
+ return (
+ ("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_generator_exist else "",
+ ("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_discriminator_exist else "",
+ )
+
+
+def change_f0(if_f0_3, sr2, version19): # f0method8,pretrained_G14,pretrained_D15
+ path_str = "" if version19 == "v1" else "_v2"
+ if_pretrained_generator_exist = os.access("pretrained%s/f0G%s.pth" % (path_str, sr2), os.F_OK)
+ if_pretrained_discriminator_exist = os.access("pretrained%s/f0D%s.pth" % (path_str, sr2), os.F_OK)
+ if (if_pretrained_generator_exist == False):
+ print("pretrained%s/f0G%s.pth" % (path_str, sr2), "not exist, will not use pretrained model")
+ if (if_pretrained_discriminator_exist == False):
+ print("pretrained%s/f0D%s.pth" % (path_str, sr2), "not exist, will not use pretrained model")
+ if if_f0_3:
+ return (
+ {"visible": True, "__type__": "update"},
+ "pretrained%s/f0G%s.pth" % (path_str, sr2) if if_pretrained_generator_exist else "",
+ "pretrained%s/f0D%s.pth" % (path_str, sr2) if if_pretrained_discriminator_exist else "",
+ )
+ return (
+ {"visible": False, "__type__": "update"},
+ ("pretrained%s/G%s.pth" % (path_str, sr2)) if if_pretrained_generator_exist else "",
+ ("pretrained%s/D%s.pth" % (path_str, sr2)) if if_pretrained_discriminator_exist else "",
+ )
+
+
+global log_interval
+
+
+def set_log_interval(exp_dir, batch_size12):
+ log_interval = 1
+
+ folder_path = os.path.join(exp_dir, "1_16k_wavs")
+
+ if os.path.exists(folder_path) and os.path.isdir(folder_path):
+ wav_files = [f for f in os.listdir(folder_path) if f.endswith(".wav")]
+ if wav_files:
+ sample_size = len(wav_files)
+ log_interval = math.ceil(sample_size / batch_size12)
+ if log_interval > 1:
+ log_interval += 1
+ return log_interval
+
+# but3.click(click_train,[exp_dir1,sr2,if_f0_3,save_epoch10,total_epoch11,batch_size12,if_save_latest13,pretrained_G14,pretrained_D15,gpus16])
+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,
+ if_save_every_weights18,
+ version19,
+):
+ CSVutil('csvdb/stop.csv', 'w+', 'formanting', False)
+ # 生成filelist
+ 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)
+ feature_dir = (
+ "%s/3_feature256" % (exp_dir)
+ if version19 == "v1"
+ else "%s/3_feature768" % (exp_dir)
+ )
+ 
+ log_interval = set_log_interval(exp_dir, batch_size12)
+ 
+ if if_f0_3:
+ 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(feature_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(feature_dir)]
+ )
+ opt = []
+ for name in names:
+ if if_f0_3:
+ opt.append(
+ "%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s"
+ % (
+ gt_wavs_dir.replace("\\", "\\\\"),
+ name,
+ feature_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,
+ feature_dir.replace("\\", "\\\\"),
+ name,
+ spk_id5,
+ )
+ )
+ fea_dim = 256 if version19 == "v1" else 768
+ if if_f0_3:
+ for _ in range(2):
+ opt.append(
+ "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
+ % (now_dir, sr2, now_dir, fea_dim, now_dir, now_dir, spk_id5)
+ )
+ else:
+ for _ in range(2):
+ opt.append(
+ "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s"
+ % (now_dir, sr2, now_dir, fea_dim, spk_id5)
+ )
+ shuffle(opt)
+ 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_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"
+ print("use gpus:", gpus16)
+ if pretrained_G14 == "":
+ print("no pretrained Generator")
+ if pretrained_D15 == "":
+ print("no pretrained Discriminator")
+ if gpus16:
+ cmd = (
+ config.python_cmd
+ + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s -li %s"
+ % (
+ exp_dir1,
+ sr2,
+ 1 if if_f0_3 else 0,
+ batch_size12,
+ gpus16,
+ total_epoch11,
+ save_epoch10,
+ ("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "",
+ ("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "",
+ 1 if if_save_latest13 == True else 0,
+ 1 if if_cache_gpu17 == True else 0,
+ 1 if if_save_every_weights18 == True else 0,
+ version19,
+ log_interval,
+ )
+ )
+ else:
+ cmd = (
+ config.python_cmd
+ + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s -li %s"
+ % (
+ exp_dir1,
+ sr2,
+ 1 if if_f0_3 else 0,
+ batch_size12,
+ total_epoch11,
+ save_epoch10,
+ ("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "\b",
+ ("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "\b",
+ 1 if if_save_latest13 == True else 0,
+ 1 if if_cache_gpu17 == True else 0,
+ 1 if if_save_every_weights18 == True else 0,
+ version19,
+ log_interval,
+ )
+ )
+ print(cmd)
+ p = Popen(cmd, shell=True, cwd=now_dir)
+ global PID
+ PID = p.pid
+ 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", {"visible": False, "__type__": "update"}, {"visible": True, "__type__": "update"})
+
+
+# but4.click(train_index, [exp_dir1], info3)
+def train_index(exp_dir1, version19):
+ exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
+ os.makedirs(exp_dir, exist_ok=True)
+ feature_dir = (
+ "%s/3_feature256" % (exp_dir)
+ if version19 == "v1"
+ else "%s/3_feature768" % (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)
+ big_npy_idx = np.arange(big_npy.shape[0])
+ np.random.shuffle(big_npy_idx)
+ big_npy = big_npy[big_npy_idx]
+ np.save("%s/total_fea.npy" % exp_dir, big_npy)
+ # n_ivf = big_npy.shape[0] // 39
+ n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
+ infos = []
+ infos.append("%s,%s" % (big_npy.shape, n_ivf))
+ yield "\n".join(infos)
+ index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf)
+ # index = faiss.index_factory(256if version19=="v1"else 768, "IVF%s,PQ128x4fs,RFlat"%n_ivf)
+ infos.append("training")
+ yield "\n".join(infos)
+ index_ivf = faiss.extract_index_ivf(index) #
+ index_ivf.nprobe = 1
+ index.train(big_npy)
+ faiss.write_index(
+ index,
+ "%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index"
+ % (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
+ )
+ # faiss.write_index(index, '%s/trained_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19))
+ infos.append("adding")
+ yield "\n".join(infos)
+ batch_size_add = 8192
+ for i in range(0, big_npy.shape[0], batch_size_add):
+ index.add(big_npy[i : i + batch_size_add])
+ faiss.write_index(
+ index,
+ "%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index"
+ % (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
+ )
+ infos.append(
+ "成功构建索引，added_IVF%s_Flat_nprobe_%s_%s_%s.index"
+ % (n_ivf, index_ivf.nprobe, exp_dir1, version19)
+ )
+ # faiss.write_index(index, '%s/added_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19))
+ # infos.append("成功构建索引，added_IVF%s_Flat_FastScan_%s.index"%(n_ivf,version19))
+ yield "\n".join(infos)
+
+
+# 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)
+def train1key(
+ exp_dir1,
+ sr2,
+ if_f0_3,
+ trainset_dir4,
+ spk_id5,
+ np7,
+ f0method8,
+ save_epoch10,
+ total_epoch11,
+ batch_size12,
+ if_save_latest13,
+ pretrained_G14,
+ pretrained_D15,
+ gpus16,
+ if_cache_gpu17,
+ if_save_every_weights18,
+ version19,
+ echl
+):
+ infos = []
+
+ def get_info_str(strr):
+ infos.append(strr)
+ return "\n".join(infos)
+
+ model_log_dir = "%s/logs/%s" % (now_dir, exp_dir1)
+ preprocess_log_path = "%s/preprocess.log" % model_log_dir
+ extract_f0_feature_log_path = "%s/extract_f0_feature.log" % model_log_dir
+ gt_wavs_dir = "%s/0_gt_wavs" % model_log_dir
+ feature_dir = (
+ "%s/3_feature256" % model_log_dir
+ if version19 == "v1"
+ else "%s/3_feature768" % model_log_dir
+ )
+
+ os.makedirs(model_log_dir, exist_ok=True)
+ #########step1:处理数据
+ open(preprocess_log_path, "w").close()
+ cmd = (
+ config.python_cmd
+ + " trainset_preprocess_pipeline_print.py %s %s %s %s "
+ % (trainset_dir4, sr_dict[sr2], np7, model_log_dir)
+ + str(config.noparallel)
+ )
+ yield get_info_str(i18n("step1:正在处理数据"))
+ yield get_info_str(cmd)
+ p = Popen(cmd, shell=True)
+ p.wait()
+ with open(preprocess_log_path, "r") as f:
+ print(f.read())
+ #########step2a:提取音高
+ open(extract_f0_feature_log_path, "w")
+ if if_f0_3:
+ yield get_info_str("step2a:正在提取音高")
+ cmd = config.python_cmd + " extract_f0_print.py %s %s %s %s" % (
+ model_log_dir,
+ np7,
+ f0method8,
+ echl
+ )
+ yield get_info_str(cmd)
+ p = Popen(cmd, shell=True, cwd=now_dir)
+ p.wait()
+ with open(extract_f0_feature_log_path, "r") as f:
+ print(f.read())
+ else:
+ yield get_info_str(i18n("step2a:无需提取音高"))
+ #######step2b:提取特征
+ yield get_info_str(i18n("step2b:正在提取���征"))
+ gpus = gpus16.split("-")
+ leng = len(gpus)
+ ps = []
+ for idx, n_g in enumerate(gpus):
+ cmd = config.python_cmd + " extract_feature_print.py %s %s %s %s %s %s" % (
+ config.device,
+ leng,
+ idx,
+ n_g,
+ model_log_dir,
+ version19,
+ )
+ 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)
+ for p in ps:
+ p.wait()
+ with open(extract_f0_feature_log_path, "r") as f:
+ print(f.read())
+ #######step3a:训练模型
+ yield get_info_str(i18n("step3a:正在训练模型"))
+ # 生成filelist
+ if if_f0_3:
+ f0_dir = "%s/2a_f0" % model_log_dir
+ f0nsf_dir = "%s/2b-f0nsf" % model_log_dir
+ names = (
+ set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)])
+ & set([name.split(".")[0] for name in os.listdir(feature_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(feature_dir)]
+ )
+ opt = []
+ for name in names:
+ if if_f0_3:
+ opt.append(
+ "%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s"
+ % (
+ gt_wavs_dir.replace("\\", "\\\\"),
+ name,
+ feature_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,
+ feature_dir.replace("\\", "\\\\"),
+ name,
+ spk_id5,
+ )
+ )
+ fea_dim = 256 if version19 == "v1" else 768
+ if if_f0_3:
+ for _ in range(2):
+ opt.append(
+ "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
+ % (now_dir, sr2, now_dir, fea_dim, now_dir, now_dir, spk_id5)
+ )
+ else:
+ for _ in range(2):
+ opt.append(
+ "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s"
+ % (now_dir, sr2, now_dir, fea_dim, spk_id5)
+ )
+ shuffle(opt)
+ with open("%s/filelist.txt" % model_log_dir, "w") as f:
+ f.write("\n".join(opt))
+ yield get_info_str("write filelist done")
+ if gpus16:
+ cmd = (
+ config.python_cmd
+ +" train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s"
+ % (
+ exp_dir1,
+ sr2,
+ 1 if if_f0_3 else 0,
+ batch_size12,
+ gpus16,
+ total_epoch11,
+ save_epoch10,
+ ("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "",
+ ("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "",
+ 1 if if_save_latest13 == True else 0,
+ 1 if if_cache_gpu17 == True else 0,
+ 1 if if_save_every_weights18 == True else 0,
+ version19,
+ )
+ )
+ else:
+ cmd = (
+ config.python_cmd
+ + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s"
+ % (
+ exp_dir1,
+ sr2,
+ 1 if if_f0_3 else 0,
+ batch_size12,
+ total_epoch11,
+ save_epoch10,
+ ("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "",
+ ("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "",
+ 1 if if_save_latest13 == True else 0,
+ 1 if if_cache_gpu17 == True else 0,
+ 1 if if_save_every_weights18 == True else 0,
+ version19,
+ )
+ )
+ yield get_info_str(cmd)
+ p = Popen(cmd, shell=True, cwd=now_dir)
+ p.wait()
+ yield get_info_str(i18n("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"))
+ #######step3b:训练索引
+ 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)
+
+ big_npy_idx = np.arange(big_npy.shape[0])
+ np.random.shuffle(big_npy_idx)
+ big_npy = big_npy[big_npy_idx]
+ np.save("%s/total_fea.npy" % model_log_dir, big_npy)
+
+ # n_ivf = big_npy.shape[0] // 39
+ n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
+ yield get_info_str("%s,%s" % (big_npy.shape, n_ivf))
+ index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf)
+ yield get_info_str("training index")
+ index_ivf = faiss.extract_index_ivf(index) #
+ index_ivf.nprobe = 1
+ index.train(big_npy)
+ faiss.write_index(
+ index,
+ "%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index"
+ % (model_log_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
+ )
+ yield get_info_str("adding index")
+ batch_size_add = 8192
+ for i in range(0, big_npy.shape[0], batch_size_add):
+ index.add(big_npy[i : i + batch_size_add])
+ faiss.write_index(
+ index,
+ "%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index"
+ % (model_log_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
+ )
+ yield get_info_str(
+ "成功构建索引, added_IVF%s_Flat_nprobe_%s_%s_%s.index"
+ % (n_ivf, index_ivf.nprobe, exp_dir1, version19)
+ )
+ yield get_info_str(i18n("全流程结束！"))
+
+
+def whethercrepeornah(radio):
+ mango = True if radio == 'mangio-crepe' or radio == 'mangio-crepe-tiny' else False
+ return ({"visible": mango, "__type__": "update"})
+
+# ckpt_path2.change(change_info_,[ckpt_path2],[sr__,if_f0__])
+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"}, {"__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"]
+ version = "v2" if ("version" in info and info["version"] == "v2") else "v1"
+ return sr, str(f0), version
+ except:
+ traceback.print_exc()
+ return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
+
+
+from lib.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM
+
+
+def export_onnx(ModelPath, ExportedPath, MoeVS=True):
+ cpt = torch.load(ModelPath, map_location="cpu")
+ cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
+ hidden_channels = 256 if cpt.get("version","v1")=="v1"else 768#cpt["config"][-2] # hidden_channels，为768Vec做准备
+
+ test_phone = torch.rand(1, 200, hidden_channels) # hidden unit
+ test_phone_lengths = torch.tensor([200]).long() # hidden unit 长度（貌似没啥用）
+ test_pitch = torch.randint(size=(1, 200), low=5, high=255) # 基频（单位赫兹）
+ test_pitchf = torch.rand(1, 200) # nsf基频
+ test_ds = torch.LongTensor([0]) # 说话人ID
+ test_rnd = torch.rand(1, 192, 200) # 噪声（加入随机因子）
+
+ device = "cpu" # 导出时设备（不影响使用模型）
+
+
+ net_g = SynthesizerTrnMsNSFsidM(
+ *cpt["config"], is_half=False,version=cpt.get("version","v1")
+ ) # fp32导出（C++要支持fp16必须手动将内存重新排列所以暂时不用fp16）
+ net_g.load_state_dict(cpt["weight"], strict=False)
+ input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"]
+ output_names = [
+ "audio",
+ ]
+ # net_g.construct_spkmixmap(n_speaker) 多角色混合轨道导出
+ torch.onnx.export(
+ net_g,
+ (
+ test_phone.to(device),
+ test_phone_lengths.to(device),
+ test_pitch.to(device),
+ test_pitchf.to(device),
+ test_ds.to(device),
+ test_rnd.to(device),
+ ),
+ ExportedPath,
+ dynamic_axes={
+ "phone": [1],
+ "pitch": [1],
+ "pitchf": [1],
+ "rnd": [2],
+ },
+ do_constant_folding=False,
+ opset_version=16,
+ verbose=False,
+ input_names=input_names,
+ output_names=output_names,
+ )
+ return "Finished"
+
+#region RVC WebUI App
+
+def get_presets():
+ data = None
+ with open('../inference-presets.json', 'r') as file:
+ data = json.load(file)
+ preset_names = []
+ for preset in data['presets']:
+ preset_names.append(preset['name'])
+ 
+ return preset_names
+
+def change_choices2():
+ audio_files=[]
+ for filename in os.listdir("./audios"):
+ if filename.endswith(('.wav','.mp3','.ogg','.flac','.m4a','.aac','.mp4')):
+ audio_files.append(os.path.join('./audios',filename).replace('\\', '/'))
+ return {"choices": sorted(audio_files), "__type__": "update"}, {"__type__": "update"}
+ 
+audio_files=[]
+for filename in os.listdir("./audios"):
+ if filename.endswith(('.wav','.mp3','.ogg','.flac','.m4a','.aac','.mp4')):
+ audio_files.append(os.path.join('./audios',filename).replace('\\', '/'))
+ 
+def get_index():
+ if check_for_name() != '':
+ chosen_model=sorted(names)[0].split(".")[0]
+ logs_path="./logs/"+chosen_model
+ if os.path.exists(logs_path):
+ for file in os.listdir(logs_path):
+ if file.endswith(".index"):
+ return os.path.join(logs_path, file)
+ return ''
+ else:
+ return ''
+ 
+def get_indexes():
+ indexes_list=[]
+ for dirpath, dirnames, filenames in os.walk("./logs/"):
+ for filename in filenames:
+ if filename.endswith(".index"):
+ indexes_list.append(os.path.join(dirpath,filename))
+ if len(indexes_list) > 0:
+ return indexes_list
+ else:
+ return ''
+ 
+def get_name():
+ if len(audio_files) > 0:
+ return sorted(audio_files)[0]
+ else:
+ return ''
+ 
+def save_to_wav(record_button):
+ if record_button is None:
+ pass
+ else:
+ path_to_file=record_button
+ new_name = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")+'.wav'
+ new_path='./audios/'+new_name
+ shutil.move(path_to_file,new_path)
+ return new_path
+ 
+def save_to_wav2(dropbox):
+ file_path=dropbox.name
+ shutil.move(file_path,'./audios')
+ return os.path.join('./audios',os.path.basename(file_path))
+ 
+def match_index(sid0):
+ folder=sid0.split(".")[0]
+ parent_dir="./logs/"+folder
+ if os.path.exists(parent_dir):
+ for filename in os.listdir(parent_dir):
+ if filename.endswith(".index"):
+ index_path=os.path.join(parent_dir,filename)
+ return index_path
+ else:
+ return ''
+ 
+def check_for_name():
+ if len(names) > 0:
+ return sorted(names)[0]
+ else:
+ return ''
+ 
+def download_from_url(url, model):
+ if url == '':
+ return "URL cannot be left empty."
+ if model =='':
+ return "You need to name your model. For example: My-Model"
+ url = url.strip()
+ zip_dirs = ["zips", "unzips"]
+ for directory in zip_dirs:
+ if os.path.exists(directory):
+ shutil.rmtree(directory)
+ os.makedirs("zips", exist_ok=True)
+ os.makedirs("unzips", exist_ok=True)
+ zipfile = model + '.zip'
+ zipfile_path = './zips/' + zipfile
+ try:
+ if "drive.google.com" in url:
+ subprocess.run(["gdown", url, "--fuzzy", "-O", zipfile_path])
+ elif "mega.nz" in url:
+ m = Mega()
+ m.download_url(url, './zips')
+ else:
+ subprocess.run(["wget", url, "-O", zipfile_path])
+ for filename in os.listdir("./zips"):
+ if filename.endswith(".zip"):
+ zipfile_path = os.path.join("./zips/",filename)
+ shutil.unpack_archive(zipfile_path, "./unzips", 'zip')
+ else:
+ return "No zipfile found."
+ for root, dirs, files in os.walk('./unzips'):
+ for file in files:
+ file_path = os.path.join(root, file)
+ if file.endswith(".index"):
+ os.mkdir(f'./logs/{model}')
+ shutil.copy2(file_path,f'./logs/{model}')
+ elif "G_" not in file and "D_" not in file and file.endswith(".pth"):
+ shutil.copy(file_path,f'./weights/{model}.pth')
+ shutil.rmtree("zips")
+ shutil.rmtree("unzips")
+ return "Success."
+ except:
+ return "There's been an error."
+def success_message(face):
+ return f'{face.name} has been uploaded.', 'None'
+def mouth(size, face, voice, faces):
+ if size == 'Half':
+ size = 2
+ else:
+ size = 1
+ if faces == 'None':
+ character = face.name
+ else:
+ if faces == 'Ben Shapiro':
+ character = '/content/wav2lip-HD/inputs/ben-shapiro-10.mp4'
+ elif faces == 'Andrew Tate':
+ character = '/content/wav2lip-HD/inputs/tate-7.mp4'
+ command = "python inference.py " \
+ "--checkpoint_path checkpoints/wav2lip.pth " \
+ f"--face {character} " \
+ f"--audio {voice} " \
+ "--pads 0 20 0 0 " \
+ "--outfile /content/wav2lip-HD/outputs/result.mp4 " \
+ "--fps 24 " \
+ f"--resize_factor {size}"
+ process = subprocess.Popen(command, shell=True, cwd='/content/wav2lip-HD/Wav2Lip-master')
+ stdout, stderr = process.communicate()
+ return '/content/wav2lip-HD/outputs/result.mp4', 'Animation completed.'
+eleven_voices = ['Adam','Antoni','Josh','Arnold','Sam','Bella','Rachel','Domi','Elli']
+eleven_voices_ids=['pNInz6obpgDQGcFmaJgB','ErXwobaYiN019PkySvjV','TxGEqnHWrfWFTfGW9XjX','VR6AewLTigWG4xSOukaG','yoZ06aMxZJJ28mfd3POQ','EXAVITQu4vr4xnSDxMaL','21m00Tcm4TlvDq8ikWAM','AZnzlk1XvdvUeBnXmlld','MF3mGyEYCl7XYWbV9V6O']
+chosen_voice = dict(zip(eleven_voices, eleven_voices_ids))
+
+def stoptraining(mim): 
+ if int(mim) == 1:
+ try:
+ CSVutil('csvdb/stop.csv', 'w+', 'stop', 'True')
+ os.kill(PID, signal.SIGTERM)
+ except Exception as e:
+ print(f"Couldn't click due to {e}")
+ return (
+ {"visible": False, "__type__": "update"}, 
+ {"visible": True, "__type__": "update"},
+ )
+
+
+def elevenTTS(xiapi, text, id, lang):
+ if xiapi!= '' and id !='': 
+ choice = chosen_voice[id]
+ CHUNK_SIZE = 1024
+ url = f"https://api.elevenlabs.io/v1/text-to-speech/{choice}"
+ headers = {
+ "Accept": "audio/mpeg",
+ "Content-Type": "application/json",
+ "xi-api-key": xiapi
+ }
+ if lang == 'en':
+ data = {
+ "text": text,
+ "model_id": "eleven_monolingual_v1",
+ "voice_settings": {
+ "stability": 0.5,
+ "similarity_boost": 0.5
+ }
+ }
+ else:
+ data = {
+ "text": text,
+ "model_id": "eleven_multilingual_v1",
+ "voice_settings": {
+ "stability": 0.5,
+ "similarity_boost": 0.5
+ }
+ }
+
+ response = requests.post(url, json=data, headers=headers)
+ with open('./temp_eleven.mp3', 'wb') as f:
+ for chunk in response.iter_content(chunk_size=CHUNK_SIZE):
+ if chunk:
+ f.write(chunk)
+ aud_path = save_to_wav('./temp_eleven.mp3')
+ return aud_path, aud_path
+ else:
+ tts = gTTS(text, lang=lang)
+ tts.save('./temp_gTTS.mp3')
+ aud_path = save_to_wav('./temp_gTTS.mp3')
+ return aud_path, aud_path
+
+def upload_to_dataset(files, dir):
+ if dir == '':
+ dir = './dataset'
+ if not os.path.exists(dir):
+ os.makedirs(dir)
+ count = 0
+ for file in files:
+ path=file.name
+ shutil.copy2(path,dir)
+ count += 1
+ return f' {count} files uploaded to {dir}.' 
+ 
+def zip_downloader(model):
+ if not os.path.exists(f'./weights/{model}.pth'):
+ return {"__type__": "update"}, f'Make sure the Voice Name is correct. I could not find {model}.pth'
+ index_found = False
+ for file in os.listdir(f'./logs/{model}'):
+ if file.endswith('.index') and 'added' in file:
+ log_file = file
+ index_found = True
+ if index_found:
+ return [f'./weights/{model}.pth', f'./logs/{model}/{log_file}'], "Done"
+ else:
+ return f'./weights/{model}.pth', "Could not find Index file."
+
+with gr.Blocks(theme=gr.themes.Base(), title='Mangio-RVC-Web 💻') as app:
+ with gr.Tabs():
+ with gr.TabItem("Inference"):
+ gr.HTML("<h1> Easy GUI v2 (rejekts) - adapted to Mangio-RVC-Fork 💻 [With extra features and fixes by kalomaze & alexlnkp]</h1>")
+
+ # Inference Preset Row
+ # with gr.Row():
+ # mangio_preset = gr.Dropdown(label="Inference Preset", choices=sorted(get_presets()))
+ # mangio_preset_name_save = gr.Textbox(
+ # label="Your preset name"
+ # )
+ # mangio_preset_save_btn = gr.Button('Save Preset', variant="primary")
+
+ # Other RVC stuff
+ with gr.Row():
+ sid0 = gr.Dropdown(label="1.Choose your Model.", choices=sorted(names), value=check_for_name())
+ refresh_button = gr.Button("Refresh", variant="primary")
+ if check_for_name() != '':
+ get_vc(sorted(names)[0])
+ vc_transform0 = gr.Number(label="Optional: You can change the pitch here or leave it at 0.", value=0)
+ #clean_button = gr.Button(i18n("卸载音色省显存"), variant="primary")
+ spk_item = gr.Slider(
+ minimum=0,
+ maximum=2333,
+ step=1,
+ label=i18n("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],
+ )
+ but0 = gr.Button("Convert", variant="primary")
+ with gr.Row():
+ with gr.Column():
+ with gr.Row():
+ dropbox = gr.File(label="Drop your audio here & hit the Reload button.")
+ with gr.Row():
+ record_button=gr.Audio(source="microphone", label="OR Record audio.", type="filepath")
+ with gr.Row():
+ input_audio0 = gr.Dropdown(
+ label="2.Choose your audio.",
+ value="./audios/someguy.mp3",
+ choices=audio_files
+ )
+ dropbox.upload(fn=save_to_wav2, inputs=[dropbox], outputs=[input_audio0])
+ dropbox.upload(fn=change_choices2, inputs=[], outputs=[input_audio0])
+ refresh_button2 = gr.Button("Refresh", variant="primary", size='sm')
+ record_button.change(fn=save_to_wav, inputs=[record_button], outputs=[input_audio0])
+ record_button.change(fn=change_choices2, inputs=[], outputs=[input_audio0])
+ with gr.Row():
+ with gr.Accordion('Text To Speech', open=False):
+ with gr.Column():
+ lang = gr.Radio(label='Chinese & Japanese do not work with ElevenLabs currently.',choices=['en','es','fr','pt','zh-CN','de','hi','ja'], value='en')
+ api_box = gr.Textbox(label="Enter your API Key for ElevenLabs, or leave empty to use GoogleTTS", value='')
+ elevenid=gr.Dropdown(label="Voice:", choices=eleven_voices)
+ with gr.Column():
+ tfs = gr.Textbox(label="Input your Text", interactive=True, value="This is a test.")
+ tts_button = gr.Button(value="Speak")
+ tts_button.click(fn=elevenTTS, inputs=[api_box,tfs, elevenid, lang], outputs=[record_button, input_audio0])
+ with gr.Row():
+ with gr.Accordion('Wav2Lip', open=False):
+ with gr.Row():
+ size = gr.Radio(label='Resolution:',choices=['Half','Full'])
+ face = gr.UploadButton("Upload A Character",type='file')
+ faces = gr.Dropdown(label="OR Choose one:", choices=['None','Ben Shapiro','Andrew Tate'])
+ with gr.Row():
+ preview = gr.Textbox(label="Status:",interactive=False)
+ face.upload(fn=success_message,inputs=[face], outputs=[preview, faces])
+ with gr.Row():
+ animation = gr.Video(type='filepath')
+ refresh_button2.click(fn=change_choices2, inputs=[], outputs=[input_audio0, animation])
+ with gr.Row():
+ animate_button = gr.Button('Animate')
+
+ with gr.Column():
+ with gr.Accordion("Index Settings", open=False):
+ file_index1 = gr.Dropdown(
+ label="3. Path to your added.index file (if it didn't automatically find it.)",
+ choices=get_indexes(),
+ value=get_index(),
+ interactive=True,
+ )
+ sid0.change(fn=match_index, inputs=[sid0],outputs=[file_index1])
+ refresh_button.click(
+ fn=change_choices, inputs=[], outputs=[sid0, file_index1]
+ )
+ # file_big_npy1 = gr.Textbox(
+ # label=i18n("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=i18n("检索特征占比"),
+ value=0.66,
+ interactive=True,
+ )
+ vc_output2 = gr.Audio(
+ label="Output Audio (Click on the Three Dots in the Right Corner to Download)",
+ type='filepath',
+ interactive=False,
+ )
+ animate_button.click(fn=mouth, inputs=[size, face, vc_output2, faces], outputs=[animation, preview])
+ with gr.Accordion("Advanced Settings", open=False):
+ f0method0 = gr.Radio(
+ label="Optional: Change the Pitch Extraction Algorithm.\nExtraction methods are sorted from 'worst quality' to 'best quality'.\nmangio-crepe may or may not be better than rmvpe in cases where 'smoothness' is more important, but rmvpe is the best overall.",
+ choices=["pm", "dio", "crepe-tiny", "mangio-crepe-tiny", "crepe", "harvest", "mangio-crepe", "rmvpe"], # Fork Feature. Add Crepe-Tiny
+ value="rmvpe",
+ interactive=True,
+ )
+ 
+ crepe_hop_length = gr.Slider(
+ minimum=1,
+ maximum=512,
+ step=1,
+ label="Mangio-Crepe Hop Length. Higher numbers will reduce the chance of extreme pitch changes but lower numbers will increase accuracy. 64-192 is a good range to experiment with.",
+ value=120,
+ interactive=True,
+ visible=False,
+ )
+ f0method0.change(fn=whethercrepeornah, inputs=[f0method0], outputs=[crepe_hop_length])
+ filter_radius0 = gr.Slider(
+ minimum=0,
+ maximum=7,
+ label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"),
+ value=3,
+ step=1,
+ interactive=True,
+ )
+ resample_sr0 = gr.Slider(
+ minimum=0,
+ maximum=48000,
+ label=i18n("后处理重采样至最终采样率，0为不进行重采样"),
+ value=0,
+ step=1,
+ interactive=True,
+ visible=False
+ )
+ rms_mix_rate0 = gr.Slider(
+ minimum=0,
+ maximum=1,
+ label=i18n("输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"),
+ value=0.21,
+ interactive=True,
+ )
+ protect0 = gr.Slider(
+ minimum=0,
+ maximum=0.5,
+ label=i18n("保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果"),
+ value=0.33,
+ step=0.01,
+ interactive=True,
+ )
+ formanting = gr.Checkbox(
+ value=bool(DoFormant),
+ label="[EXPERIMENTAL] Formant shift inference audio",
+ info="Used for male to female and vice-versa conversions",
+ interactive=True,
+ visible=True,
+ )
+ 
+ formant_preset = gr.Dropdown(
+ value='',
+ choices=get_fshift_presets(),
+ label="browse presets for formanting",
+ visible=bool(DoFormant),
+ )
+ formant_refresh_button = gr.Button(
+ value='\U0001f504',
+ visible=bool(DoFormant),
+ variant='primary',
+ )
+ #formant_refresh_button = ToolButton( elem_id='1')
+ #create_refresh_button(formant_preset, lambda: {"choices": formant_preset}, "refresh_list_shiftpresets")
+ 
+ qfrency = gr.Slider(
+ value=Quefrency,
+ info="Default value is 1.0",
+ label="Quefrency for formant shifting",
+ minimum=0.0,
+ maximum=16.0,
+ step=0.1,
+ visible=bool(DoFormant),
+ interactive=True,
+ )
+ tmbre = gr.Slider(
+ value=Timbre,
+ info="Default value is 1.0",
+ label="Timbre for formant shifting",
+ minimum=0.0,
+ maximum=16.0,
+ step=0.1,
+ visible=bool(DoFormant),
+ interactive=True,
+ )
+ 
+ formant_preset.change(fn=preset_apply, inputs=[formant_preset, qfrency, tmbre], outputs=[qfrency, tmbre])
+ frmntbut = gr.Button("Apply", variant="primary", visible=bool(DoFormant))
+ formanting.change(fn=formant_enabled,inputs=[formanting,qfrency,tmbre,frmntbut,formant_preset,formant_refresh_button],outputs=[formanting,qfrency,tmbre,frmntbut,formant_preset,formant_refresh_button])
+ frmntbut.click(fn=formant_apply,inputs=[qfrency, tmbre], outputs=[qfrency, tmbre])
+ formant_refresh_button.click(fn=update_fshift_presets,inputs=[formant_preset, qfrency, tmbre],outputs=[formant_preset, qfrency, tmbre])
+ with gr.Row():
+ vc_output1 = gr.Textbox("")
+ f0_file = gr.File(label=i18n("F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调"), visible=False)
+ 
+ but0.click(
+ vc_single,
+ [
+ spk_item,
+ input_audio0,
+ vc_transform0,
+ f0_file,
+ f0method0,
+ file_index1,
+ # file_index2,
+ # file_big_npy1,
+ index_rate1,
+ filter_radius0,
+ resample_sr0,
+ rms_mix_rate0,
+ protect0,
+ crepe_hop_length
+ ],
+ [vc_output1, vc_output2],
+ )
+ 
+ with gr.Accordion("Batch Conversion",open=False):
+ with gr.Row():
+ with gr.Column():
+ vc_transform1 = gr.Number(
+ label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0
+ )
+ opt_input = gr.Textbox(label=i18n("指定输出文件夹"), value="opt")
+ f0method1 = gr.Radio(
+ label=i18n(
+ "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,crepe效果好但吃GPU"
+ ),
+ choices=["pm", "harvest", "crepe", "rmvpe"],
+ value="rmvpe",
+ interactive=True,
+ )
+ filter_radius1 = gr.Slider(
+ minimum=0,
+ maximum=7,
+ label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"),
+ value=3,
+ step=1,
+ interactive=True,
+ )
+ with gr.Column():
+ file_index3 = gr.Textbox(
+ label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"),
+ value="",
+ interactive=True,
+ )
+ file_index4 = gr.Dropdown(
+ label=i18n("自动检测index路径,下拉式选择(dropdown)"),
+ choices=sorted(index_paths),
+ interactive=True,
+ )
+ refresh_button.click(
+ fn=lambda: change_choices()[1],
+ inputs=[],
+ outputs=file_index4,
+ )
+ # file_big_npy2 = gr.Textbox(
+ # label=i18n("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=i18n("检索特征占比"),
+ value=1,
+ interactive=True,
+ )
+ with gr.Column():
+ resample_sr1 = gr.Slider(
+ minimum=0,
+ maximum=48000,
+ label=i18n("后处理重采样至最终采样率，0为不进行重采样"),
+ value=0,
+ step=1,
+ interactive=True,
+ )
+ rms_mix_rate1 = gr.Slider(
+ minimum=0,
+ maximum=1,
+ label=i18n("输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"),
+ value=1,
+ interactive=True,
+ )
+ protect1 = gr.Slider(
+ minimum=0,
+ maximum=0.5,
+ label=i18n(
+ "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果"
+ ),
+ value=0.33,
+ step=0.01,
+ interactive=True,
+ )
+ with gr.Column():
+ dir_input = gr.Textbox(
+ label=i18n("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\\test-20230416b\\todo-songs",
+ )
+ inputs = gr.File(
+ file_count="multiple", label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹")
+ )
+ with gr.Row():
+ format1 = gr.Radio(
+ label=i18n("导出文件格式"),
+ choices=["wav", "flac", "mp3", "m4a"],
+ value="flac",
+ interactive=True,
+ )
+ but1 = gr.Button(i18n("转换"), variant="primary")
+ vc_output3 = gr.Textbox(label=i18n("输出信息"))
+ but1.click(
+ vc_multi,
+ [
+ spk_item,
+ dir_input,
+ opt_input,
+ inputs,
+ vc_transform1,
+ f0method1,
+ file_index3,
+ file_index4,
+ # file_big_npy2,
+ index_rate2,
+ filter_radius1,
+ resample_sr1,
+ rms_mix_rate1,
+ protect1,
+ format1,
+ crepe_hop_length,
+ ],
+ [vc_output3],
+ )
+ but1.click(fn=lambda: easy_uploader.clear())
+ with gr.TabItem("Download Model"):
+ with gr.Row():
+ url=gr.Textbox(label="Enter the URL to the Model:")
+ with gr.Row():
+ model = gr.Textbox(label="Name your model:")
+ download_button=gr.Button("Download")
+ with gr.Row():
+ status_bar=gr.Textbox(label="")
+ download_button.click(fn=download_from_url, inputs=[url, model], outputs=[status_bar])
+ with gr.Row():
+ gr.Markdown(
+ """
+ Original RVC:https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI
+ Mangio's RVC Fork:https://github.com/Mangio621/Mangio-RVC-Fork
+ ❤️ If you like the EasyGUI, help me keep it.❤️ 
+ https://paypal.me/lesantillan
+ """
+ )
+ 
+ def has_two_files_in_pretrained_folder():
+ pretrained_folder = "./pretrained/"
+ if not os.path.exists(pretrained_folder):
+ return False
+
+ files_in_folder = os.listdir(pretrained_folder)
+ num_files = len(files_in_folder)
+ return num_files >= 2
+
+ if has_two_files_in_pretrained_folder(): 
+ print("Pretrained weights are downloaded. Training tab enabled!\n-------------------------------") 
+ with gr.TabItem("Train", visible=False):
+ with gr.Row():
+ with gr.Column():
+ exp_dir1 = gr.Textbox(label="Voice Name:", value="My-Voice")
+ sr2 = gr.Radio(
+ label=i18n("目标采样率"),
+ choices=["40k", "48k"],
+ value="40k",
+ interactive=True,
+ visible=False
+ )
+ if_f0_3 = gr.Radio(
+ label=i18n("模型是否带音高指导(唱歌一定要, 语音可以不要)"),
+ choices=[True, False],
+ value=True,
+ interactive=True,
+ visible=False
+ )
+ version19 = gr.Radio(
+ label="RVC version",
+ choices=["v1", "v2"],
+ value="v2",
+ interactive=True,
+ visible=False,
+ )
+ np7 = gr.Slider(
+ minimum=0,
+ maximum=config.n_cpu,
+ step=1,
+ label="# of CPUs for data processing (Leave as it is)",
+ value=config.n_cpu,
+ interactive=True,
+ visible=True
+ )
+ trainset_dir4 = gr.Textbox(label="Path to your dataset (audios, not zip):", value="./dataset")
+ easy_uploader = gr.Files(label='OR Drop your audios here. They will be uploaded in your dataset path above.',file_types=['audio'])
+ but1 = gr.Button("1. Process The Dataset", variant="primary")
+ info1 = gr.Textbox(label="Status (wait until it says 'end preprocess'):", value="")
+ easy_uploader.upload(fn=upload_to_dataset, inputs=[easy_uploader, trainset_dir4], outputs=[info1])
+ but1.click(
+ preprocess_dataset, [trainset_dir4, exp_dir1, sr2, np7], [info1]
+ )
+ with gr.Column():
+ spk_id5 = gr.Slider(
+ minimum=0,
+ maximum=4,
+ step=1,
+ label=i18n("请指定说话人id"),
+ value=0,
+ interactive=True,
+ visible=False
+ )
+ with gr.Accordion('GPU Settings', open=False, visible=False):
+ gpus6 = gr.Textbox(
+ label=i18n("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,
+ visible=False
+ )
+ gpu_info9 = gr.Textbox(label=i18n("显卡信息"), value=gpu_info)
+ f0method8 = gr.Radio(
+ label=i18n(
+ "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=["harvest","crepe", "mangio-crepe", "rmvpe"], # Fork feature: Crepe on f0 extraction for training.
+ value="rmvpe",
+ interactive=True,
+ )
+ 
+ extraction_crepe_hop_length = gr.Slider(
+ minimum=1,
+ maximum=512,
+ step=1,
+ label=i18n("crepe_hop_length"),
+ value=128,
+ interactive=True,
+ visible=False,
+ )
+ f0method8.change(fn=whethercrepeornah, inputs=[f0method8], outputs=[extraction_crepe_hop_length])
+ but2 = gr.Button("2. Pitch Extraction", variant="primary")
+ info2 = gr.Textbox(label="Status(Check the Colab Notebook's cell output):", value="", max_lines=8)
+ but2.click(
+ extract_f0_feature,
+ [gpus6, np7, f0method8, if_f0_3, exp_dir1, version19, extraction_crepe_hop_length],
+ [info2],
+ )
+ with gr.Row(): 
+ with gr.Column():
+ total_epoch11 = gr.Slider(
+ minimum=1,
+ maximum=5000,
+ step=10,
+ label="Total # of training epochs (IF you choose a value too high, your model will sound horribly overtrained.):",
+ value=250,
+ interactive=True,
+ )
+ butstop = gr.Button(
+ "Stop Training",
+ variant='primary',
+ visible=False,
+ )
+ but3 = gr.Button("3. Train Model", variant="primary", visible=True)
+ 
+ but3.click(fn=stoptraining, inputs=[gr.Number(value=0, visible=False)], outputs=[but3, butstop])
+ butstop.click(fn=stoptraining, inputs=[gr.Number(value=1, visible=False)], outputs=[butstop, but3])
+ 
+ 
+ but4 = gr.Button("4.Train Index", variant="primary")
+ info3 = gr.Textbox(label="Status(Check the Colab Notebook's cell output):", value="", max_lines=10)
+ with gr.Accordion("Training Preferences (You can leave these as they are)", open=False):
+ #gr.Markdown(value=i18n("step3: 填写训练设置, 开始训练模型和索引"))
+ with gr.Column():
+ save_epoch10 = gr.Slider(
+ minimum=1,
+ maximum=200,
+ step=1,
+ label="Backup every X amount of epochs:",
+ value=10,
+ interactive=True,
+ )
+ batch_size12 = gr.Slider(
+ minimum=1,
+ maximum=40,
+ step=1,
+ label="Batch Size (LEAVE IT unless you know what you're doing!):",
+ value=default_batch_size,
+ interactive=True,
+ )
+ if_save_latest13 = gr.Checkbox(
+ label="Save only the latest '.ckpt' file to save disk space.",
+ value=True,
+ interactive=True,
+ )
+ if_cache_gpu17 = gr.Checkbox(
+ label="Cache all training sets to GPU memory. Caching small datasets (less than 10 minutes) can speed up training, but caching large datasets will consume a lot of GPU memory and may not provide much speed improvement.",
+ value=False,
+ interactive=True,
+ )
+ if_save_every_weights18 = gr.Checkbox(
+ label="Save a small final model to the 'weights' folder at each save point.",
+ value=True,
+ interactive=True,
+ )
+ zip_model = gr.Button('5. Download Model')
+ zipped_model = gr.Files(label='Your Model and Index file can be downloaded here:')
+ zip_model.click(fn=zip_downloader, inputs=[exp_dir1], outputs=[zipped_model, info3])
+ with gr.Group():
+ with gr.Accordion("Base Model Locations:", open=False, visible=False):
+ pretrained_G14 = gr.Textbox(
+ label=i18n("加载预训练底模G路径"),
+ value="pretrained_v2/f0G40k.pth",
+ interactive=True,
+ )
+ pretrained_D15 = gr.Textbox(
+ label=i18n("加载预训练底模D路径"),
+ value="pretrained_v2/f0D40k.pth",
+ interactive=True,
+ )
+ gpus16 = gr.Textbox(
+ label=i18n("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,
+ )
+ sr2.change(
+ change_sr2,
+ [sr2, if_f0_3, version19],
+ [pretrained_G14, pretrained_D15, version19],
+ )
+ version19.change(
+ change_version19,
+ [sr2, if_f0_3, version19],
+ [pretrained_G14, pretrained_D15],
+ )
+ if_f0_3.change(
+ change_f0,
+ [if_f0_3, sr2, version19],
+ [f0method8, pretrained_G14, pretrained_D15],
+ )
+ but5 = gr.Button(i18n("一键训练"), variant="primary", visible=False)
+ 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,
+ if_save_every_weights18,
+ version19,
+ ],
+ [
+ info3,
+ butstop,
+ but3,
+ ],
+ )
+ but4.click(train_index, [exp_dir1, version19], info3)
+ but5.click(
+ train1key,
+ [
+ exp_dir1,
+ sr2,
+ if_f0_3,
+ trainset_dir4,
+ spk_id5,
+ np7,
+ f0method8,
+ save_epoch10,
+ total_epoch11,
+ batch_size12,
+ if_save_latest13,
+ pretrained_G14,
+ pretrained_D15,
+ gpus16,
+ if_cache_gpu17,
+ if_save_every_weights18,
+ version19,
+ extraction_crepe_hop_length
+ ],
+ info3,
+ )
+
+ else:
+ print(
+ "Pretrained weights not downloaded. Disabling training tab.\n"
+ "Wondering how to train a voice? Visit here for the RVC model training guide: https://t.ly/RVC_Training_Guide\n"
+ "-------------------------------\n"
+ )
+ 
+ if config.iscolab or config.paperspace: # Share gradio link for colab and paperspace (FORK FEATURE)
+ app.queue(concurrency_count=511, max_size=1022).launch(share=True, quiet=True)
+ else:
+ app.queue(concurrency_count=511, max_size=1022).launch(
+ server_name="0.0.0.0",
+ inbrowser=not config.noautoopen,
+ server_port=config.listen_port,
+ quiet=True,
+ )
+#endregion

infer_uvr5.py

@@ -0,0 +1,363 @@
+import os, sys, torch, warnings, pdb
+
+now_dir = os.getcwd()
+sys.path.append(now_dir)
+from json import load as ll
+
+warnings.filterwarnings("ignore")
+import librosa
+import importlib
+import numpy as np
+import hashlib, math
+from tqdm import tqdm
+from lib.uvr5_pack.lib_v5 import spec_utils
+from lib.uvr5_pack.utils import _get_name_params, inference
+from lib.uvr5_pack.lib_v5.model_param_init import ModelParameters
+import soundfile as sf
+from lib.uvr5_pack.lib_v5.nets_new import CascadedNet
+from lib.uvr5_pack.lib_v5 import nets_61968KB as nets
+
+
+class _audio_pre_:
+ def __init__(self, agg, model_path, device, is_half):
+ self.model_path = model_path
+ self.device = device
+ self.data = {
+ # Processing Options
+ "postprocess": False,
+ "tta": False,
+ # Constants
+ "window_size": 512,
+ "agg": agg,
+ "high_end_process": "mirroring",
+ }
+ mp = ModelParameters("lib/uvr5_pack/lib_v5/modelparams/4band_v2.json")
+ model = nets.CascadedASPPNet(mp.param["bins"] * 2)
+ cpk = torch.load(model_path, map_location="cpu")
+ model.load_state_dict(cpk)
+ model.eval()
+ if is_half:
+ model = model.half().to(device)
+ else:
+ model = model.to(device)
+
+ self.mp = mp
+ self.model = model
+
+ def _path_audio_(self, music_file, ins_root=None, vocal_root=None, format="flac"):
+ if ins_root is None and vocal_root is None:
+ return "No save root."
+ name = os.path.basename(music_file)
+ if ins_root is not None:
+ os.makedirs(ins_root, exist_ok=True)
+ if vocal_root is not None:
+ os.makedirs(vocal_root, exist_ok=True)
+ X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {}
+ bands_n = len(self.mp.param["band"])
+ # print(bands_n)
+ for d in range(bands_n, 0, -1):
+ bp = self.mp.param["band"][d]
+ if d == bands_n: # high-end band
+ (
+ X_wave[d],
+ _,
+ ) = librosa.core.load( # 理论上librosa读取可能对某些音频有bug，应该上ffmpeg读取，但是太麻烦了弃坑
+ music_file,
+ bp["sr"],
+ False,
+ dtype=np.float32,
+ res_type=bp["res_type"],
+ )
+ if X_wave[d].ndim == 1:
+ X_wave[d] = np.asfortranarray([X_wave[d], X_wave[d]])
+ else: # lower bands
+ X_wave[d] = librosa.core.resample(
+ X_wave[d + 1],
+ self.mp.param["band"][d + 1]["sr"],
+ bp["sr"],
+ res_type=bp["res_type"],
+ )
+ # Stft of wave source
+ X_spec_s[d] = spec_utils.wave_to_spectrogram_mt(
+ X_wave[d],
+ bp["hl"],
+ bp["n_fft"],
+ self.mp.param["mid_side"],
+ self.mp.param["mid_side_b2"],
+ self.mp.param["reverse"],
+ )
+ # pdb.set_trace()
+ if d == bands_n and self.data["high_end_process"] != "none":
+ input_high_end_h = (bp["n_fft"] // 2 - bp["crop_stop"]) + (
+ self.mp.param["pre_filter_stop"] - self.mp.param["pre_filter_start"]
+ )
+ input_high_end = X_spec_s[d][
+ :, bp["n_fft"] // 2 - input_high_end_h : bp["n_fft"] // 2, :
+ ]
+
+ X_spec_m = spec_utils.combine_spectrograms(X_spec_s, self.mp)
+ aggresive_set = float(self.data["agg"] / 100)
+ aggressiveness = {
+ "value": aggresive_set,
+ "split_bin": self.mp.param["band"][1]["crop_stop"],
+ }
+ with torch.no_grad():
+ pred, X_mag, X_phase = inference(
+ X_spec_m, self.device, self.model, aggressiveness, self.data
+ )
+ # Postprocess
+ if self.data["postprocess"]:
+ pred_inv = np.clip(X_mag - pred, 0, np.inf)
+ pred = spec_utils.mask_silence(pred, pred_inv)
+ y_spec_m = pred * X_phase
+ v_spec_m = X_spec_m - y_spec_m
+
+ if ins_root is not None:
+ if self.data["high_end_process"].startswith("mirroring"):
+ input_high_end_ = spec_utils.mirroring(
+ self.data["high_end_process"], y_spec_m, input_high_end, self.mp
+ )
+ wav_instrument = spec_utils.cmb_spectrogram_to_wave(
+ y_spec_m, self.mp, input_high_end_h, input_high_end_
+ )
+ else:
+ wav_instrument = spec_utils.cmb_spectrogram_to_wave(y_spec_m, self.mp)
+ print("%s instruments done" % name)
+ if format in ["wav", "flac"]:
+ sf.write(
+ os.path.join(
+ ins_root,
+ "instrument_{}_{}.{}".format(name, self.data["agg"], format),
+ ),
+ (np.array(wav_instrument) * 32768).astype("int16"),
+ self.mp.param["sr"],
+ ) #
+ else:
+ path = os.path.join(
+ ins_root, "instrument_{}_{}.wav".format(name, self.data["agg"])
+ )
+ sf.write(
+ path,
+ (np.array(wav_instrument) * 32768).astype("int16"),
+ self.mp.param["sr"],
+ )
+ if os.path.exists(path):
+ os.system(
+ "ffmpeg -i %s -vn %s -q:a 2 -y"
+ % (path, path[:-4] + ".%s" % format)
+ )
+ if vocal_root is not None:
+ if self.data["high_end_process"].startswith("mirroring"):
+ input_high_end_ = spec_utils.mirroring(
+ self.data["high_end_process"], v_spec_m, input_high_end, self.mp
+ )
+ wav_vocals = spec_utils.cmb_spectrogram_to_wave(
+ v_spec_m, self.mp, input_high_end_h, input_high_end_
+ )
+ else:
+ wav_vocals = spec_utils.cmb_spectrogram_to_wave(v_spec_m, self.mp)
+ print("%s vocals done" % name)
+ if format in ["wav", "flac"]:
+ sf.write(
+ os.path.join(
+ vocal_root,
+ "vocal_{}_{}.{}".format(name, self.data["agg"], format),
+ ),
+ (np.array(wav_vocals) * 32768).astype("int16"),
+ self.mp.param["sr"],
+ )
+ else:
+ path = os.path.join(
+ vocal_root, "vocal_{}_{}.wav".format(name, self.data["agg"])
+ )
+ sf.write(
+ path,
+ (np.array(wav_vocals) * 32768).astype("int16"),
+ self.mp.param["sr"],
+ )
+ if os.path.exists(path):
+ os.system(
+ "ffmpeg -i %s -vn %s -q:a 2 -y"
+ % (path, path[:-4] + ".%s" % format)
+ )
+
+
+class _audio_pre_new:
+ def __init__(self, agg, model_path, device, is_half):
+ self.model_path = model_path
+ self.device = device
+ self.data = {
+ # Processing Options
+ "postprocess": False,
+ "tta": False,
+ # Constants
+ "window_size": 512,
+ "agg": agg,
+ "high_end_process": "mirroring",
+ }
+ mp = ModelParameters("lib/uvr5_pack/lib_v5/modelparams/4band_v3.json")
+ nout = 64 if "DeReverb" in model_path else 48
+ model = CascadedNet(mp.param["bins"] * 2, nout)
+ cpk = torch.load(model_path, map_location="cpu")
+ model.load_state_dict(cpk)
+ model.eval()
+ if is_half:
+ model = model.half().to(device)
+ else:
+ model = model.to(device)
+
+ self.mp = mp
+ self.model = model
+
+ def _path_audio_(
+ self, music_file, vocal_root=None, ins_root=None, format="flac"
+ ): # 3个VR模型vocal和ins是反的
+ if ins_root is None and vocal_root is None:
+ return "No save root."
+ name = os.path.basename(music_file)
+ if ins_root is not None:
+ os.makedirs(ins_root, exist_ok=True)
+ if vocal_root is not None:
+ os.makedirs(vocal_root, exist_ok=True)
+ X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {}
+ bands_n = len(self.mp.param["band"])
+ # print(bands_n)
+ for d in range(bands_n, 0, -1):
+ bp = self.mp.param["band"][d]
+ if d == bands_n: # high-end band
+ (
+ X_wave[d],
+ _,
+ ) = librosa.core.load( # 理论上librosa读取可能对某些音频有bug，应该上ffmpeg读取，但是太麻烦了弃坑
+ music_file,
+ bp["sr"],
+ False,
+ dtype=np.float32,
+ res_type=bp["res_type"],
+ )
+ if X_wave[d].ndim == 1:
+ X_wave[d] = np.asfortranarray([X_wave[d], X_wave[d]])
+ else: # lower bands
+ X_wave[d] = librosa.core.resample(
+ X_wave[d + 1],
+ self.mp.param["band"][d + 1]["sr"],
+ bp["sr"],
+ res_type=bp["res_type"],
+ )
+ # Stft of wave source
+ X_spec_s[d] = spec_utils.wave_to_spectrogram_mt(
+ X_wave[d],
+ bp["hl"],
+ bp["n_fft"],
+ self.mp.param["mid_side"],
+ self.mp.param["mid_side_b2"],
+ self.mp.param["reverse"],
+ )
+ # pdb.set_trace()
+ if d == bands_n and self.data["high_end_process"] != "none":
+ input_high_end_h = (bp["n_fft"] // 2 - bp["crop_stop"]) + (
+ self.mp.param["pre_filter_stop"] - self.mp.param["pre_filter_start"]
+ )
+ input_high_end = X_spec_s[d][
+ :, bp["n_fft"] // 2 - input_high_end_h : bp["n_fft"] // 2, :
+ ]
+
+ X_spec_m = spec_utils.combine_spectrograms(X_spec_s, self.mp)
+ aggresive_set = float(self.data["agg"] / 100)
+ aggressiveness = {
+ "value": aggresive_set,
+ "split_bin": self.mp.param["band"][1]["crop_stop"],
+ }
+ with torch.no_grad():
+ pred, X_mag, X_phase = inference(
+ X_spec_m, self.device, self.model, aggressiveness, self.data
+ )
+ # Postprocess
+ if self.data["postprocess"]:
+ pred_inv = np.clip(X_mag - pred, 0, np.inf)
+ pred = spec_utils.mask_silence(pred, pred_inv)
+ y_spec_m = pred * X_phase
+ v_spec_m = X_spec_m - y_spec_m
+
+ if ins_root is not None:
+ if self.data["high_end_process"].startswith("mirroring"):
+ input_high_end_ = spec_utils.mirroring(
+ self.data["high_end_process"], y_spec_m, input_high_end, self.mp
+ )
+ wav_instrument = spec_utils.cmb_spectrogram_to_wave(
+ y_spec_m, self.mp, input_high_end_h, input_high_end_
+ )
+ else:
+ wav_instrument = spec_utils.cmb_spectrogram_to_wave(y_spec_m, self.mp)
+ print("%s instruments done" % name)
+ if format in ["wav", "flac"]:
+ sf.write(
+ os.path.join(
+ ins_root,
+ "instrument_{}_{}.{}".format(name, self.data["agg"], format),
+ ),
+ (np.array(wav_instrument) * 32768).astype("int16"),
+ self.mp.param["sr"],
+ ) #
+ else:
+ path = os.path.join(
+ ins_root, "instrument_{}_{}.wav".format(name, self.data["agg"])
+ )
+ sf.write(
+ path,
+ (np.array(wav_instrument) * 32768).astype("int16"),
+ self.mp.param["sr"],
+ )
+ if os.path.exists(path):
+ os.system(
+ "ffmpeg -i %s -vn %s -q:a 2 -y"
+ % (path, path[:-4] + ".%s" % format)
+ )
+ if vocal_root is not None:
+ if self.data["high_end_process"].startswith("mirroring"):
+ input_high_end_ = spec_utils.mirroring(
+ self.data["high_end_process"], v_spec_m, input_high_end, self.mp
+ )
+ wav_vocals = spec_utils.cmb_spectrogram_to_wave(
+ v_spec_m, self.mp, input_high_end_h, input_high_end_
+ )
+ else:
+ wav_vocals = spec_utils.cmb_spectrogram_to_wave(v_spec_m, self.mp)
+ print("%s vocals done" % name)
+ if format in ["wav", "flac"]:
+ sf.write(
+ os.path.join(
+ vocal_root,
+ "vocal_{}_{}.{}".format(name, self.data["agg"], format),
+ ),
+ (np.array(wav_vocals) * 32768).astype("int16"),
+ self.mp.param["sr"],
+ )
+ else:
+ path = os.path.join(
+ vocal_root, "vocal_{}_{}.wav".format(name, self.data["agg"])
+ )
+ sf.write(
+ path,
+ (np.array(wav_vocals) * 32768).astype("int16"),
+ self.mp.param["sr"],
+ )
+ if os.path.exists(path):
+ os.system(
+ "ffmpeg -i %s -vn %s -q:a 2 -y"
+ % (path, path[:-4] + ".%s" % format)
+ )
+
+
+if __name__ == "__main__":
+ device = "cuda"
+ is_half = True
+ # model_path = "uvr5_weights/2_HP-UVR.pth"
+ # model_path = "uvr5_weights/VR-DeEchoDeReverb.pth"
+ # model_path = "uvr5_weights/VR-DeEchoNormal.pth"
+ model_path = "uvr5_weights/DeEchoNormal.pth"
+ # pre_fun = _audio_pre_(model_path=model_path, device=device, is_half=True,agg=10)
+ pre_fun = _audio_pre_new(model_path=model_path, device=device, is_half=True, agg=10)
+ audio_path = "雪雪伴奏对消HP5.wav"
+ save_path = "opt"
+ pre_fun._path_audio_(audio_path, save_path, save_path)