diff --git "a/infer-web.py" "b/infer-web.py" new file mode 100644--- /dev/null +++ "b/infer-web.py" @@ -0,0 +1,3094 @@ +import os +import shutil +import sys +import json # Mangio fork using json for preset saving +import math + +import signal + +now_dir = os.getcwd() +sys.path.append(now_dir) +import traceback, pdb +import warnings + +import numpy as np +import torch +import re + +os.environ["OPENBLAS_NUM_THREADS"] = "1" +os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1" +import logging +import threading +from random import shuffle +from subprocess import Popen +from time import sleep + +import faiss +import ffmpeg +import gradio as gr +import soundfile as sf +from config import Config +from fairseq import checkpoint_utils +from i18n import I18nAuto +from lib.infer_pack.models import ( + SynthesizerTrnMs256NSFsid, + SynthesizerTrnMs256NSFsid_nono, + SynthesizerTrnMs768NSFsid, + SynthesizerTrnMs768NSFsid_nono, +) +from lib.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM +from infer_uvr5 import _audio_pre_, _audio_pre_new +from MDXNet import MDXNetDereverb +from my_utils import load_audio, CSVutil +from train.process_ckpt import change_info, extract_small_model, merge, show_info +from vc_infer_pipeline import VC +from sklearn.cluster import MiniBatchKMeans + +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) +shutil.rmtree("%s/runtime/Lib/site-packages/uvr5_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, "audios"), exist_ok=True) +os.makedirs(os.path.join(now_dir, "datasets"), 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) + +logging.getLogger("numba").setLevel(logging.WARNING) + +import csv + +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() + +global DoFormant, Quefrency, Timbre + +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) + +config = Config() +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_gpu_ok = False + +isinterrupted = 0 + +if torch.cuda.is_available() or ngpu != 0: + for i in range(ngpu): + gpu_name = torch.cuda.get_device_name(i) + if any( + value in gpu_name.upper() + for value in [ + "10", + "16", + "20", + "30", + "40", + "A2", + "A3", + "A4", + "P4", + "A50", + "500", + "A60", + "70", + "80", + "90", + "M4", + "T4", + "TITAN", + ] + ): + # 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 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]) + +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" +weight_uvr5_root = "uvr5_weights" +index_root = "./logs/" +audio_root = "audios" +names = [] +for name in os.listdir(weight_root): + if name.endswith(".pth"): + names.append(name) +index_paths = [] + +global indexes_list +indexes_list = [] + +audio_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)) + +for root, dirs, files in os.walk(audio_root, topdown=False): + for name in files: + audio_paths.append("%s/%s" % (root, name)) + +uvr5_names = [] +for name in os.listdir(weight_uvr5_root): + if name.endswith(".pth") or "onnx" in name: + uvr5_names.append(name.replace(".pth", "")) + + +def check_for_name(): + if len(names) > 0: + return sorted(names)[0] + else: + return "" + + +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).replace("\\", "/") + return "" + else: + return "" + + +def get_indexes(): + for dirpath, dirnames, filenames in os.walk("./logs/"): + for filename in filenames: + if filename.endswith(".index") and "trained" not in filename: + indexes_list.append(os.path.join(dirpath, filename).replace("\\", "/")) + if len(indexes_list) > 0: + return indexes_list + else: + return "" + + +fshift_presets_list = [] + + +def get_fshift_presets(): + fshift_presets_list = [] + for dirpath, dirnames, 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 vc_single( + sid, + input_audio_path0, + input_audio_path1, + 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_path0 is None or input_audio_path0 is None: + return "You need to upload an audio", None + f0_up_key = int(f0_up_key) + try: + if input_audio_path0 == "": + audio = load_audio(input_audio_path1, 16000, DoFormant, Quefrency, Timbre) + + else: + audio = load_audio(input_audio_path0, 16000, DoFormant, Quefrency, Timbre) + + audio_max = np.abs(audio).max() / 0.95 + if audio_max > 1: + audio /= audio_max + times = [0, 0, 0] + if not hubert_model: + load_hubert() + if_f0 = cpt.get("f0", 1) + file_index = ( + ( + file_index.strip(" ") + .strip('"') + .strip("\n") + .strip('"') + .strip(" ") + .replace("trained", "added") + ) + if file_index != "" + else file_index2 + ) # 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_path1, + 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 tgt_sr != resample_sr >= 16000: + 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, + None, + f0_up_key, + None, + f0_method, + file_index, + file_index2, + # 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", "mp3", "ogg", "aac"]: + 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() + + +def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format0): + 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(" ") + ) + if model_name == "onnx_dereverb_By_FoxJoy": + pre_fun = MDXNetDereverb(15) + else: + func = _audio_pre_ if "DeEcho" not in model_name else _audio_pre_new + pre_fun = func( + agg=int(agg), + model_path=os.path.join(weight_uvr5_root, model_name + ".pth"), + device=config.device, + is_half=config.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, format0 + ) + 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, format0 + ) + 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: + if model_name == "onnx_dereverb_By_FoxJoy": + del pre_fun.pred.model + del pre_fun.pred.model_ + else: + 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, to_return_protect0, to_return_protect1): + global n_spk, tgt_sr, net_g, vc, cpt, version + if sid == "" or sid == []: + global hubert_model + if hubert_model is not 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"}, + {"visible": False, "__type__": "update"}, + {"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 == 0: + to_return_protect0 = to_return_protect1 = { + "visible": False, + "value": 0.5, + "__type__": "update", + } + else: + to_return_protect0 = { + "visible": True, + "value": to_return_protect0, + "__type__": "update", + } + to_return_protect1 = { + "visible": True, + "value": to_return_protect1, + "__type__": "update", + } + 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": True, "maximum": n_spk, "__type__": "update"}, + to_return_protect0, + to_return_protect1, + ) + + +def change_choices(): + names = [] + for name in os.listdir(weight_root): + if name.endswith(".pth"): + names.append(name) + index_paths = [] + audio_paths = [] + audios_path = os.path.abspath(os.getcwd()) + "/audios/" + 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)) + for file in os.listdir(audios_path): + audio_paths.append("%s/%s" % (audio_root, file)) + return ( + {"choices": sorted(names), "__type__": "update"}, + {"choices": sorted(index_paths), "__type__": "update"}, + {"choices": sorted(audio_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() is 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() is None: + flag = 0 + sleep(0.5) + break + if flag == 1: + break + done[0] = True + + +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 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 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"}, + ) + + +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]: + 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]: + 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]: + 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 not if_pretrained_generator_exist: + print( + "pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), + "doesn't exist, will not use pretrained model", + ) + if not if_pretrained_discriminator_exist: + print( + "pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), + "doesn't 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_version19(sr2, if_f0_3, version19): + path_str = "" if version19 == "v1" else "_v2" + if sr2 == "32k" and version19 == "v1": + sr2 = "40k" + to_return_sr2 = ( + {"choices": ["40k", "48k"], "__type__": "update", "value": sr2} + if version19 == "v1" + else {"choices": ["40k", "48k", "32k"], "__type__": "update", "value": sr2} + ) + 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 not if_pretrained_generator_exist: + print( + "pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), + "doesn't exist, will not use pretrained model", + ) + if not if_pretrained_discriminator_exist: + print( + "pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), + "doesn't 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 "", + to_return_sr2, + ) + + +def change_f0( + if_f0_3, + sr2, + version19, + step2b, + gpus6, + gpu_info9, + extraction_crepe_hop_length, + but2, + info2, +): # 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 not if_pretrained_generator_exist: + print( + "pretrained%s/f0G%s.pth" % (path_str, sr2), + "not exist, will not use pretrained model", + ) + if not if_pretrained_discriminator_exist: + 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 "", + {"visible": True, "__type__": "update"}, + {"visible": True, "__type__": "update"}, + {"visible": True, "__type__": "update"}, + {"visible": True, "__type__": "update"}, + {"visible": True, "__type__": "update"}, + {"visible": True, "__type__": "update"}, + ) + + 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 "", + {"visible": False, "__type__": "update"}, + {"visible": False, "__type__": "update"}, + {"visible": False, "__type__": "update"}, + {"visible": False, "__type__": "update"}, + {"visible": False, "__type__": "update"}, + {"visible": False, "__type__": "update"}, + ) + + +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) + global p + 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 not os.path.exists(feature_dir): + 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！" + infos = [] + 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] + if big_npy.shape[0] > 2e5: + # if(1): + infos.append("Trying doing kmeans %s shape to 10k centers." % big_npy.shape[0]) + yield "\n".join(infos) + try: + big_npy = ( + MiniBatchKMeans( + n_clusters=10000, + verbose=True, + batch_size=256 * config.n_cpu, + compute_labels=False, + init="random", + ) + .fit(big_npy) + .cluster_centers_ + ) + except: + info = traceback.format_exc() + print(info) + infos.append(info) + yield "\n".join(infos) + + np.save("%s/total_fea.npy" % exp_dir, big_npy) + n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39) + 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( + "Successful Index Construction，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) + + +# def setBoolean(status): #true to false and vice versa / not implemented yet, dont touch!!!!!!! +# status = not status +# return status + + +# 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:正在提取特征")) + 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] + + if big_npy.shape[0] > 2e5: + # if(1): + info = "Trying doing kmeans %s shape to 10k centers." % big_npy.shape[0] + print(info) + yield get_info_str(info) + try: + big_npy = ( + MiniBatchKMeans( + n_clusters=10000, + verbose=True, + batch_size=256 * config.n_cpu, + compute_labels=False, + init="random", + ) + .fit(big_npy) + .cluster_centers_ + ) + except: + info = traceback.format_exc() + print(info) + yield get_info_str(info) + + 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！")) + + +# ckpt_path2.change(change_info_,[ckpt_path2],[sr__,if_f0__]) +def change_info_(ckpt_path): + if not os.path.exists(ckpt_path.replace(os.path.basename(ckpt_path), "train.log")): + 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"} + + +def export_onnx(ModelPath, ExportedPath): + cpt = torch.load(ModelPath, map_location="cpu") + cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] + vec_channels = 256 if cpt.get("version", "v1") == "v1" else 768 + + test_phone = torch.rand(1, 200, vec_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=13, + verbose=False, + input_names=input_names, + output_names=output_names, + ) + return "Finished" + + +# region Mangio-RVC-Fork CLI App +import re as regex +import scipy.io.wavfile as wavfile + +cli_current_page = "HOME" + + +def cli_split_command(com): + exp = r'(?:(?<=\s)|^)"(.*?)"(?=\s|$)|(\S+)' + split_array = regex.findall(exp, com) + split_array = [group[0] if group[0] else group[1] for group in split_array] + return split_array + + +def execute_generator_function(genObject): + for _ in genObject: + pass + + +def cli_infer(com): + # get VC first + com = cli_split_command(com) + model_name = com[0] + source_audio_path = com[1] + output_file_name = com[2] + feature_index_path = com[3] + f0_file = None # Not Implemented Yet + + # Get parameters for inference + speaker_id = int(com[4]) + transposition = float(com[5]) + f0_method = com[6] + crepe_hop_length = int(com[7]) + harvest_median_filter = int(com[8]) + resample = int(com[9]) + mix = float(com[10]) + feature_ratio = float(com[11]) + protection_amnt = float(com[12]) + protect1 = 0.5 + + if com[14] == "False" or com[14] == "false": + DoFormant = False + Quefrency = 0.0 + Timbre = 0.0 + CSVutil( + "csvdb/formanting.csv", "w+", "formanting", DoFormant, Quefrency, Timbre + ) + + else: + DoFormant = True + Quefrency = float(com[15]) + Timbre = float(com[16]) + CSVutil( + "csvdb/formanting.csv", "w+", "formanting", DoFormant, Quefrency, Timbre + ) + + print("Mangio-RVC-Fork Infer-CLI: Starting the inference...") + vc_data = get_vc(model_name, protection_amnt, protect1) + print(vc_data) + print("Mangio-RVC-Fork Infer-CLI: Performing inference...") + conversion_data = vc_single( + speaker_id, + source_audio_path, + source_audio_path, + transposition, + f0_file, + f0_method, + feature_index_path, + feature_index_path, + feature_ratio, + harvest_median_filter, + resample, + mix, + protection_amnt, + crepe_hop_length, + ) + if "Success." in conversion_data[0]: + print( + "Mangio-RVC-Fork Infer-CLI: Inference succeeded. Writing to %s/%s..." + % ("audio-outputs", output_file_name) + ) + wavfile.write( + "%s/%s" % ("audio-outputs", output_file_name), + conversion_data[1][0], + conversion_data[1][1], + ) + print( + "Mangio-RVC-Fork Infer-CLI: Finished! Saved output to %s/%s" + % ("audio-outputs", output_file_name) + ) + else: + print("Mangio-RVC-Fork Infer-CLI: Inference failed. Here's the traceback: ") + print(conversion_data[0]) + + +def cli_pre_process(com): + com = cli_split_command(com) + model_name = com[0] + trainset_directory = com[1] + sample_rate = com[2] + num_processes = int(com[3]) + + print("Mangio-RVC-Fork Pre-process: Starting...") + generator = preprocess_dataset( + trainset_directory, model_name, sample_rate, num_processes + ) + execute_generator_function(generator) + print("Mangio-RVC-Fork Pre-process: Finished") + + +def cli_extract_feature(com): + com = cli_split_command(com) + model_name = com[0] + gpus = com[1] + num_processes = int(com[2]) + has_pitch_guidance = True if (int(com[3]) == 1) else False + f0_method = com[4] + crepe_hop_length = int(com[5]) + version = com[6] # v1 or v2 + + print("Mangio-RVC-CLI: Extract Feature Has Pitch: " + str(has_pitch_guidance)) + print("Mangio-RVC-CLI: Extract Feature Version: " + str(version)) + print("Mangio-RVC-Fork Feature Extraction: Starting...") + generator = extract_f0_feature( + gpus, + num_processes, + f0_method, + has_pitch_guidance, + model_name, + version, + crepe_hop_length, + ) + execute_generator_function(generator) + print("Mangio-RVC-Fork Feature Extraction: Finished") + + +def cli_train(com): + com = cli_split_command(com) + model_name = com[0] + sample_rate = com[1] + has_pitch_guidance = True if (int(com[2]) == 1) else False + speaker_id = int(com[3]) + save_epoch_iteration = int(com[4]) + total_epoch = int(com[5]) # 10000 + batch_size = int(com[6]) + gpu_card_slot_numbers = com[7] + if_save_latest = True if (int(com[8]) == 1) else False + if_cache_gpu = True if (int(com[9]) == 1) else False + if_save_every_weight = True if (int(com[10]) == 1) else False + version = com[11] + + pretrained_base = "pretrained/" if version == "v1" else "pretrained_v2/" + + g_pretrained_path = "%sf0G%s.pth" % (pretrained_base, sample_rate) + d_pretrained_path = "%sf0D%s.pth" % (pretrained_base, sample_rate) + + print("Mangio-RVC-Fork Train-CLI: Training...") + click_train( + model_name, + sample_rate, + has_pitch_guidance, + speaker_id, + save_epoch_iteration, + total_epoch, + batch_size, + if_save_latest, + g_pretrained_path, + d_pretrained_path, + gpu_card_slot_numbers, + if_cache_gpu, + if_save_every_weight, + version, + ) + + +def cli_train_feature(com): + com = cli_split_command(com) + model_name = com[0] + version = com[1] + print("Mangio-RVC-Fork Train Feature Index-CLI: Training... Please wait") + generator = train_index(model_name, version) + execute_generator_function(generator) + print("Mangio-RVC-Fork Train Feature Index-CLI: Done!") + + +def cli_extract_model(com): + com = cli_split_command(com) + model_path = com[0] + save_name = com[1] + sample_rate = com[2] + has_pitch_guidance = com[3] + info = com[4] + version = com[5] + extract_small_model_process = extract_small_model( + model_path, save_name, sample_rate, has_pitch_guidance, info, version + ) + if extract_small_model_process == "Success.": + print("Mangio-RVC-Fork Extract Small Model: Success!") + else: + print(str(extract_small_model_process)) + print("Mangio-RVC-Fork Extract Small Model: Failed!") + + +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 print_page_details(): + if cli_current_page == "HOME": + print( + "\n go home : Takes you back to home with a navigation list." + "\n go infer : Takes you to inference command execution." + "\n go pre-process : Takes you to training step.1) pre-process command execution." + "\n go extract-feature : Takes you to training step.2) extract-feature command execution." + "\n go train : Takes you to training step.3) being or continue training command execution." + "\n go train-feature : Takes you to the train feature index command execution." + "\n go extract-model : Takes you to the extract small model command execution." + ) + elif cli_current_page == "INFER": + print( + "\n arg 1) model name with .pth in ./weights: mi-test.pth" + "\n arg 2) source audio path: myFolder\\MySource.wav" + "\n arg 3) output file name to be placed in './audio-outputs': MyTest.wav" + "\n arg 4) feature index file path: logs/mi-test/added_IVF3042_Flat_nprobe_1.index" + "\n arg 5) speaker id: 0" + "\n arg 6) transposition: 0" + "\n arg 7) f0 method: harvest (pm, harvest, crepe, crepe-tiny, hybrid[x,x,x,x], mangio-crepe, mangio-crepe-tiny, rmvpe)" + "\n arg 8) crepe hop length: 128" + "\n arg 9) harvest median filter radius: 3 (0-7)" + "\n arg 10) post resample rate: 0" + "\n arg 11) mix volume envelope: 1" + "\n arg 12) feature index ratio: 0.78 (0-1)" + "\n arg 13) Voiceless Consonant Protection (Less Artifact): 0.33 (Smaller number = more protection. 0.50 means Dont Use.)" + "\n arg 14) Whether to formant shift the inference audio before conversion: False (if set to false, you can ignore setting the quefrency and timbre values for formanting)" + "\n arg 15)* Quefrency for formanting: 8.0 (no need to set if arg14 is False/false)" + "\n arg 16)* Timbre for formanting: 1.2 (no need to set if arg14 is False/false) \n" + "\nExample: mi-test.pth saudio/Sidney.wav myTest.wav logs/mi-test/added_index.index 0 -2 harvest 160 3 0 1 0.95 0.33 0.45 True 8.0 1.2" + ) + elif cli_current_page == "PRE-PROCESS": + print( + "\n arg 1) Model folder name in ./logs: mi-test" + "\n arg 2) Trainset directory: mydataset (or) E:\\my-data-set" + "\n arg 3) Sample rate: 40k (32k, 40k, 48k)" + "\n arg 4) Number of CPU threads to use: 8 \n" + "\nExample: mi-test mydataset 40k 24" + ) + elif cli_current_page == "EXTRACT-FEATURE": + print( + "\n arg 1) Model folder name in ./logs: mi-test" + "\n arg 2) Gpu card slot: 0 (0-1-2 if using 3 GPUs)" + "\n arg 3) Number of CPU threads to use: 8" + "\n arg 4) Has Pitch Guidance?: 1 (0 for no, 1 for yes)" + "\n arg 5) f0 Method: harvest (pm, harvest, dio, crepe)" + "\n arg 6) Crepe hop length: 128" + "\n arg 7) Version for pre-trained models: v2 (use either v1 or v2)\n" + "\nExample: mi-test 0 24 1 harvest 128 v2" + ) + elif cli_current_page == "TRAIN": + print( + "\n arg 1) Model folder name in ./logs: mi-test" + "\n arg 2) Sample rate: 40k (32k, 40k, 48k)" + "\n arg 3) Has Pitch Guidance?: 1 (0 for no, 1 for yes)" + "\n arg 4) speaker id: 0" + "\n arg 5) Save epoch iteration: 50" + "\n arg 6) Total epochs: 10000" + "\n arg 7) Batch size: 8" + "\n arg 8) Gpu card slot: 0 (0-1-2 if using 3 GPUs)" + "\n arg 9) Save only the latest checkpoint: 0 (0 for no, 1 for yes)" + "\n arg 10) Whether to cache training set to vram: 0 (0 for no, 1 for yes)" + "\n arg 11) Save extracted small model every generation?: 0 (0 for no, 1 for yes)" + "\n arg 12) Model architecture version: v2 (use either v1 or v2)\n" + "\nExample: mi-test 40k 1 0 50 10000 8 0 0 0 0 v2" + ) + elif cli_current_page == "TRAIN-FEATURE": + print( + "\n arg 1) Model folder name in ./logs: mi-test" + "\n arg 2) Model architecture version: v2 (use either v1 or v2)\n" + "\nExample: mi-test v2" + ) + elif cli_current_page == "EXTRACT-MODEL": + print( + "\n arg 1) Model Path: logs/mi-test/G_168000.pth" + "\n arg 2) Model save name: MyModel" + "\n arg 3) Sample rate: 40k (32k, 40k, 48k)" + "\n arg 4) Has Pitch Guidance?: 1 (0 for no, 1 for yes)" + '\n arg 5) Model information: "My Model"' + "\n arg 6) Model architecture version: v2 (use either v1 or v2)\n" + '\nExample: logs/mi-test/G_168000.pth MyModel 40k 1 "Created by Cole Mangio" v2' + ) + + +def change_page(page): + global cli_current_page + cli_current_page = page + return 0 + + +def execute_command(com): + if com == "go home": + return change_page("HOME") + elif com == "go infer": + return change_page("INFER") + elif com == "go pre-process": + return change_page("PRE-PROCESS") + elif com == "go extract-feature": + return change_page("EXTRACT-FEATURE") + elif com == "go train": + return change_page("TRAIN") + elif com == "go train-feature": + return change_page("TRAIN-FEATURE") + elif com == "go extract-model": + return change_page("EXTRACT-MODEL") + else: + if com[:3] == "go ": + print("page '%s' does not exist!" % com[3:]) + return 0 + + if cli_current_page == "INFER": + cli_infer(com) + elif cli_current_page == "PRE-PROCESS": + cli_pre_process(com) + elif cli_current_page == "EXTRACT-FEATURE": + cli_extract_feature(com) + elif cli_current_page == "TRAIN": + cli_train(com) + elif cli_current_page == "TRAIN-FEATURE": + cli_train_feature(com) + elif cli_current_page == "EXTRACT-MODEL": + cli_extract_model(com) + + +def cli_navigation_loop(): + while True: + print("\nYou are currently in '%s':" % cli_current_page) + print_page_details() + command = input("%s: " % cli_current_page) + try: + execute_command(command) + except: + print(traceback.format_exc()) + + +if config.is_cli: + print("\n\nMangio-RVC-Fork v2 CLI App!\n") + print( + "Welcome to the CLI version of RVC. Please read the documentation on https://github.com/Mangio621/Mangio-RVC-Fork (README.MD) to understand how to use this app.\n" + ) + cli_navigation_loop() + +# endregion + +# 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 stepdisplay(if_save_every_weights): + return {"visible": if_save_every_weights, "__type__": "update"} + + +def match_index(sid0): + picked = False + # folder = sid0.split('.')[0] + + # folder = re.split(r'. |_', sid0)[0] + folder = sid0.split(".")[0].split("_")[0] + # folder_test = sid0.split('.')[0].split('_')[0].split('-')[0] + parent_dir = "./logs/" + folder + # print(parent_dir) + if os.path.exists(parent_dir): + # print('path exists') + for filename in os.listdir(parent_dir.replace("\\", "/")): + if filename.endswith(".index"): + for i in range(len(indexes_list)): + if indexes_list[i] == ( + os.path.join(("./logs/" + folder), filename).replace("\\", "/") + ): + # print('regular index found') + break + else: + if indexes_list[i] == ( + os.path.join( + ("./logs/" + folder.lower()), filename + ).replace("\\", "/") + ): + # print('lowered index found') + parent_dir = "./logs/" + folder.lower() + break + # elif (indexes_list[i]).casefold() == ((os.path.join(("./logs/" + folder), filename).replace('\\','/')).casefold()): + # print('8') + # parent_dir = "./logs/" + folder.casefold() + # break + # elif (indexes_list[i]) == ((os.path.join(("./logs/" + folder_test), filename).replace('\\','/'))): + # parent_dir = "./logs/" + folder_test + # print(parent_dir) + # break + # elif (indexes_list[i]) == (os.path.join(("./logs/" + folder_test.lower()), filename).replace('\\','/')): + # parent_dir = "./logs/" + folder_test + # print(parent_dir) + # break + # else: + # #print('couldnt find index') + # continue + + # print('all done') + index_path = os.path.join( + parent_dir.replace("\\", "/"), filename.replace("\\", "/") + ).replace("\\", "/") + # print(index_path) + return (index_path, index_path) + + else: + # print('nothing found') + return ("", "") + + +def stoptraining(mim): + if int(mim) == 1: + CSVutil("csvdb/stop.csv", "w+", "stop", "True") + # p.terminate() + # p.kill() + try: + os.kill(PID, signal.SIGTERM) + except Exception as e: + print(f"Couldn't click due to {e}") + pass + else: + pass + + return ( + {"visible": False, "__type__": "update"}, + {"visible": True, "__type__": "update"}, + ) + + +def whethercrepeornah(radio): + mango = True if radio == "mangio-crepe" or radio == "mangio-crepe-tiny" else False + + return {"visible": mango, "__type__": "update"} + + +# Change your Gradio Theme here. 👇 👇 👇 👇 Example: " theme='HaleyCH/HaleyCH_Theme' " +with gr.Blocks(theme=gr.themes.Soft(), title="Mangio-RVC-Web 💻") as app: + gr.HTML("

The Mangio-RVC-Fork 💻

") + 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")): + # 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=i18n("推理音色"), choices=sorted(names), value=check_for_name()) + sid0 = gr.Dropdown(label=i18n("推理音色"), choices=sorted(names), value="") + # input_audio_path2 + + refresh_button = gr.Button( + i18n("Refresh voice list, index path and audio files"), + variant="primary", + ) + 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]) + + 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( + "Add audio's name to the path to the audio file to be processed (default is the correct format example) Remove the path to use an audio from the dropdown list:" + ), + value=os.path.abspath(os.getcwd()).replace("\\", "/") + + "/audios/" + + "audio.wav", + ) + input_audio1 = gr.Dropdown( + label=i18n( + "Auto detect audio path and select from the dropdown:" + ), + choices=sorted(audio_paths), + value="", + interactive=True, + ) + input_audio1.change( + fn=lambda: "", inputs=[], outputs=[input_audio0] + ) + 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,crepe效果好但吃GPU" + ), + choices=[ + "pm", + "harvest", + "dio", + "crepe", + "crepe-tiny", + "mangio-crepe", + "mangio-crepe-tiny", + "rmvpe", + ], # Fork Feature. Add Crepe-Tiny + value="rmvpe", + interactive=True, + ) + crepe_hop_length = gr.Slider( + minimum=1, + maximum=512, + step=1, + label=i18n("crepe_hop_length"), + 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, + ) + with gr.Column(): + file_index1 = 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_index2 = 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, + allow_custom_value=True, + ) + # sid0.select(fn=match_index, inputs=sid0, outputs=file_index2) + + refresh_button.click( + fn=change_choices, + inputs=[], + outputs=[sid0, file_index2, input_audio1], + ) + # 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=0.75, + interactive=True, + ) + with gr.Column(): + 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, + ) + 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��"), + value=0.25, + 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", + ) + + 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], + ) + ##formant_refresh_button.click(fn=preset_apply, inputs=[formant_preset, qfrency, tmbre], outputs=[formant_preset, qfrency, tmbre]) + ##formant_refresh_button.click(fn=update_fshift_presets, inputs=[formant_preset, qfrency, tmbre], outputs=[formant_preset, qfrency, tmbre]) + 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.Row(): + 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, + input_audio1, + 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.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,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( # file index dropdown for batch + label=i18n("自动检测index路径,下拉式选择(dropdown)"), + choices=get_indexes(), + value=get_index(), + interactive=True, + ) + sid0.select( + fn=match_index, + inputs=[sid0], + outputs=[file_index2, file_index4], + ) + 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=0.25, + 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=os.path.abspath(os.getcwd()).replace("\\", "/") + + "/audios/", + ) + 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], + ) + sid0.change( + fn=get_vc, + inputs=[sid0, protect0, protect1], + outputs=[spk_item, protect0, protect1], + ) + with gr.TabItem(i18n("伴奏人声分离&去混响&去回声")): + with gr.Group(): + gr.Markdown( + value=i18n( + "人声伴奏分离批量处理， 使用UVR5模型。
" + "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)。
" + "模型分为三类：
" + "1、保留人声：不带和声的音频选这个，对主人声保留比HP5更好。内置HP2和HP3两个模型，HP3可能轻微漏伴奏但对主人声保留比HP2稍微好一丁点；
" + "2、仅保留主人声：带和声的音频选这个，对主人声可能有削弱。内置HP5一个模型；
" + "3、去混响、去延迟模型（by FoxJoy）：
" + "  (1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；
" + " (234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。
" + "去混响/去延迟，附：
" + "1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；
" + "2、MDX-Net-Dereverb模型挺慢的；
" + "3、个人推荐的最干净的配置是先MDX-Net再DeEcho-Aggressive。" + ) + ) + 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=((os.getcwd()).replace("\\", "/") + "/audios/"), + ) + 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("指定输出主人声文件夹"), value="opt" + ) + opt_ins_root = gr.Textbox( + label=i18n("指定输出非主人声文件夹"), value="opt" + ) + format0 = gr.Radio( + label=i18n("导出文件格式"), + choices=["wav", "flac", "mp3", "m4a"], + value="flac", + interactive=True, + ) + 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, + format0, + ], + [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=["40k", "48k"], + value="40k", + interactive=True, + ) + if_f0_3 = gr.Checkbox( + label="Whether the model has pitch guidance.", + value=True, + interactive=True, + ) + version19 = gr.Radio( + label=i18n("版本"), + choices=["v1", "v2"], + value="v1", + interactive=True, + visible=True, + ) + np7 = gr.Slider( + minimum=0, + maximum=config.n_cpu, + step=1, + label=i18n("提取音高和处理数据使用的CPU进程数"), + value=int(np.ceil(config.n_cpu / 1.5)), + 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=os.path.abspath(os.getcwd()) + "\\datasets\\", + ) + 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, np7], [info1] + ) + with gr.Group(): + step2b = 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(): + 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", + "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=64, + interactive=True, + visible=False, + ) + + f0method8.change( + fn=whethercrepeornah, + inputs=[f0method8], + outputs=[extraction_crepe_hop_length], + ) + but2 = gr.Button(i18n("特征提取"), variant="primary") + info2 = gr.Textbox( + label=i18n("输出信息"), value="", max_lines=8, interactive=False + ) + but2.click( + extract_f0_feature, + [ + gpus6, + np7, + f0method8, + if_f0_3, + exp_dir1, + version19, + extraction_crepe_hop_length, + ], + [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=1, + maximum=50, + step=1, + label=i18n("保存频率save_every_epoch"), + value=5, + interactive=True, + visible=True, + ) + total_epoch11 = gr.Slider( + minimum=1, + maximum=10000, + step=1, + label=i18n("总训练轮数total_epoch"), + value=20, + interactive=True, + ) + batch_size12 = gr.Slider( + minimum=1, + maximum=40, + step=1, + label=i18n("每张显卡的batch_size"), + value=default_batch_size, + interactive=True, + ) + if_save_latest13 = gr.Checkbox( + label="Whether to save only the latest .ckpt file to save hard drive 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, + ) + with gr.Row(): + pretrained_G14 = gr.Textbox( + lines=2, + label=i18n("加载预训练底模G路径"), + value="pretrained/f0G40k.pth", + interactive=True, + ) + pretrained_D15 = gr.Textbox( + lines=2, + 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, version19], + [pretrained_G14, pretrained_D15], + ) + version19.change( + change_version19, + [sr2, if_f0_3, version19], + [pretrained_G14, pretrained_D15, sr2], + ) + ### if f0_3 put here + if_f0_3.change( + fn=change_f0, + inputs=[ + if_f0_3, + sr2, + version19, + step2b, + gpus6, + gpu_info9, + extraction_crepe_hop_length, + but2, + info2, + ], + outputs=[ + f0method8, + pretrained_G14, + pretrained_D15, + step2b, + gpus6, + gpu_info9, + extraction_crepe_hop_length, + but2, + info2, + ], + ) + if_f0_3.change( + fn=whethercrepeornah, + inputs=[f0method8], + outputs=[extraction_crepe_hop_length], + ) + 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, + ) + butstop = gr.Button( + "Stop Training", + variant="primary", + visible=False, + ) + but3 = gr.Button(i18n("训练模型"), 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(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) + + if_save_every_weights18.change( + fn=stepdisplay, + inputs=[if_save_every_weights18], + outputs=[save_epoch10], + ) + + 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, + # ) + + 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, + placeholder="Path to your model A.", + ) + ckpt_b = gr.Textbox( + label=i18n("B模型路径"), + value="", + interactive=True, + placeholder="Path to your model B.", + ) + 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=["40k", "48k"], + value="40k", + interactive=True, + ) + if_f0_ = gr.Checkbox( + label="Whether the model has pitch guidance.", + value=True, + interactive=True, + ) + info__ = gr.Textbox( + label=i18n("要置入的模型信息"), + value="", + max_lines=8, + interactive=True, + placeholder="Model information to be placed.", + ) + name_to_save0 = gr.Textbox( + label=i18n("保存的模型名不带后缀"), + value="", + placeholder="Name for saving.", + max_lines=1, + interactive=True, + ) + version_2 = gr.Radio( + label=i18n("模型版本型号"), + choices=["v1", "v2"], + value="v1", + 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, + version_2, + ], + 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"), + placeholder="Path to your Model.", + value="", + interactive=True, + ) + info_ = gr.Textbox( + label=i18n("要改的模型信息"), + value="", + max_lines=8, + interactive=True, + placeholder="Model information to be changed.", + ) + name_to_save1 = gr.Textbox( + label=i18n("保存的文件名, 默认空为和源文件同名"), + placeholder="Either leave empty or put in the Name of the Model to be saved.", + 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, + placeholder="Model path here.", + ) + 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( + lines=3, + label=i18n("模型路径"), + value=os.path.abspath(os.getcwd()).replace("\\", "/") + + "/logs/[YOUR_MODEL]/G_23333.pth", + interactive=True, + ) + save_name = gr.Textbox( + label=i18n("保存名"), + value="", + interactive=True, + placeholder="Your filename here.", + ) + sr__ = gr.Radio( + label=i18n("目标采样率"), + choices=["32k", "40k", "48k"], + value="40k", + interactive=True, + ) + if_f0__ = gr.Checkbox( + label="Whether the model has pitch guidance.", + value=True, + interactive=True, + ) + version_1 = gr.Radio( + label=i18n("模型版本型号"), + choices=["v1", "v2"], + value="v2", + interactive=True, + ) + info___ = gr.Textbox( + label=i18n("要置入的模型信息"), + value="", + max_lines=8, + interactive=True, + placeholder="Model info here.", + ) + 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__, version_1] + ) + but9.click( + extract_small_model, + [ckpt_path2, save_name, sr__, if_f0__, info___, version_1], + 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, + placeholder="RVC model path.", + ) + with gr.Row(): + onnx_dir = gr.Textbox( + label=i18n("Onnx输出路径"), + value="", + interactive=True, + placeholder="Onnx model output path.", + ) + with gr.Row(): + infoOnnx = gr.Label(label="info") + with gr.Row(): + butOnnx = gr.Button(i18n("导出Onnx模型"), variant="primary") + butOnnx.click(export_onnx, [ckpt_dir, onnx_dir], infoOnnx) + + tab_faq = i18n("常见问题解答") + with gr.TabItem(tab_faq): + try: + if tab_faq == "常见问题解答": + with open("docs/faq.md", "r", encoding="utf8") as f: + info = f.read() + else: + with open("docs/faq_en.md", "r", encoding="utf8") as f: + info = f.read() + gr.Markdown(value=info) + except: + gr.Markdown(traceback.format_exc()) + + # region Mangio Preset Handler Region + def save_preset( + preset_name, + sid0, + vc_transform, + input_audio0, + input_audio1, + f0method, + crepe_hop_length, + filter_radius, + file_index1, + file_index2, + index_rate, + resample_sr, + rms_mix_rate, + protect, + f0_file, + ): + data = None + with open("../inference-presets.json", "r") as file: + data = json.load(file) + preset_json = { + "name": preset_name, + "model": sid0, + "transpose": vc_transform, + "audio_file": input_audio0, + "auto_audio_file": input_audio1, + "f0_method": f0method, + "crepe_hop_length": crepe_hop_length, + "median_filtering": filter_radius, + "feature_path": file_index1, + "auto_feature_path": file_index2, + "search_feature_ratio": index_rate, + "resample": resample_sr, + "volume_envelope": rms_mix_rate, + "protect_voiceless": protect, + "f0_file_path": f0_file, + } + data["presets"].append(preset_json) + with open("../inference-presets.json", "w") as file: + json.dump(data, file) + file.flush() + print("Saved Preset %s into inference-presets.json!" % preset_name) + + def on_preset_changed(preset_name): + print("Changed Preset to %s!" % preset_name) + data = None + with open("../inference-presets.json", "r") as file: + data = json.load(file) + + print("Searching for " + preset_name) + returning_preset = None + for preset in data["presets"]: + if preset["name"] == preset_name: + print("Found a preset") + returning_preset = preset + # return all new input values + return ( + # returning_preset['model'], + # returning_preset['transpose'], + # returning_preset['audio_file'], + # returning_preset['f0_method'], + # returning_preset['crepe_hop_length'], + # returning_preset['median_filtering'], + # returning_preset['feature_path'], + # returning_preset['auto_feature_path'], + # returning_preset['search_feature_ratio'], + # returning_preset['resample'], + # returning_preset['volume_envelope'], + # returning_preset['protect_voiceless'], + # returning_preset['f0_file_path'] + ) + + # Preset State Changes + + # This click calls save_preset that saves the preset into inference-presets.json with the preset name + # mangio_preset_save_btn.click( + # fn=save_preset, + # inputs=[ + # mangio_preset_name_save, + # sid0, + # vc_transform0, + # input_audio0, + # f0method0, + # crepe_hop_length, + # filter_radius0, + # file_index1, + # file_index2, + # index_rate1, + # resample_sr0, + # rms_mix_rate0, + # protect0, + # f0_file + # ], + # outputs=[] + # ) + + # mangio_preset.change( + # on_preset_changed, + # inputs=[ + # # Pass inputs here + # mangio_preset + # ], + # outputs=[ + # # Pass Outputs here. These refer to the gradio elements that we want to directly change + # # sid0, + # # vc_transform0, + # # input_audio0, + # # f0method0, + # # crepe_hop_length, + # # filter_radius0, + # # file_index1, + # # file_index2, + # # index_rate1, + # # resample_sr0, + # # rms_mix_rate0, + # # protect0, + # # f0_file + # ] + # ) + # endregion + + # 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 ( + 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) + 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=False, + ) + +# endregion