diff --git a/infer-web.py b/infer-web.py
index bc2d63d..7975c6f 100644
--- a/infer-web.py
+++ b/infer-web.py
@@ -1,1541 +1,1542 @@
-from multiprocessing import cpu_count
-import threading, pdb, librosa
-from time import sleep
-from subprocess import Popen
-from time import sleep
-import torch, os, traceback, sys, warnings, shutil, numpy as np
-import faiss
-from random import shuffle
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-tmp = os.path.join(now_dir, "TEMP")
-shutil.rmtree(tmp, 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 ffmpeg
-
-i18n = I18nAuto()
-# 判断是否有能用来训练和加速推理的N卡
-ncpu = cpu_count()
-ngpu = torch.cuda.device_count()
-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 "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 "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 = "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 infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
-from scipy.io import wavfile
-from fairseq import checkpoint_utils
-import gradio as gr
-import logging
-from vc_infer_pipeline import VC
-from config import (
- is_half,
- device,
- python_cmd,
- listen_port,
- iscolab,
- noparallel,
- noautoopen,
-)
-from infer_uvr5 import _audio_pre_
-from my_utils import load_audio
-from train.process_ckpt import show_info, change_info, merge, extract_small_model
-
-# from trainset_preprocess_pipeline import PreProcess
-logging.getLogger("numba").setLevel(logging.WARNING)
-
-
-class ToolButton(gr.Button, gr.components.FormComponent):
- """Small button with single emoji as text, fits inside gradio forms"""
-
- def __init__(self, **kwargs):
- super().__init__(variant="tool", **kwargs)
-
- def get_block_name(self):
- return "button"
-
-
-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(device)
- if is_half:
- hubert_model = hubert_model.half()
- else:
- hubert_model = hubert_model.float()
- hubert_model.eval()
-
-
-weight_root = "weights"
-weight_uvr5_root = "uvr5_weights"
-names = []
-for name in os.listdir(weight_root):
- if name.endswith(".pth"):
- names.append(name)
-uvr5_names = []
-for name in os.listdir(weight_uvr5_root):
- if name.endswith(".pth"):
- uvr5_names.append(name.replace(".pth", ""))
-
-
-def vc_single(
- sid,
- input_audio,
- f0_up_key,
- f0_file,
- f0_method,
- file_index,
- # file_big_npy,
- index_rate,
-): # spk_item, input_audio0, vc_transform0,f0_file,f0method0
- global tgt_sr, net_g, vc, hubert_model
- if input_audio is None:
- return "You need to upload an audio", None
- f0_up_key = int(f0_up_key)
- try:
- audio = load_audio(input_audio, 16000)
- 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,
- times,
- f0_up_key,
- f0_method,
- file_index,
- # file_big_npy,
- index_rate,
- if_f0,
- f0_file=f0_file,
- )
- print(
- "npy: ", times[0], "s, f0: ", times[1], "s, infer: ", times[2], "s", sep=""
- )
- return "Success", (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_big_npy,
- index_rate,
-):
- 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 = []
- 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
- for path in paths:
- info, opt = vc_single(
- sid,
- path,
- f0_up_key,
- None,
- f0_method,
- file_index,
- # file_big_npy,
- index_rate,
- )
- if info == "Success":
- try:
- tgt_sr, audio_opt = opt
- wavfile.write(
- "%s/%s" % (opt_root, os.path.basename(path)), tgt_sr, audio_opt
- )
- 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()
-
-
-def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg):
- infos = []
- try:
- inp_root = inp_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
- save_root_vocal = (
- save_root_vocal.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
- )
- save_root_ins = (
- save_root_ins.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
- )
- pre_fun = _audio_pre_(
- agg=int(agg),
- model_path=os.path.join(weight_uvr5_root, model_name + ".pth"),
- device=device,
- is_half=is_half,
- )
- if inp_root != "":
- paths = [os.path.join(inp_root, name) for name in os.listdir(inp_root)]
- else:
- paths = [path.name for path in paths]
- for path in paths:
- inp_path = os.path.join(inp_root, path)
- need_reformat = 1
- done = 0
- try:
- info = ffmpeg.probe(inp_path, cmd="ffprobe")
- if (
- info["streams"][0]["channels"] == 2
- and info["streams"][0]["sample_rate"] == "44100"
- ):
- need_reformat = 0
- pre_fun._path_audio_(inp_path, save_root_ins, save_root_vocal)
- done = 1
- except:
- need_reformat = 1
- traceback.print_exc()
- if need_reformat == 1:
- tmp_path = "%s/%s.reformatted.wav" % (tmp, os.path.basename(inp_path))
- os.system(
- "ffmpeg -i %s -vn -acodec pcm_s16le -ac 2 -ar 44100 %s -y"
- % (inp_path, tmp_path)
- )
- inp_path = tmp_path
- try:
- if done == 0:
- pre_fun._path_audio_(inp_path, save_root_ins, save_root_vocal)
- infos.append("%s->Success" % (os.path.basename(inp_path)))
- yield "\n".join(infos)
- except:
- infos.append(
- "%s->%s" % (os.path.basename(inp_path), traceback.format_exc())
- )
- yield "\n".join(infos)
- except:
- infos.append(traceback.format_exc())
- yield "\n".join(infos)
- finally:
- try:
- del pre_fun.model
- del pre_fun
- except:
- traceback.print_exc()
- print("clean_empty_cache")
- if torch.cuda.is_available():
- torch.cuda.empty_cache()
- yield "\n".join(infos)
-
-
-# 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
- if 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)
- if if_f0 == 1:
- net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
- else:
- net_g = SynthesizerTrnMs256NSFsid_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)
- if if_f0 == 1:
- net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
- else:
- net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
- del net_g.enc_q
- print(net_g.load_state_dict(cpt["weight"], strict=False)) # 不加这一行清不干净, 真奇葩
- net_g.eval().to(device)
- if is_half:
- net_g = net_g.half()
- else:
- net_g = net_g.float()
- vc = VC(tgt_sr, device, is_half)
- n_spk = cpt["config"][-3]
- return {"visible": True, "maximum": n_spk, "__type__": "update"}
-
-
-def change_choices():
- names = []
- for name in os.listdir(weight_root):
- if name.endswith(".pth"):
- names.append(name)
- return {"choices": sorted(names), "__type__": "update"}
-
-
-def clean():
- return {"value": "", "__type__": "update"}
-
-
-def change_f0(if_f0_3, sr2): # np7, f0method8,pretrained_G14,pretrained_D15
- if if_f0_3 == "是":
- return (
- {"visible": True, "__type__": "update"},
- {"visible": True, "__type__": "update"},
- "pretrained/f0G%s.pth" % sr2,
- "pretrained/f0D%s.pth" % sr2,
- )
- return (
- {"visible": False, "__type__": "update"},
- {"visible": False, "__type__": "update"},
- "pretrained/G%s.pth" % sr2,
- "pretrained/D%s.pth" % sr2,
- )
-
-
-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=ncpu):
- 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 = (
- python_cmd
- + " trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s "
- % (trainset_dir, sr, n_p, now_dir, exp_dir)
- + str(noparallel)
- )
- print(cmd)
- p = Popen(cmd, shell=True) # , stdin=PIPE, stdout=PIPE,stderr=PIPE,cwd=now_dir
- ###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
- 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):
- 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 == "是":
- cmd = python_cmd + " extract_f0_print.py %s/logs/%s %s %s" % (
- now_dir,
- exp_dir,
- n_p,
- f0method,
- )
- print(cmd)
- p = Popen(cmd, shell=True, cwd=now_dir) # , stdin=PIPE, stdout=PIPE,stderr=PIPE
- ###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
- 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 = python_cmd + " extract_feature_print.py %s %s %s %s %s/logs/%s" % (
- device,
- leng,
- idx,
- n_g,
- now_dir,
- exp_dir,
- )
- print(cmd)
- p = Popen(
- cmd, shell=True, cwd=now_dir
- ) # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
- ps.append(p)
- ###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):
- if if_f0_3 == "是":
- return "pretrained/f0G%s.pth" % sr2, "pretrained/f0D%s.pth" % sr2
- else:
- return "pretrained/G%s.pth" % sr2, "pretrained/D%s.pth" % sr2
-
-
-# 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,
-):
- # 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)
- 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,
- )
- )
- if if_f0_3 == "是":
- for _ in range(2):
- opt.append(
- "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature256/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
- % (now_dir, sr2, now_dir, 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_feature256/mute.npy|%s"
- % (now_dir, sr2, now_dir, 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 gpus16:
- cmd = (
- python_cmd
- + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s -pg %s -pd %s -l %s -c %s"
- % (
- exp_dir1,
- sr2,
- 1 if if_f0_3 == "是" else 0,
- batch_size12,
- gpus16,
- total_epoch11,
- save_epoch10,
- pretrained_G14,
- pretrained_D15,
- 1 if if_save_latest13 == "是" else 0,
- 1 if if_cache_gpu17 == "是" else 0,
- )
- )
- else:
- cmd = (
- python_cmd
- + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %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,
- 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"
-
-
-# but4.click(train_index, [exp_dir1], info3)
-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
- 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, "IVF%s,Flat" % n_ivf)
- # index = faiss.index_factory(256, "IVF%s,PQ128x4fs,RFlat"%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_ivf.nprobe = 1
- index.train(big_npy)
- faiss.write_index(
- index,
- "%s/trained_IVF%s_Flat_nprobe_%s.index" % (exp_dir, n_ivf, index_ivf.nprobe),
- )
- # faiss.write_index(index, '%s/trained_IVF%s_Flat_FastScan.index'%(exp_dir,n_ivf))
- 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))
- # faiss.write_index(index, '%s/added_IVF%s_Flat_FastScan.index'%(exp_dir,n_ivf))
- # infos.append("成功构建索引，added_IVF%s_Flat_FastScan.index"%(n_ivf))
- 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,
- 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)
- #########step1:处理数据
- open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir1), "w").close()
- cmd = (
- python_cmd
- + " trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s "
- % (trainset_dir4, sr_dict[sr2], ncpu, now_dir, exp_dir1)
- + str(noparallel)
- )
- yield get_info_str("step1:正在处理数据")
- yield get_info_str(cmd)
- p = Popen(cmd, shell=True)
- p.wait()
- with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir1), "r") as f:
- print(f.read())
- #########step2a:提取音高
- open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir1), "w")
- if if_f0_3 == "是":
- yield get_info_str("step2a:正在提取音高")
- cmd = python_cmd + " extract_f0_print.py %s/logs/%s %s %s" % (
- now_dir,
- exp_dir1,
- np7,
- f0method8,
- )
- yield get_info_str(cmd)
- p = Popen(cmd, shell=True, cwd=now_dir)
- p.wait()
- 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")
- #######step2b:提取特征
- 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 = python_cmd + " extract_feature_print.py %s %s %s %s %s/logs/%s" % (
- device,
- leng,
- idx,
- n_g,
- now_dir,
- exp_dir1,
- )
- yield get_info_str(cmd)
- p = Popen(
- cmd, shell=True, cwd=now_dir
- ) # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
- ps.append(p)
- 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())
- #######step3a:训练模型
- yield get_info_str("step3a:正在训练模型")
- # 生成filelist
- 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,
- )
- )
- if if_f0_3 == "是":
- for _ in range(2):
- opt.append(
- "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature256/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
- % (now_dir, sr2, now_dir, 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_feature256/mute.npy|%s"
- % (now_dir, sr2, now_dir, spk_id5)
- )
- shuffle(opt)
- with open("%s/filelist.txt" % exp_dir, "w") as f:
- f.write("\n".join(opt))
- yield get_info_str("write filelist done")
- if gpus16:
- cmd = (
- python_cmd
- + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s -pg %s -pd %s -l %s -c %s"
- % (
- exp_dir1,
- sr2,
- 1 if if_f0_3 == "是" else 0,
- batch_size12,
- gpus16,
- total_epoch11,
- save_epoch10,
- pretrained_G14,
- pretrained_D15,
- 1 if if_save_latest13 == "是" else 0,
- 1 if if_cache_gpu17 == "是" else 0,
- )
- )
- else:
- cmd = (
- python_cmd
- + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %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,
- 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")
- #######step3b:训练索引
- 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
- 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, "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_ivf.nprobe = 1
- 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！")
-
-
-# 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"}
- 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"}
-
-
-from infer_pack.models_onnx_moess import SynthesizerTrnMs256NSFsidM
-from infer_pack.models_onnx import SynthesizerTrnMs256NSFsidO
-
-
-def export_onnx(ModelPath, ExportedPath, MoeVS=True):
- hidden_channels = 256 # hidden_channels，为768Vec做准备
- cpt = torch.load(ModelPath, map_location="cpu")
- cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
- print(*cpt["config"])
-
- 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）
-
- if MoeVS:
- net_g = SynthesizerTrnMs256NSFsidM(
- *cpt["config"], is_half=False
- ) # 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",
- ]
- 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,
- )
- else:
- net_g = SynthesizerTrnMs256NSFsidO(
- *cpt["config"], is_half=False
- ) # 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"]
- output_names = [
- "audio",
- ]
- 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),
- ),
- ExportedPath,
- dynamic_axes={
- "phone": [1],
- "pitch": [1],
- "pitchf": [1],
- },
- do_constant_folding=False,
- opset_version=16,
- verbose=False,
- input_names=input_names,
- output_names=output_names,
- )
- return "Finished"
-
-
-with gr.Blocks() as app:
- gr.Markdown(
- value=i18n(
- "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.
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(i18n("模型推理")):
- with gr.Row():
- sid0 = gr.Dropdown(label=i18n("推理音色"), choices=sorted(names))
- refresh_button = gr.Button(i18n("刷新音色列表"), variant="primary")
- refresh_button.click(fn=change_choices, inputs=[], outputs=[sid0])
- 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],
- )
- with gr.Group():
- gr.Markdown(
- value=i18n("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=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0
- )
- input_audio0 = gr.Textbox(
- label=i18n("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=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"),
- choices=["pm", "harvest"],
- value="pm",
- interactive=True,
- )
- with gr.Column():
- file_index1 = gr.Textbox(
- label=i18n("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=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="检索特征占比",
- value=0.76,
- interactive=True,
- )
- 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"))
- but0 = gr.Button(i18n("转换"), variant="primary")
- with gr.Column():
- vc_output1 = gr.Textbox(label=i18n("输出信息"))
- vc_output2 = gr.Audio(label=i18n("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=i18n("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=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"),
- choices=["pm", "harvest"],
- value="pm",
- interactive=True,
- )
- with gr.Column():
- file_index2 = gr.Textbox(
- label=i18n("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=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():
- 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\\vits_vc_gpu_train\\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")
- )
- 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_index2,
- # file_big_npy2,
- index_rate2,
- ],
- [vc_output3],
- )
- with gr.TabItem(i18n("伴奏人声分离")):
- with gr.Group():
- gr.Markdown(
- value=i18n(
- "人声伴奏分离批量处理, 使用UVR5模型.
不带和声用HP2, 带和声且提取的人声不需要和声用HP5
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=i18n("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=i18n("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=i18n("模型"), choices=uvr5_names)
- agg = gr.Slider(
- minimum=0,
- maximum=20,
- step=1,
- label="人声提取激进程度",
- value=10,
- interactive=True,
- visible=False, # 先不开放调整
- )
- opt_vocal_root = gr.Textbox(
- label=i18n("指定输出人声文件夹"), value="opt"
- )
- opt_ins_root = gr.Textbox(label=i18n("指定输出乐器文件夹"), value="opt")
- but2 = gr.Button(i18n("转换"), variant="primary")
- vc_output4 = gr.Textbox(label=i18n("输出信息"))
- but2.click(
- uvr,
- [
- model_choose,
- dir_wav_input,
- opt_vocal_root,
- wav_inputs,
- opt_ins_root,
- agg,
- ],
- [vc_output4],
- )
- with gr.TabItem(i18n("训练")):
- gr.Markdown(
- value=i18n(
- "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=i18n("输入实验名"), value="mi-test")
- sr2 = gr.Radio(
- label=i18n("目标采样率"),
- choices=["32k", "40k", "48k"],
- value="40k",
- interactive=True,
- )
- 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=["是", "否"],
- value="是",
- interactive=True,
- )
- with gr.Group(): # 暂时单人的, 后面支持最多4人的#数据处理
- gr.Markdown(
- value=i18n(
- "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=i18n("输入训练文件夹路径"), value="E:\\语音音频+标注\\米津玄师\\src"
- )
- spk_id5 = gr.Slider(
- minimum=0,
- maximum=4,
- step=1,
- label=i18n("请指定说话人id"),
- value=0,
- interactive=True,
- )
- but1 = gr.Button(i18n("处理数据"), variant="primary")
- info1 = gr.Textbox(label=i18n("输出信息"), value="")
- but1.click(
- preprocess_dataset, [trainset_dir4, exp_dir1, sr2], [info1]
- )
- with gr.Group():
- gr.Markdown(value=i18n("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=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,
- )
- gpu_info9 = gr.Textbox(label=i18n("显卡信息"), value=gpu_info)
- with gr.Column():
- np7 = gr.Slider(
- minimum=0,
- maximum=ncpu,
- step=1,
- label=i18n("提取音高使用的CPU进程数"),
- value=ncpu,
- interactive=True,
- )
- 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=["pm", "harvest", "dio"],
- value="harvest",
- interactive=True,
- )
- but2 = gr.Button(i18n("特征提取"), variant="primary")
- info2 = gr.Textbox(label=i18n("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=i18n("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=i18n("保存频率save_every_epoch"),
- value=5,
- interactive=True,
- )
- total_epoch11 = gr.Slider(
- minimum=0,
- maximum=1000,
- step=1,
- label=i18n("总训练轮数total_epoch"),
- value=20,
- interactive=True,
- )
- batch_size12 = gr.Slider(
- minimum=0,
- maximum=40,
- step=1,
- label="每张显卡的batch_size",
- value=default_batch_size,
- interactive=True,
- )
- if_save_latest13 = gr.Radio(
- label=i18n("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=i18n(
- "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=i18n("加载预训练底模G路径"),
- value="pretrained/f0G40k.pth",
- interactive=True,
- )
- pretrained_D15 = gr.Textbox(
- label=i18n("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=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,
- )
- but3 = gr.Button(i18n("训练模型"), variant="primary")
- but4 = gr.Button(i18n("训练特征索引"), variant="primary")
- but5 = gr.Button(i18n("一键训练"), variant="primary")
- info3 = gr.Textbox(label=i18n("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(i18n("ckpt处理")):
- with gr.Group():
- gr.Markdown(value=i18n("模型融合, 可用于测试音色融合"))
- with gr.Row():
- ckpt_a = gr.Textbox(label=i18n("A模型路径"), value="", interactive=True)
- ckpt_b = gr.Textbox(label=i18n("B模型路径"), value="", interactive=True)
- alpha_a = gr.Slider(
- minimum=0,
- maximum=1,
- label=i18n("A模型权重"),
- value=0.5,
- interactive=True,
- )
- with gr.Row():
- sr_ = gr.Radio(
- label=i18n("目标采样率"),
- choices=["32k", "40k", "48k"],
- value="40k",
- interactive=True,
- )
- if_f0_ = gr.Radio(
- label=i18n("模型是否带音高指导"),
- choices=["是", "否"],
- value="是",
- interactive=True,
- )
- info__ = gr.Textbox(
- label=i18n("要置入的模型信息"), value="", max_lines=8, interactive=True
- )
- name_to_save0 = gr.Textbox(
- label=i18n("保存的模型名不带后缀"),
- value="",
- max_lines=1,
- interactive=True,
- )
- with gr.Row():
- but6 = gr.Button(i18n("融合"), variant="primary")
- info4 = gr.Textbox(label=i18n("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,
- ) # def merge(path1,path2,alpha1,sr,f0,info):
- with gr.Group():
- gr.Markdown(value=i18n("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=i18n("模型路径"), value="", interactive=True
- )
- info_ = gr.Textbox(
- label=i18n("要改的模型信息"), value="", max_lines=8, interactive=True
- )
- name_to_save1 = gr.Textbox(
- label=i18n("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(i18n("修改"), variant="primary")
- info5 = gr.Textbox(label=i18n("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=i18n("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=i18n("模型路径"), value="", interactive=True
- )
- but8 = gr.Button(i18n("查看"), variant="primary")
- info6 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
- but8.click(show_info, [ckpt_path1], info6)
- with gr.Group():
- gr.Markdown(
- value=i18n(
- "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=i18n("模型路径"),
- value="E:\\codes\\py39\\logs\\mi-test_f0_48k\\G_23333.pth",
- interactive=True,
- )
- save_name = gr.Textbox(
- label=i18n("保存名"), value="", interactive=True
- )
- sr__ = gr.Radio(
- label=i18n("目标采样率"),
- choices=["32k", "40k", "48k"],
- value="40k",
- interactive=True,
- )
- if_f0__ = gr.Radio(
- label=i18n("模型是否带音高指导,1是0否"),
- choices=["1", "0"],
- value="1",
- interactive=True,
- )
- info___ = gr.Textbox(
- label=i18n("要置入的模型信息"), value="", max_lines=8, interactive=True
- )
- but9 = gr.Button(i18n("提取"), variant="primary")
- info7 = gr.Textbox(label=i18n("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(i18n("Onnx导出")):
- with gr.Row():
- ckpt_dir = gr.Textbox(label=i18n("RVC模型路径"), value="", interactive=True)
- with gr.Row():
- onnx_dir = gr.Textbox(
- label=i18n("Onnx输出路径"), value="", interactive=True
- )
- with gr.Row():
- moevs = gr.Checkbox(label=i18n("MoeVS模型"), value=True)
- infoOnnx = gr.Label(label="Null")
- with gr.Row():
- butOnnx = gr.Button(i18n("导出Onnx模型"), variant="primary")
- butOnnx.click(export_onnx, [ckpt_dir, onnx_dir, moevs], infoOnnx)
-
- # with gr.TabItem(i18n("招募音高曲线前端编辑器")):
- # gr.Markdown(value=i18n("加开发群联系我xxxxx"))
- # with gr.TabItem(i18n("点击查看交流、问题反馈群号")):
- # gr.Markdown(value=i18n("xxxxx"))
-
- if iscolab:
- app.queue(concurrency_count=511, max_size=1022).launch(share=True)
- else:
- app.queue(concurrency_count=511, max_size=1022).launch(
- server_name="0.0.0.0",
- inbrowser=not noautoopen,
- server_port=listen_port,
- quiet=True,
- )
+from multiprocessing import cpu_count
+import threading, pdb, librosa
+from time import sleep
+from subprocess import Popen
+from time import sleep
+import torch, os, traceback, sys, warnings, shutil, numpy as np
+import faiss
+from random import shuffle
+
+now_dir = os.getcwd()
+sys.path.append(now_dir)
+tmp = os.path.join(now_dir, "TEMP")
+shutil.rmtree(tmp, 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 ffmpeg
+
+i18n = I18nAuto()
+# 判断是否有能用来训练和加速推理的N卡
+ncpu = cpu_count()
+ngpu = torch.cuda.device_count()
+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 "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 = "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 infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
+from scipy.io import wavfile
+from fairseq import checkpoint_utils
+import gradio as gr
+import logging
+from vc_infer_pipeline import VC
+from config import (
+ is_half,
+ device,
+ python_cmd,
+ listen_port,
+ iscolab,
+ noparallel,
+ noautoopen,
+)
+from infer_uvr5 import _audio_pre_
+from my_utils import load_audio
+from train.process_ckpt import show_info, change_info, merge, extract_small_model
+
+# from trainset_preprocess_pipeline import PreProcess
+logging.getLogger("numba").setLevel(logging.WARNING)
+
+
+class ToolButton(gr.Button, gr.components.FormComponent):
+ """Small button with single emoji as text, fits inside gradio forms"""
+
+ def __init__(self, **kwargs):
+ super().__init__(variant="tool", **kwargs)
+
+ def get_block_name(self):
+ return "button"
+
+
+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(device)
+ if is_half:
+ hubert_model = hubert_model.half()
+ else:
+ hubert_model = hubert_model.float()
+ hubert_model.eval()
+
+
+weight_root = "weights"
+weight_uvr5_root = "uvr5_weights"
+names = []
+for name in os.listdir(weight_root):
+ if name.endswith(".pth"):
+ names.append(name)
+uvr5_names = []
+for name in os.listdir(weight_uvr5_root):
+ if name.endswith(".pth"):
+ uvr5_names.append(name.replace(".pth", ""))
+
+
+def vc_single(
+ sid,
+ input_audio,
+ f0_up_key,
+ f0_file,
+ f0_method,
+ file_index,
+ # file_big_npy,
+ index_rate,
+): # spk_item, input_audio0, vc_transform0,f0_file,f0method0
+ global tgt_sr, net_g, vc, hubert_model
+ if input_audio is None:
+ return "You need to upload an audio", None
+ f0_up_key = int(f0_up_key)
+ try:
+ audio = load_audio(input_audio, 16000)
+ 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,
+ times,
+ f0_up_key,
+ f0_method,
+ file_index,
+ # file_big_npy,
+ index_rate,
+ if_f0,
+ f0_file=f0_file,
+ )
+ print(
+ "npy: ", times[0], "s, f0: ", times[1], "s, infer: ", times[2], "s", sep=""
+ )
+ return "Success", (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_big_npy,
+ index_rate,
+):
+ 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 = []
+ 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
+ for path in paths:
+ info, opt = vc_single(
+ sid,
+ path,
+ f0_up_key,
+ None,
+ f0_method,
+ file_index,
+ # file_big_npy,
+ index_rate,
+ )
+ if info == "Success":
+ try:
+ tgt_sr, audio_opt = opt
+ wavfile.write(
+ "%s/%s" % (opt_root, os.path.basename(path)), tgt_sr, audio_opt
+ )
+ 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()
+
+
+def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg):
+ infos = []
+ try:
+ inp_root = inp_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
+ save_root_vocal = (
+ save_root_vocal.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
+ )
+ save_root_ins = (
+ save_root_ins.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
+ )
+ pre_fun = _audio_pre_(
+ agg=int(agg),
+ model_path=os.path.join(weight_uvr5_root, model_name + ".pth"),
+ device=device,
+ is_half=is_half,
+ )
+ if inp_root != "":
+ paths = [os.path.join(inp_root, name) for name in os.listdir(inp_root)]
+ else:
+ paths = [path.name for path in paths]
+ for path in paths:
+ inp_path = os.path.join(inp_root, path)
+ need_reformat = 1
+ done = 0
+ try:
+ info = ffmpeg.probe(inp_path, cmd="ffprobe")
+ if (
+ info["streams"][0]["channels"] == 2
+ and info["streams"][0]["sample_rate"] == "44100"
+ ):
+ need_reformat = 0
+ pre_fun._path_audio_(inp_path, save_root_ins, save_root_vocal)
+ done = 1
+ except:
+ need_reformat = 1
+ traceback.print_exc()
+ if need_reformat == 1:
+ tmp_path = "%s/%s.reformatted.wav" % (tmp, os.path.basename(inp_path))
+ os.system(
+ "ffmpeg -i %s -vn -acodec pcm_s16le -ac 2 -ar 44100 %s -y"
+ % (inp_path, tmp_path)
+ )
+ inp_path = tmp_path
+ try:
+ if done == 0:
+ pre_fun._path_audio_(inp_path, save_root_ins, save_root_vocal)
+ infos.append("%s->Success" % (os.path.basename(inp_path)))
+ yield "\n".join(infos)
+ except:
+ infos.append(
+ "%s->%s" % (os.path.basename(inp_path), traceback.format_exc())
+ )
+ yield "\n".join(infos)
+ except:
+ infos.append(traceback.format_exc())
+ yield "\n".join(infos)
+ finally:
+ try:
+ del pre_fun.model
+ del pre_fun
+ except:
+ traceback.print_exc()
+ print("clean_empty_cache")
+ if torch.cuda.is_available():
+ torch.cuda.empty_cache()
+ yield "\n".join(infos)
+
+
+# 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
+ if 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)
+ if if_f0 == 1:
+ net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
+ else:
+ net_g = SynthesizerTrnMs256NSFsid_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)
+ if if_f0 == 1:
+ net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
+ else:
+ net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
+ del net_g.enc_q
+ print(net_g.load_state_dict(cpt["weight"], strict=False)) # 不加这一行清不干净, 真奇葩
+ net_g.eval().to(device)
+ if is_half:
+ net_g = net_g.half()
+ else:
+ net_g = net_g.float()
+ vc = VC(tgt_sr, device, is_half)
+ n_spk = cpt["config"][-3]
+ return {"visible": True, "maximum": n_spk, "__type__": "update"}
+
+
+def change_choices():
+ names = []
+ for name in os.listdir(weight_root):
+ if name.endswith(".pth"):
+ names.append(name)
+ return {"choices": sorted(names), "__type__": "update"}
+
+
+def clean():
+ return {"value": "", "__type__": "update"}
+
+
+def change_f0(if_f0_3, sr2): # np7, f0method8,pretrained_G14,pretrained_D15
+ if if_f0_3 == "是":
+ return (
+ {"visible": True, "__type__": "update"},
+ {"visible": True, "__type__": "update"},
+ "pretrained/f0G%s.pth" % sr2,
+ "pretrained/f0D%s.pth" % sr2,
+ )
+ return (
+ {"visible": False, "__type__": "update"},
+ {"visible": False, "__type__": "update"},
+ "pretrained/G%s.pth" % sr2,
+ "pretrained/D%s.pth" % sr2,
+ )
+
+
+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=ncpu):
+ 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 = (
+ python_cmd
+ + " trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s "
+ % (trainset_dir, sr, n_p, now_dir, exp_dir)
+ + str(noparallel)
+ )
+ print(cmd)
+ p = Popen(cmd, shell=True) # , stdin=PIPE, stdout=PIPE,stderr=PIPE,cwd=now_dir
+ ###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
+ 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):
+ 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 == "是":
+ cmd = python_cmd + " extract_f0_print.py %s/logs/%s %s %s" % (
+ now_dir,
+ exp_dir,
+ n_p,
+ f0method,
+ )
+ print(cmd)
+ p = Popen(cmd, shell=True, cwd=now_dir) # , stdin=PIPE, stdout=PIPE,stderr=PIPE
+ ###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
+ 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 = python_cmd + " extract_feature_print.py %s %s %s %s %s/logs/%s" % (
+ device,
+ leng,
+ idx,
+ n_g,
+ now_dir,
+ exp_dir,
+ )
+ print(cmd)
+ p = Popen(
+ cmd, shell=True, cwd=now_dir
+ ) # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
+ ps.append(p)
+ ###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):
+ if if_f0_3 == "是":
+ return "pretrained/f0G%s.pth" % sr2, "pretrained/f0D%s.pth" % sr2
+ else:
+ return "pretrained/G%s.pth" % sr2, "pretrained/D%s.pth" % sr2
+
+
+# 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,
+):
+ # 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)
+ 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,
+ )
+ )
+ if if_f0_3 == "是":
+ for _ in range(2):
+ opt.append(
+ "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature256/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
+ % (now_dir, sr2, now_dir, 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_feature256/mute.npy|%s"
+ % (now_dir, sr2, now_dir, 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 gpus16:
+ cmd = (
+ python_cmd
+ + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s -pg %s -pd %s -l %s -c %s"
+ % (
+ exp_dir1,
+ sr2,
+ 1 if if_f0_3 == "是" else 0,
+ batch_size12,
+ gpus16,
+ total_epoch11,
+ save_epoch10,
+ pretrained_G14,
+ pretrained_D15,
+ 1 if if_save_latest13 == "是" else 0,
+ 1 if if_cache_gpu17 == "是" else 0,
+ )
+ )
+ else:
+ cmd = (
+ python_cmd
+ + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %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,
+ 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"
+
+
+# but4.click(train_index, [exp_dir1], info3)
+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
+ 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, "IVF%s,Flat" % n_ivf)
+ # index = faiss.index_factory(256, "IVF%s,PQ128x4fs,RFlat"%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_ivf.nprobe = 1
+ index.train(big_npy)
+ faiss.write_index(
+ index,
+ "%s/trained_IVF%s_Flat_nprobe_%s.index" % (exp_dir, n_ivf, index_ivf.nprobe),
+ )
+ # faiss.write_index(index, '%s/trained_IVF%s_Flat_FastScan.index'%(exp_dir,n_ivf))
+ 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))
+ # faiss.write_index(index, '%s/added_IVF%s_Flat_FastScan.index'%(exp_dir,n_ivf))
+ # infos.append("成功构建索引，added_IVF%s_Flat_FastScan.index"%(n_ivf))
+ 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,
+ 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)
+ #########step1:处理数据
+ open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir1), "w").close()
+ cmd = (
+ python_cmd
+ + " trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s "
+ % (trainset_dir4, sr_dict[sr2], ncpu, now_dir, exp_dir1)
+ + str(noparallel)
+ )
+ yield get_info_str("step1:正在处理数据")
+ yield get_info_str(cmd)
+ p = Popen(cmd, shell=True)
+ p.wait()
+ with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir1), "r") as f:
+ print(f.read())
+ #########step2a:提取音高
+ open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir1), "w")
+ if if_f0_3 == "是":
+ yield get_info_str("step2a:正在提取音高")
+ cmd = python_cmd + " extract_f0_print.py %s/logs/%s %s %s" % (
+ now_dir,
+ exp_dir1,
+ np7,
+ f0method8,
+ )
+ yield get_info_str(cmd)
+ p = Popen(cmd, shell=True, cwd=now_dir)
+ p.wait()
+ 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")
+ #######step2b:提取特征
+ 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 = python_cmd + " extract_feature_print.py %s %s %s %s %s/logs/%s" % (
+ device,
+ leng,
+ idx,
+ n_g,
+ now_dir,
+ exp_dir1,
+ )
+ yield get_info_str(cmd)
+ p = Popen(
+ cmd, shell=True, cwd=now_dir
+ ) # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir
+ ps.append(p)
+ 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())
+ #######step3a:训练模型
+ yield get_info_str("step3a:正在训练模型")
+ # 生成filelist
+ 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,
+ )
+ )
+ if if_f0_3 == "是":
+ for _ in range(2):
+ opt.append(
+ "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature256/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
+ % (now_dir, sr2, now_dir, 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_feature256/mute.npy|%s"
+ % (now_dir, sr2, now_dir, spk_id5)
+ )
+ shuffle(opt)
+ with open("%s/filelist.txt" % exp_dir, "w") as f:
+ f.write("\n".join(opt))
+ yield get_info_str("write filelist done")
+ if gpus16:
+ cmd = (
+ python_cmd
+ + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s -pg %s -pd %s -l %s -c %s"
+ % (
+ exp_dir1,
+ sr2,
+ 1 if if_f0_3 == "是" else 0,
+ batch_size12,
+ gpus16,
+ total_epoch11,
+ save_epoch10,
+ pretrained_G14,
+ pretrained_D15,
+ 1 if if_save_latest13 == "是" else 0,
+ 1 if if_cache_gpu17 == "是" else 0,
+ )
+ )
+ else:
+ cmd = (
+ python_cmd
+ + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %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,
+ 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")
+ #######step3b:训练索引
+ 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
+ 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, "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_ivf.nprobe = 1
+ 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！")
+
+
+# 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"}
+ 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"}
+
+
+from infer_pack.models_onnx_moess import SynthesizerTrnMs256NSFsidM
+from infer_pack.models_onnx import SynthesizerTrnMs256NSFsidO
+
+
+def export_onnx(ModelPath, ExportedPath, MoeVS=True):
+ hidden_channels = 256 # hidden_channels，为768Vec做准备
+ cpt = torch.load(ModelPath, map_location="cpu")
+ cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
+ print(*cpt["config"])
+
+ 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）
+
+ if MoeVS:
+ net_g = SynthesizerTrnMs256NSFsidM(
+ *cpt["config"], is_half=False
+ ) # 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",
+ ]
+ 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,
+ )
+ else:
+ net_g = SynthesizerTrnMs256NSFsidO(
+ *cpt["config"], is_half=False
+ ) # 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"]
+ output_names = [
+ "audio",
+ ]
+ 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),
+ ),
+ ExportedPath,
+ dynamic_axes={
+ "phone": [1],
+ "pitch": [1],
+ "pitchf": [1],
+ },
+ do_constant_folding=False,
+ opset_version=16,
+ verbose=False,
+ input_names=input_names,
+ output_names=output_names,
+ )
+ return "Finished"
+
+
+with gr.Blocks() as app:
+ gr.Markdown(
+ value=i18n(
+ "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.
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(i18n("模型推理")):
+ with gr.Row():
+ sid0 = gr.Dropdown(label=i18n("推理音色"), choices=sorted(names))
+ refresh_button = gr.Button(i18n("刷新音色列表"), variant="primary")
+ refresh_button.click(fn=change_choices, inputs=[], outputs=[sid0])
+ 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],
+ )
+ with gr.Group():
+ gr.Markdown(
+ value=i18n("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=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0
+ )
+ input_audio0 = gr.Textbox(
+ label=i18n("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=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"),
+ choices=["pm", "harvest"],
+ value="pm",
+ interactive=True,
+ )
+ with gr.Column():
+ file_index1 = gr.Textbox(
+ label=i18n("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=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="检索特征占比",
+ value=0.76,
+ interactive=True,
+ )
+ 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"))
+ but0 = gr.Button(i18n("转换"), variant="primary")
+ with gr.Column():
+ vc_output1 = gr.Textbox(label=i18n("输出信息"))
+ vc_output2 = gr.Audio(label=i18n("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=i18n("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=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"),
+ choices=["pm", "harvest"],
+ value="pm",
+ interactive=True,
+ )
+ with gr.Column():
+ file_index2 = gr.Textbox(
+ label=i18n("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=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():
+ 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\\vits_vc_gpu_train\\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")
+ )
+ 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_index2,
+ # file_big_npy2,
+ index_rate2,
+ ],
+ [vc_output3],
+ )
+ with gr.TabItem(i18n("伴奏人声分离")):
+ with gr.Group():
+ gr.Markdown(
+ value=i18n(
+ "人声伴奏分离批量处理, 使用UVR5模型.
不带和声用HP2, 带和声且提取的人声不需要和声用HP5
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=i18n("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=i18n("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=i18n("模型"), choices=uvr5_names)
+ agg = gr.Slider(
+ minimum=0,
+ maximum=20,
+ step=1,
+ label="人声提取激进程度",
+ value=10,
+ interactive=True,
+ visible=False, # 先不开放调整
+ )
+ opt_vocal_root = gr.Textbox(
+ label=i18n("指定输出人声文件夹"), value="opt"
+ )
+ opt_ins_root = gr.Textbox(label=i18n("指定输出乐器文件夹"), value="opt")
+ but2 = gr.Button(i18n("转换"), variant="primary")
+ vc_output4 = gr.Textbox(label=i18n("输出信息"))
+ but2.click(
+ uvr,
+ [
+ model_choose,
+ dir_wav_input,
+ opt_vocal_root,
+ wav_inputs,
+ opt_ins_root,
+ agg,
+ ],
+ [vc_output4],
+ )
+ with gr.TabItem(i18n("训练")):
+ gr.Markdown(
+ value=i18n(
+ "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=i18n("输入实验名"), value="mi-test")
+ sr2 = gr.Radio(
+ label=i18n("目标采样率"),
+ choices=["32k", "40k", "48k"],
+ value="40k",
+ interactive=True,
+ )
+ 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=["是", "否"],
+ value="是",
+ interactive=True,
+ )
+ with gr.Group(): # 暂时单人的, 后面支持最多4人的#数据处理
+ gr.Markdown(
+ value=i18n(
+ "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=i18n("输入训练文件夹路径"), value="E:\\语音音频+标注\\米津玄师\\src"
+ )
+ spk_id5 = gr.Slider(
+ minimum=0,
+ maximum=4,
+ step=1,
+ label=i18n("请指定说话人id"),
+ value=0,
+ interactive=True,
+ )
+ but1 = gr.Button(i18n("处理数据"), variant="primary")
+ info1 = gr.Textbox(label=i18n("输出信息"), value="")
+ but1.click(
+ preprocess_dataset, [trainset_dir4, exp_dir1, sr2], [info1]
+ )
+ with gr.Group():
+ gr.Markdown(value=i18n("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=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,
+ )
+ gpu_info9 = gr.Textbox(label=i18n("显卡信息"), value=gpu_info)
+ with gr.Column():
+ np7 = gr.Slider(
+ minimum=0,
+ maximum=ncpu,
+ step=1,
+ label=i18n("提取音高使用的CPU进程数"),
+ value=ncpu,
+ interactive=True,
+ )
+ 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=["pm", "harvest", "dio"],
+ value="harvest",
+ interactive=True,
+ )
+ but2 = gr.Button(i18n("特征提取"), variant="primary")
+ info2 = gr.Textbox(label=i18n("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=i18n("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=i18n("保存频率save_every_epoch"),
+ value=5,
+ interactive=True,
+ )
+ total_epoch11 = gr.Slider(
+ minimum=0,
+ maximum=1000,
+ step=1,
+ label=i18n("总训练轮数total_epoch"),
+ value=20,
+ interactive=True,
+ )
+ batch_size12 = gr.Slider(
+ minimum=0,
+ maximum=40,
+ step=1,
+ label="每张显卡的batch_size",
+ value=default_batch_size,
+ interactive=True,
+ )
+ if_save_latest13 = gr.Radio(
+ label=i18n("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=i18n(
+ "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=i18n("加载预训练底模G路径"),
+ value="pretrained/f0G40k.pth",
+ interactive=True,
+ )
+ pretrained_D15 = gr.Textbox(
+ label=i18n("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=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,
+ )
+ but3 = gr.Button(i18n("训练模型"), variant="primary")
+ but4 = gr.Button(i18n("训练特征索引"), variant="primary")
+ but5 = gr.Button(i18n("一键训练"), variant="primary")
+ info3 = gr.Textbox(label=i18n("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(i18n("ckpt处理")):
+ with gr.Group():
+ gr.Markdown(value=i18n("模型融合, 可用于测试音色融合"))
+ with gr.Row():
+ ckpt_a = gr.Textbox(label=i18n("A模型路径"), value="", interactive=True)
+ ckpt_b = gr.Textbox(label=i18n("B模型路径"), value="", interactive=True)
+ alpha_a = gr.Slider(
+ minimum=0,
+ maximum=1,
+ label=i18n("A模型权重"),
+ value=0.5,
+ interactive=True,
+ )
+ with gr.Row():
+ sr_ = gr.Radio(
+ label=i18n("目标采样率"),
+ choices=["32k", "40k", "48k"],
+ value="40k",
+ interactive=True,
+ )
+ if_f0_ = gr.Radio(
+ label=i18n("模型是否带音高指导"),
+ choices=["是", "否"],
+ value="是",
+ interactive=True,
+ )
+ info__ = gr.Textbox(
+ label=i18n("要置入的模型信息"), value="", max_lines=8, interactive=True
+ )
+ name_to_save0 = gr.Textbox(
+ label=i18n("保存的模型名不带后缀"),
+ value="",
+ max_lines=1,
+ interactive=True,
+ )
+ with gr.Row():
+ but6 = gr.Button(i18n("融合"), variant="primary")
+ info4 = gr.Textbox(label=i18n("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,
+ ) # def merge(path1,path2,alpha1,sr,f0,info):
+ with gr.Group():
+ gr.Markdown(value=i18n("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=i18n("模型路径"), value="", interactive=True
+ )
+ info_ = gr.Textbox(
+ label=i18n("要改的模型信息"), value="", max_lines=8, interactive=True
+ )
+ name_to_save1 = gr.Textbox(
+ label=i18n("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(i18n("修改"), variant="primary")
+ info5 = gr.Textbox(label=i18n("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=i18n("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=i18n("模型路径"), value="", interactive=True
+ )
+ but8 = gr.Button(i18n("查看"), variant="primary")
+ info6 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
+ but8.click(show_info, [ckpt_path1], info6)
+ with gr.Group():
+ gr.Markdown(
+ value=i18n(
+ "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=i18n("模型路径"),
+ value="E:\\codes\\py39\\logs\\mi-test_f0_48k\\G_23333.pth",
+ interactive=True,
+ )
+ save_name = gr.Textbox(
+ label=i18n("保存名"), value="", interactive=True
+ )
+ sr__ = gr.Radio(
+ label=i18n("目标采样率"),
+ choices=["32k", "40k", "48k"],
+ value="40k",
+ interactive=True,
+ )
+ if_f0__ = gr.Radio(
+ label=i18n("模型是否带音高指导,1是0否"),
+ choices=["1", "0"],
+ value="1",
+ interactive=True,
+ )
+ info___ = gr.Textbox(
+ label=i18n("要置入的模型信息"), value="", max_lines=8, interactive=True
+ )
+ but9 = gr.Button(i18n("提取"), variant="primary")
+ info7 = gr.Textbox(label=i18n("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(i18n("Onnx导出")):
+ with gr.Row():
+ ckpt_dir = gr.Textbox(label=i18n("RVC模型路径"), value="", interactive=True)
+ with gr.Row():
+ onnx_dir = gr.Textbox(
+ label=i18n("Onnx输出路径"), value="", interactive=True
+ )
+ with gr.Row():
+ moevs = gr.Checkbox(label=i18n("MoeVS模型"), value=True)
+ infoOnnx = gr.Label(label="Null")
+ with gr.Row():
+ butOnnx = gr.Button(i18n("导出Onnx模型"), variant="primary")
+ butOnnx.click(export_onnx, [ckpt_dir, onnx_dir, moevs], infoOnnx)
+
+ # with gr.TabItem(i18n("招募音高曲线前端编辑器")):
+ # gr.Markdown(value=i18n("加开发群联系我xxxxx"))
+ # with gr.TabItem(i18n("点击查看交流、问题反馈群号")):
+ # gr.Markdown(value=i18n("xxxxx"))
+
+ if iscolab:
+ app.queue(concurrency_count=511, max_size=1022).launch(share=True)
+ else:
+ app.queue(concurrency_count=511, max_size=1022).launch(
+ server_name="0.0.0.0",
+ inbrowser=not noautoopen,
+ server_port=listen_port,
+ quiet=True,
+ )