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| import subprocess, torch, os, traceback, sys, warnings, shutil, numpy as np | |
| from mega import Mega | |
| os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1" | |
| import threading | |
| from time import sleep | |
| from subprocess import Popen | |
| import faiss | |
| from random import shuffle | |
| import json, datetime, requests | |
| from gtts import gTTS | |
| now_dir = os.getcwd() | |
| sys.path.append(now_dir) | |
| tmp = os.path.join(now_dir, "TEMP") | |
| shutil.rmtree(tmp, ignore_errors=True) | |
| shutil.rmtree("%s/runtime/Lib/site-packages/infer_pack" % (now_dir), ignore_errors=True) | |
| os.makedirs(tmp, exist_ok=True) | |
| os.makedirs(os.path.join(now_dir, "logs"), exist_ok=True) | |
| os.makedirs(os.path.join(now_dir, "weights"), exist_ok=True) | |
| os.environ["TEMP"] = tmp | |
| warnings.filterwarnings("ignore") | |
| torch.manual_seed(114514) | |
| from i18n import I18nAuto | |
| import edge_tts, asyncio | |
| from ilariatts import tts_order_voice | |
| language_dict = tts_order_voice | |
| ilariavoices = language_dict.keys() | |
| import signal | |
| import math | |
| from utils import load_audio, CSVutil | |
| global DoFormant, Quefrency, Timbre | |
| if not os.path.isdir('csvdb/'): | |
| os.makedirs('csvdb') | |
| frmnt, stp = open("csvdb/formanting.csv", 'w'), open("csvdb/stop.csv", 'w') | |
| frmnt.close() | |
| stp.close() | |
| try: | |
| DoFormant, Quefrency, Timbre = CSVutil('csvdb/formanting.csv', 'r', 'formanting') | |
| DoFormant = ( | |
| lambda DoFormant: True if DoFormant.lower() == 'true' else (False if DoFormant.lower() == 'false' else DoFormant) | |
| )(DoFormant) | |
| except (ValueError, TypeError, IndexError): | |
| DoFormant, Quefrency, Timbre = False, 1.0, 1.0 | |
| CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, Quefrency, Timbre) | |
| def download_models(): | |
| # Download hubert base model if not present | |
| if not os.path.isfile('./hubert_base.pt'): | |
| response = requests.get('https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt') | |
| if response.status_code == 200: | |
| with open('./hubert_base.pt', 'wb') as f: | |
| f.write(response.content) | |
| print("Downloaded hubert base model file successfully. File saved to ./hubert_base.pt.") | |
| else: | |
| raise Exception("Failed to download hubert base model file. Status code: " + str(response.status_code) + ".") | |
| # Download rmvpe model if not present | |
| if not os.path.isfile('./rmvpe.pt'): | |
| response = requests.get('https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/rmvpe.pt?download=true') | |
| if response.status_code == 200: | |
| with open('./rmvpe.pt', 'wb') as f: | |
| f.write(response.content) | |
| print("Downloaded rmvpe model file successfully. File saved to ./rmvpe.pt.") | |
| else: | |
| raise Exception("Failed to download rmvpe model file. Status code: " + str(response.status_code) + ".") | |
| download_models() | |
| print("\n-------------------------------\nRVC v2 Easy GUI\n-------------------------------\n") | |
| def formant_apply(qfrency, tmbre): | |
| Quefrency = qfrency | |
| Timbre = tmbre | |
| DoFormant = True | |
| CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, qfrency, tmbre) | |
| return ({"value": Quefrency, "__type__": "update"}, {"value": Timbre, "__type__": "update"}) | |
| def get_fshift_presets(): | |
| fshift_presets_list = [] | |
| for dirpath, _, filenames in os.walk("./formantshiftcfg/"): | |
| for filename in filenames: | |
| if filename.endswith(".txt"): | |
| fshift_presets_list.append(os.path.join(dirpath,filename).replace('\\','/')) | |
| if len(fshift_presets_list) > 0: | |
| return fshift_presets_list | |
| else: | |
| return '' | |
| def formant_enabled(cbox, qfrency, tmbre, frmntapply, formantpreset, formant_refresh_button): | |
| if (cbox): | |
| DoFormant = True | |
| CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, qfrency, tmbre) | |
| #print(f"is checked? - {cbox}\ngot {DoFormant}") | |
| return ( | |
| {"value": True, "__type__": "update"}, | |
| {"visible": True, "__type__": "update"}, | |
| {"visible": True, "__type__": "update"}, | |
| {"visible": True, "__type__": "update"}, | |
| {"visible": True, "__type__": "update"}, | |
| {"visible": True, "__type__": "update"}, | |
| ) | |
| else: | |
| DoFormant = False | |
| CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, qfrency, tmbre) | |
| #print(f"is checked? - {cbox}\ngot {DoFormant}") | |
| return ( | |
| {"value": False, "__type__": "update"}, | |
| {"visible": False, "__type__": "update"}, | |
| {"visible": False, "__type__": "update"}, | |
| {"visible": False, "__type__": "update"}, | |
| {"visible": False, "__type__": "update"}, | |
| {"visible": False, "__type__": "update"}, | |
| {"visible": False, "__type__": "update"}, | |
| ) | |
| def preset_apply(preset, qfer, tmbr): | |
| if str(preset) != '': | |
| with open(str(preset), 'r') as p: | |
| content = p.readlines() | |
| qfer, tmbr = content[0].split('\n')[0], content[1] | |
| formant_apply(qfer, tmbr) | |
| else: | |
| pass | |
| return ({"value": qfer, "__type__": "update"}, {"value": tmbr, "__type__": "update"}) | |
| def update_fshift_presets(preset, qfrency, tmbre): | |
| qfrency, tmbre = preset_apply(preset, qfrency, tmbre) | |
| if (str(preset) != ''): | |
| with open(str(preset), 'r') as p: | |
| content = p.readlines() | |
| qfrency, tmbre = content[0].split('\n')[0], content[1] | |
| formant_apply(qfrency, tmbre) | |
| else: | |
| pass | |
| return ( | |
| {"choices": get_fshift_presets(), "__type__": "update"}, | |
| {"value": qfrency, "__type__": "update"}, | |
| {"value": tmbre, "__type__": "update"}, | |
| ) | |
| i18n = I18nAuto(language="pt_BR") | |
| #i18n.print() | |
| # 判断是否有能用来训练和加速推理的N卡 | |
| ngpu = torch.cuda.device_count() | |
| gpu_infos = [] | |
| mem = [] | |
| if (not torch.cuda.is_available()) or ngpu == 0: | |
| if_gpu_ok = False | |
| else: | |
| if_gpu_ok = False | |
| for i in range(ngpu): | |
| gpu_name = torch.cuda.get_device_name(i) | |
| if ( | |
| "10" in gpu_name | |
| or "16" in gpu_name | |
| or "20" in gpu_name | |
| or "30" in gpu_name | |
| or "40" in gpu_name | |
| or "A2" in gpu_name.upper() | |
| or "A3" in gpu_name.upper() | |
| or "A4" in gpu_name.upper() | |
| or "P4" in gpu_name.upper() | |
| or "A50" in gpu_name.upper() | |
| or "A60" in gpu_name.upper() | |
| or "70" in gpu_name | |
| or "80" in gpu_name | |
| or "90" in gpu_name | |
| or "M4" in gpu_name.upper() | |
| or "T4" in gpu_name.upper() | |
| or "TITAN" in gpu_name.upper() | |
| ): # A10#A100#V100#A40#P40#M40#K80#A4500 | |
| if_gpu_ok = True # 至少有一张能用的N卡 | |
| gpu_infos.append("%s\t%s" % (i, gpu_name)) | |
| mem.append( | |
| int( | |
| torch.cuda.get_device_properties(i).total_memory | |
| / 1024 | |
| / 1024 | |
| / 1024 | |
| + 0.4 | |
| ) | |
| ) | |
| if if_gpu_ok == True and len(gpu_infos) > 0: | |
| gpu_info = "\n".join(gpu_infos) | |
| default_batch_size = min(mem) // 2 | |
| else: | |
| gpu_info = i18n("很遗憾您这没有能用的显卡来支持您训练") | |
| default_batch_size = 1 | |
| gpus = "-".join([i[0] for i in gpu_infos]) | |
| from lib.infer_pack.models import ( | |
| SynthesizerTrnMs256NSFsid, | |
| SynthesizerTrnMs256NSFsid_nono, | |
| SynthesizerTrnMs768NSFsid, | |
| SynthesizerTrnMs768NSFsid_nono, | |
| ) | |
| import soundfile as sf | |
| from fairseq import checkpoint_utils | |
| import gradio as gr | |
| import logging | |
| from vc_infer_pipeline import VC | |
| from config import Config | |
| config = Config() | |
| # from trainset_preprocess_pipeline import PreProcess | |
| logging.getLogger("numba").setLevel(logging.WARNING) | |
| hubert_model = None | |
| def load_hubert(): | |
| global hubert_model | |
| models, _, _ = checkpoint_utils.load_model_ensemble_and_task( | |
| ["hubert_base.pt"], | |
| suffix="", | |
| ) | |
| hubert_model = models[0] | |
| hubert_model = hubert_model.to(config.device) | |
| if config.is_half: | |
| hubert_model = hubert_model.half() | |
| else: | |
| hubert_model = hubert_model.float() | |
| hubert_model.eval() | |
| weight_root = "weights" | |
| index_root = "logs" | |
| names = [] | |
| for name in os.listdir(weight_root): | |
| if name.endswith(".pth"): | |
| names.append(name) | |
| index_paths = [] | |
| for root, dirs, files in os.walk(index_root, topdown=False): | |
| for name in files: | |
| if name.endswith(".index") and "trained" not in name: | |
| index_paths.append("%s/%s" % (root, name)) | |
| def vc_single( | |
| sid, | |
| input_audio_path, | |
| f0_up_key, | |
| f0_file, | |
| f0_method, | |
| file_index, | |
| #file_index2, | |
| # file_big_npy, | |
| index_rate, | |
| filter_radius, | |
| resample_sr, | |
| rms_mix_rate, | |
| protect, | |
| crepe_hop_length, | |
| ): # spk_item, input_audio0, vc_transform0,f0_file,f0method0 | |
| global tgt_sr, net_g, vc, hubert_model, version | |
| if input_audio_path is None: | |
| return "You need to upload an audio", None | |
| f0_up_key = int(f0_up_key) | |
| try: | |
| audio = load_audio(input_audio_path, 16000, DoFormant, Quefrency, Timbre) | |
| audio_max = np.abs(audio).max() / 0.95 | |
| if audio_max > 1: | |
| audio /= audio_max | |
| times = [0, 0, 0] | |
| if hubert_model == None: | |
| load_hubert() | |
| if_f0 = cpt.get("f0", 1) | |
| file_index = ( | |
| ( | |
| file_index.strip(" ") | |
| .strip('"') | |
| .strip("\n") | |
| .strip('"') | |
| .strip(" ") | |
| .replace("trained", "added") | |
| ) | |
| ) # 防止小白写错，自动帮他替换掉 | |
| # file_big_npy = ( | |
| # file_big_npy.strip(" ").strip('"').strip("\n").strip('"').strip(" ") | |
| # ) | |
| audio_opt = vc.pipeline( | |
| hubert_model, | |
| net_g, | |
| sid, | |
| audio, | |
| input_audio_path, | |
| times, | |
| f0_up_key, | |
| f0_method, | |
| file_index, | |
| # file_big_npy, | |
| index_rate, | |
| if_f0, | |
| filter_radius, | |
| tgt_sr, | |
| resample_sr, | |
| rms_mix_rate, | |
| version, | |
| protect, | |
| crepe_hop_length, | |
| f0_file=f0_file, | |
| ) | |
| if resample_sr >= 16000 and tgt_sr != resample_sr: | |
| tgt_sr = resample_sr | |
| index_info = ( | |
| "Using index:%s." % file_index | |
| if os.path.exists(file_index) | |
| else "Index not used." | |
| ) | |
| return "Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss" % ( | |
| index_info, | |
| times[0], | |
| times[1], | |
| times[2], | |
| ), (tgt_sr, audio_opt) | |
| except: | |
| info = traceback.format_exc() | |
| print(info) | |
| return info, (None, None) | |
| def vc_multi( | |
| sid, | |
| dir_path, | |
| opt_root, | |
| paths, | |
| f0_up_key, | |
| f0_method, | |
| file_index, | |
| file_index2, | |
| # file_big_npy, | |
| index_rate, | |
| filter_radius, | |
| resample_sr, | |
| rms_mix_rate, | |
| protect, | |
| format1, | |
| crepe_hop_length, | |
| ): | |
| try: | |
| dir_path = ( | |
| dir_path.strip(" ").strip('"').strip("\n").strip('"').strip(" ") | |
| ) # 防止小白拷路径头尾带了空格和"和回车 | |
| opt_root = opt_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ") | |
| os.makedirs(opt_root, exist_ok=True) | |
| try: | |
| if dir_path != "": | |
| paths = [os.path.join(dir_path, name) for name in os.listdir(dir_path)] | |
| else: | |
| paths = [path.name for path in paths] | |
| except: | |
| traceback.print_exc() | |
| paths = [path.name for path in paths] | |
| infos = [] | |
| for path in paths: | |
| info, opt = vc_single( | |
| sid, | |
| path, | |
| f0_up_key, | |
| None, | |
| f0_method, | |
| file_index, | |
| # file_big_npy, | |
| index_rate, | |
| filter_radius, | |
| resample_sr, | |
| rms_mix_rate, | |
| protect, | |
| crepe_hop_length | |
| ) | |
| if "Success" in info: | |
| try: | |
| tgt_sr, audio_opt = opt | |
| if format1 in ["wav", "flac"]: | |
| sf.write( | |
| "%s/%s.%s" % (opt_root, os.path.basename(path), format1), | |
| audio_opt, | |
| tgt_sr, | |
| ) | |
| else: | |
| path = "%s/%s.wav" % (opt_root, os.path.basename(path)) | |
| sf.write( | |
| path, | |
| audio_opt, | |
| tgt_sr, | |
| ) | |
| if os.path.exists(path): | |
| os.system( | |
| "ffmpeg -i %s -vn %s -q:a 2 -y" | |
| % (path, path[:-4] + ".%s" % format1) | |
| ) | |
| except: | |
| info += traceback.format_exc() | |
| infos.append("%s->%s" % (os.path.basename(path), info)) | |
| yield "\n".join(infos) | |
| yield "\n".join(infos) | |
| except: | |
| yield traceback.format_exc() | |
| # 一个选项卡全局只能有一个音色 | |
| def get_vc(sid): | |
| global n_spk, tgt_sr, net_g, vc, cpt, version | |
| if sid == "" or sid == []: | |
| global hubert_model | |
| if hubert_model != None: # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的 | |
| print("clean_empty_cache") | |
| del net_g, n_spk, vc, hubert_model, tgt_sr # ,cpt | |
| hubert_model = net_g = n_spk = vc = hubert_model = tgt_sr = None | |
| if torch.cuda.is_available(): | |
| torch.cuda.empty_cache() | |
| ###楼下不这么折腾清理不干净 | |
| if_f0 = cpt.get("f0", 1) | |
| version = cpt.get("version", "v1") | |
| if version == "v1": | |
| if if_f0 == 1: | |
| net_g = SynthesizerTrnMs256NSFsid( | |
| *cpt["config"], is_half=config.is_half | |
| ) | |
| else: | |
| net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) | |
| elif version == "v2": | |
| if if_f0 == 1: | |
| net_g = SynthesizerTrnMs768NSFsid( | |
| *cpt["config"], is_half=config.is_half | |
| ) | |
| else: | |
| net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"]) | |
| del net_g, cpt | |
| if torch.cuda.is_available(): | |
| torch.cuda.empty_cache() | |
| cpt = None | |
| return {"visible": False, "__type__": "update"} | |
| person = "%s/%s" % (weight_root, sid) | |
| print("loading %s" % person) | |
| cpt = torch.load(person, map_location="cpu") | |
| tgt_sr = cpt["config"][-1] | |
| cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk | |
| if_f0 = cpt.get("f0", 1) | |
| version = cpt.get("version", "v1") | |
| if version == "v1": | |
| if if_f0 == 1: | |
| net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half) | |
| else: | |
| net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) | |
| elif version == "v2": | |
| if if_f0 == 1: | |
| net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half) | |
| else: | |
| net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"]) | |
| del net_g.enc_q | |
| print(net_g.load_state_dict(cpt["weight"], strict=False)) | |
| net_g.eval().to(config.device) | |
| if config.is_half: | |
| net_g = net_g.half() | |
| else: | |
| net_g = net_g.float() | |
| vc = VC(tgt_sr, config) | |
| n_spk = cpt["config"][-3] | |
| return {"visible": False, "maximum": n_spk, "__type__": "update"} | |
| def change_choices(): | |
| names = [] | |
| for name in os.listdir(weight_root): | |
| if name.endswith(".pth"): | |
| names.append(name) | |
| index_paths = [] | |
| for root, dirs, files in os.walk(index_root, topdown=False): | |
| for name in files: | |
| if name.endswith(".index") and "trained" not in name: | |
| index_paths.append("%s/%s" % (root, name)) | |
| return {"choices": sorted(names), "__type__": "update"}, { | |
| "choices": sorted(index_paths), | |
| "__type__": "update", | |
| } | |
| def clean(): | |
| return {"value": "", "__type__": "update"} | |
| sr_dict = { | |
| "32k": 32000, | |
| "40k": 40000, | |
| "48k": 48000, | |
| } | |
| def if_done(done, p): | |
| while 1: | |
| if p.poll() == None: | |
| sleep(0.5) | |
| else: | |
| break | |
| done[0] = True | |
| def if_done_multi(done, ps): | |
| while 1: | |
| # poll==None代表进程未结束 | |
| # 只要有一个进程未结束都不停 | |
| flag = 1 | |
| for p in ps: | |
| if p.poll() == None: | |
| flag = 0 | |
| sleep(0.5) | |
| break | |
| if flag == 1: | |
| break | |
| done[0] = True | |
| def preprocess_dataset(trainset_dir, exp_dir, sr, n_p): | |
| sr = sr_dict[sr] | |
| os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True) | |
| f = open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "w") | |
| f.close() | |
| cmd = ( | |
| config.python_cmd | |
| + " trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s " | |
| % (trainset_dir, sr, n_p, now_dir, exp_dir) | |
| + str(config.noparallel) | |
| ) | |
| print(cmd) | |
| p = Popen(cmd, shell=True) # , stdin=PIPE, stdout=PIPE,stderr=PIPE,cwd=now_dir | |
| ###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读 | |
| done = [False] | |
| threading.Thread( | |
| target=if_done, | |
| args=( | |
| done, | |
| p, | |
| ), | |
| ).start() | |
| while 1: | |
| with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f: | |
| yield (f.read()) | |
| sleep(1) | |
| if done[0] == True: | |
| break | |
| with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f: | |
| log = f.read() | |
| print(log) | |
| yield log | |
| # but2.click(extract_f0,[gpus6,np7,f0method8,if_f0_3,trainset_dir4],[info2]) | |
| def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, echl): | |
| gpus = gpus.split("-") | |
| os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True) | |
| f = open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "w") | |
| f.close() | |
| if if_f0: | |
| cmd = config.python_cmd + " extract_f0_print.py %s/logs/%s %s %s %s" % ( | |
| now_dir, | |
| exp_dir, | |
| n_p, | |
| f0method, | |
| echl, | |
| ) | |
| print(cmd) | |
| p = Popen(cmd, shell=True, cwd=now_dir) # , stdin=PIPE, stdout=PIPE,stderr=PIPE | |
| ###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读 | |
| done = [False] | |
| threading.Thread( | |
| target=if_done, | |
| args=( | |
| done, | |
| p, | |
| ), | |
| ).start() | |
| while 1: | |
| with open( | |
| "%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r" | |
| ) as f: | |
| yield (f.read()) | |
| sleep(1) | |
| if done[0] == True: | |
| break | |
| with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f: | |
| log = f.read() | |
| print(log) | |
| yield log | |
| ####对不同part分别开多进程 | |
| """ | |
| n_part=int(sys.argv[1]) | |
| i_part=int(sys.argv[2]) | |
| i_gpu=sys.argv[3] | |
| exp_dir=sys.argv[4] | |
| os.environ["CUDA_VISIBLE_DEVICES"]=str(i_gpu) | |
| """ | |
| leng = len(gpus) | |
| ps = [] | |
| for idx, n_g in enumerate(gpus): | |
| cmd = ( | |
| config.python_cmd | |
| + " extract_feature_print.py %s %s %s %s %s/logs/%s %s" | |
| % ( | |
| config.device, | |
| leng, | |
| idx, | |
| n_g, | |
| now_dir, | |
| exp_dir, | |
| version19, | |
| ) | |
| ) | |
| print(cmd) | |
| p = Popen( | |
| cmd, shell=True, cwd=now_dir | |
| ) # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir | |
| ps.append(p) | |
| ###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读 | |
| done = [False] | |
| threading.Thread( | |
| target=if_done_multi, | |
| args=( | |
| done, | |
| ps, | |
| ), | |
| ).start() | |
| while 1: | |
| with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f: | |
| yield (f.read()) | |
| sleep(1) | |
| if done[0] == True: | |
| break | |
| with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f: | |
| log = f.read() | |
| print(log) | |
| yield log | |
| def change_sr2(sr2, if_f0_3, version19): | |
| path_str = "" if version19 == "v1" else "_v2" | |
| f0_str = "f0" if if_f0_3 else "" | |
| if_pretrained_generator_exist = os.access("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK) | |
| if_pretrained_discriminator_exist = os.access("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK) | |
| if (if_pretrained_generator_exist == False): | |
| print("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model") | |
| if (if_pretrained_discriminator_exist == False): | |
| print("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model") | |
| return ( | |
| ("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_generator_exist else "", | |
| ("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_discriminator_exist else "", | |
| {"visible": True, "__type__": "update"} | |
| ) | |
| def change_version19(sr2, if_f0_3, version19): | |
| path_str = "" if version19 == "v1" else "_v2" | |
| f0_str = "f0" if if_f0_3 else "" | |
| if_pretrained_generator_exist = os.access("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK) | |
| if_pretrained_discriminator_exist = os.access("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK) | |
| if (if_pretrained_generator_exist == False): | |
| print("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model") | |
| if (if_pretrained_discriminator_exist == False): | |
| print("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model") | |
| return ( | |
| ("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_generator_exist else "", | |
| ("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_discriminator_exist else "", | |
| ) | |
| def change_f0(if_f0_3, sr2, version19): # f0method8,pretrained_G14,pretrained_D15 | |
| path_str = "" if version19 == "v1" else "_v2" | |
| if_pretrained_generator_exist = os.access("pretrained%s/f0G%s.pth" % (path_str, sr2), os.F_OK) | |
| if_pretrained_discriminator_exist = os.access("pretrained%s/f0D%s.pth" % (path_str, sr2), os.F_OK) | |
| if (if_pretrained_generator_exist == False): | |
| print("pretrained%s/f0G%s.pth" % (path_str, sr2), "not exist, will not use pretrained model") | |
| if (if_pretrained_discriminator_exist == False): | |
| print("pretrained%s/f0D%s.pth" % (path_str, sr2), "not exist, will not use pretrained model") | |
| if if_f0_3: | |
| return ( | |
| {"visible": True, "__type__": "update"}, | |
| "pretrained%s/f0G%s.pth" % (path_str, sr2) if if_pretrained_generator_exist else "", | |
| "pretrained%s/f0D%s.pth" % (path_str, sr2) if if_pretrained_discriminator_exist else "", | |
| ) | |
| return ( | |
| {"visible": False, "__type__": "update"}, | |
| ("pretrained%s/G%s.pth" % (path_str, sr2)) if if_pretrained_generator_exist else "", | |
| ("pretrained%s/D%s.pth" % (path_str, sr2)) if if_pretrained_discriminator_exist else "", | |
| ) | |
| global log_interval | |
| def set_log_interval(exp_dir, batch_size12): | |
| log_interval = 1 | |
| folder_path = os.path.join(exp_dir, "1_16k_wavs") | |
| if os.path.exists(folder_path) and os.path.isdir(folder_path): | |
| wav_files = [f for f in os.listdir(folder_path) if f.endswith(".wav")] | |
| if wav_files: | |
| sample_size = len(wav_files) | |
| log_interval = math.ceil(sample_size / batch_size12) | |
| if log_interval > 1: | |
| log_interval += 1 | |
| return log_interval | |
| # but3.click(click_train,[exp_dir1,sr2,if_f0_3,save_epoch10,total_epoch11,batch_size12,if_save_latest13,pretrained_G14,pretrained_D15,gpus16]) | |
| def click_train( | |
| exp_dir1, | |
| sr2, | |
| if_f0_3, | |
| spk_id5, | |
| save_epoch10, | |
| total_epoch11, | |
| batch_size12, | |
| if_save_latest13, | |
| pretrained_G14, | |
| pretrained_D15, | |
| gpus16, | |
| if_cache_gpu17, | |
| if_save_every_weights18, | |
| version19, | |
| ): | |
| CSVutil('csvdb/stop.csv', 'w+', 'formanting', False) | |
| # 生成filelist | |
| exp_dir = "%s/logs/%s" % (now_dir, exp_dir1) | |
| os.makedirs(exp_dir, exist_ok=True) | |
| gt_wavs_dir = "%s/0_gt_wavs" % (exp_dir) | |
| feature_dir = ( | |
| "%s/3_feature256" % (exp_dir) | |
| if version19 == "v1" | |
| else "%s/3_feature768" % (exp_dir) | |
| ) | |
| log_interval = set_log_interval(exp_dir, batch_size12) | |
| if if_f0_3: | |
| f0_dir = "%s/2a_f0" % (exp_dir) | |
| f0nsf_dir = "%s/2b-f0nsf" % (exp_dir) | |
| names = ( | |
| set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) | |
| & set([name.split(".")[0] for name in os.listdir(feature_dir)]) | |
| & set([name.split(".")[0] for name in os.listdir(f0_dir)]) | |
| & set([name.split(".")[0] for name in os.listdir(f0nsf_dir)]) | |
| ) | |
| else: | |
| names = set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) & set( | |
| [name.split(".")[0] for name in os.listdir(feature_dir)] | |
| ) | |
| opt = [] | |
| for name in names: | |
| if if_f0_3: | |
| opt.append( | |
| "%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s" | |
| % ( | |
| gt_wavs_dir.replace("\\", "\\\\"), | |
| name, | |
| feature_dir.replace("\\", "\\\\"), | |
| name, | |
| f0_dir.replace("\\", "\\\\"), | |
| name, | |
| f0nsf_dir.replace("\\", "\\\\"), | |
| name, | |
| spk_id5, | |
| ) | |
| ) | |
| else: | |
| opt.append( | |
| "%s/%s.wav|%s/%s.npy|%s" | |
| % ( | |
| gt_wavs_dir.replace("\\", "\\\\"), | |
| name, | |
| feature_dir.replace("\\", "\\\\"), | |
| name, | |
| spk_id5, | |
| ) | |
| ) | |
| fea_dim = 256 if version19 == "v1" else 768 | |
| if if_f0_3: | |
| for _ in range(2): | |
| opt.append( | |
| "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s" | |
| % (now_dir, sr2, now_dir, fea_dim, now_dir, now_dir, spk_id5) | |
| ) | |
| else: | |
| for _ in range(2): | |
| opt.append( | |
| "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s" | |
| % (now_dir, sr2, now_dir, fea_dim, spk_id5) | |
| ) | |
| shuffle(opt) | |
| with open("%s/filelist.txt" % exp_dir, "w") as f: | |
| f.write("\n".join(opt)) | |
| print("write filelist done") | |
| # 生成config#无需生成config | |
| # cmd = python_cmd + " train_nsf_sim_cache_sid_load_pretrain.py -e mi-test -sr 40k -f0 1 -bs 4 -g 0 -te 10 -se 5 -pg pretrained/f0G40k.pth -pd pretrained/f0D40k.pth -l 1 -c 0" | |
| print("use gpus:", gpus16) | |
| if pretrained_G14 == "": | |
| print("no pretrained Generator") | |
| if pretrained_D15 == "": | |
| print("no pretrained Discriminator") | |
| if gpus16: | |
| cmd = ( | |
| config.python_cmd | |
| + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s -li %s" | |
| % ( | |
| exp_dir1, | |
| sr2, | |
| 1 if if_f0_3 else 0, | |
| batch_size12, | |
| gpus16, | |
| total_epoch11, | |
| save_epoch10, | |
| ("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "", | |
| ("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "", | |
| 1 if if_save_latest13 == True else 0, | |
| 1 if if_cache_gpu17 == True else 0, | |
| 1 if if_save_every_weights18 == True else 0, | |
| version19, | |
| log_interval, | |
| ) | |
| ) | |
| else: | |
| cmd = ( | |
| config.python_cmd | |
| + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s -li %s" | |
| % ( | |
| exp_dir1, | |
| sr2, | |
| 1 if if_f0_3 else 0, | |
| batch_size12, | |
| total_epoch11, | |
| save_epoch10, | |
| ("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "\b", | |
| ("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "\b", | |
| 1 if if_save_latest13 == True else 0, | |
| 1 if if_cache_gpu17 == True else 0, | |
| 1 if if_save_every_weights18 == True else 0, | |
| version19, | |
| log_interval, | |
| ) | |
| ) | |
| print(cmd) | |
| p = Popen(cmd, shell=True, cwd=now_dir) | |
| global PID | |
| PID = p.pid | |
| p.wait() | |
| return ("训练结束, 您可查看控制台训练日志或实验文件夹下的train.log", {"visible": False, "__type__": "update"}, {"visible": True, "__type__": "update"}) | |
| # but4.click(train_index, [exp_dir1], info3) | |
| def train_index(exp_dir1, version19): | |
| exp_dir = "%s/logs/%s" % (now_dir, exp_dir1) | |
| os.makedirs(exp_dir, exist_ok=True) | |
| feature_dir = ( | |
| "%s/3_feature256" % (exp_dir) | |
| if version19 == "v1" | |
| else "%s/3_feature768" % (exp_dir) | |
| ) | |
| if os.path.exists(feature_dir) == False: | |
| return "请先进行特征提取!" | |
| listdir_res = list(os.listdir(feature_dir)) | |
| if len(listdir_res) == 0: | |
| return "请先进行特征提取！" | |
| npys = [] | |
| for name in sorted(listdir_res): | |
| phone = np.load("%s/%s" % (feature_dir, name)) | |
| npys.append(phone) | |
| big_npy = np.concatenate(npys, 0) | |
| big_npy_idx = np.arange(big_npy.shape[0]) | |
| np.random.shuffle(big_npy_idx) | |
| big_npy = big_npy[big_npy_idx] | |
| np.save("%s/total_fea.npy" % exp_dir, big_npy) | |
| # n_ivf = big_npy.shape[0] // 39 | |
| n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39) | |
| infos = [] | |
| infos.append("%s,%s" % (big_npy.shape, n_ivf)) | |
| yield "\n".join(infos) | |
| index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf) | |
| # index = faiss.index_factory(256if version19=="v1"else 768, "IVF%s,PQ128x4fs,RFlat"%n_ivf) | |
| infos.append("training") | |
| yield "\n".join(infos) | |
| index_ivf = faiss.extract_index_ivf(index) # | |
| index_ivf.nprobe = 1 | |
| index.train(big_npy) | |
| faiss.write_index( | |
| index, | |
| "%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index" | |
| % (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19), | |
| ) | |
| # faiss.write_index(index, '%s/trained_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19)) | |
| infos.append("adding") | |
| yield "\n".join(infos) | |
| batch_size_add = 8192 | |
| for i in range(0, big_npy.shape[0], batch_size_add): | |
| index.add(big_npy[i : i + batch_size_add]) | |
| faiss.write_index( | |
| index, | |
| "%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index" | |
| % (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19), | |
| ) | |
| infos.append( | |
| "成功构建索引，added_IVF%s_Flat_nprobe_%s_%s_%s.index" | |
| % (n_ivf, index_ivf.nprobe, exp_dir1, version19) | |
| ) | |
| # faiss.write_index(index, '%s/added_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19)) | |
| # infos.append("成功构建索引，added_IVF%s_Flat_FastScan_%s.index"%(n_ivf,version19)) | |
| yield "\n".join(infos) | |
| # but5.click(train1key, [exp_dir1, sr2, if_f0_3, trainset_dir4, spk_id5, gpus6, np7, f0method8, save_epoch10, total_epoch11, batch_size12, if_save_latest13, pretrained_G14, pretrained_D15, gpus16, if_cache_gpu17], info3) | |
| def train1key( | |
| exp_dir1, | |
| sr2, | |
| if_f0_3, | |
| trainset_dir4, | |
| spk_id5, | |
| np7, | |
| f0method8, | |
| save_epoch10, | |
| total_epoch11, | |
| batch_size12, | |
| if_save_latest13, | |
| pretrained_G14, | |
| pretrained_D15, | |
| gpus16, | |
| if_cache_gpu17, | |
| if_save_every_weights18, | |
| version19, | |
| echl | |
| ): | |
| infos = [] | |
| def get_info_str(strr): | |
| infos.append(strr) | |
| return "\n".join(infos) | |
| model_log_dir = "%s/logs/%s" % (now_dir, exp_dir1) | |
| preprocess_log_path = "%s/preprocess.log" % model_log_dir | |
| extract_f0_feature_log_path = "%s/extract_f0_feature.log" % model_log_dir | |
| gt_wavs_dir = "%s/0_gt_wavs" % model_log_dir | |
| feature_dir = ( | |
| "%s/3_feature256" % model_log_dir | |
| if version19 == "v1" | |
| else "%s/3_feature768" % model_log_dir | |
| ) | |
| os.makedirs(model_log_dir, exist_ok=True) | |
| #########step1:处理数据 | |
| open(preprocess_log_path, "w").close() | |
| cmd = ( | |
| config.python_cmd | |
| + " trainset_preprocess_pipeline_print.py %s %s %s %s " | |
| % (trainset_dir4, sr_dict[sr2], np7, model_log_dir) | |
| + str(config.noparallel) | |
| ) | |
| yield get_info_str(i18n("step1:正在处理数据")) | |
| yield get_info_str(cmd) | |
| p = Popen(cmd, shell=True) | |
| p.wait() | |
| with open(preprocess_log_path, "r") as f: | |
| print(f.read()) | |
| #########step2a:提取音高 | |
| open(extract_f0_feature_log_path, "w") | |
| if if_f0_3: | |
| yield get_info_str("step2a:正在提取音高") | |
| cmd = config.python_cmd + " extract_f0_print.py %s %s %s %s" % ( | |
| model_log_dir, | |
| np7, | |
| f0method8, | |
| echl | |
| ) | |
| yield get_info_str(cmd) | |
| p = Popen(cmd, shell=True, cwd=now_dir) | |
| p.wait() | |
| with open(extract_f0_feature_log_path, "r") as f: | |
| print(f.read()) | |
| else: | |
| yield get_info_str(i18n("step2a:无需提取音高")) | |
| #######step2b:提取特征 | |
| yield get_info_str(i18n("step2b:正在提取特征")) | |
| 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] | |
| np.save("%s/total_fea.npy" % model_log_dir, big_npy) | |
| # n_ivf = big_npy.shape[0] // 39 | |
| n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39) | |
| yield get_info_str("%s,%s" % (big_npy.shape, n_ivf)) | |
| index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf) | |
| yield get_info_str("training index") | |
| index_ivf = faiss.extract_index_ivf(index) # | |
| index_ivf.nprobe = 1 | |
| index.train(big_npy) | |
| faiss.write_index( | |
| index, | |
| "%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index" | |
| % (model_log_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19), | |
| ) | |
| yield get_info_str("adding index") | |
| batch_size_add = 8192 | |
| for i in range(0, big_npy.shape[0], batch_size_add): | |
| index.add(big_npy[i : i + batch_size_add]) | |
| faiss.write_index( | |
| index, | |
| "%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index" | |
| % (model_log_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19), | |
| ) | |
| yield get_info_str( | |
| "成功构建索引, added_IVF%s_Flat_nprobe_%s_%s_%s.index" | |
| % (n_ivf, index_ivf.nprobe, exp_dir1, version19) | |
| ) | |
| yield get_info_str(i18n("全流程结束！")) | |
| def whethercrepeornah(radio): | |
| mango = True if radio == 'mangio-crepe' or radio == 'mangio-crepe-tiny' else False | |
| return ({"visible": mango, "__type__": "update"}) | |
| # ckpt_path2.change(change_info_,[ckpt_path2],[sr__,if_f0__]) | |
| def change_info_(ckpt_path): | |
| if ( | |
| os.path.exists(ckpt_path.replace(os.path.basename(ckpt_path), "train.log")) | |
| == False | |
| ): | |
| return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"} | |
| try: | |
| with open( | |
| ckpt_path.replace(os.path.basename(ckpt_path), "train.log"), "r" | |
| ) as f: | |
| info = eval(f.read().strip("\n").split("\n")[0].split("\t")[-1]) | |
| sr, f0 = info["sample_rate"], info["if_f0"] | |
| version = "v2" if ("version" in info and info["version"] == "v2") else "v1" | |
| return sr, str(f0), version | |
| except: | |
| traceback.print_exc() | |
| return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"} | |
| from lib.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM | |
| def export_onnx(ModelPath, ExportedPath, MoeVS=True): | |
| cpt = torch.load(ModelPath, map_location="cpu") | |
| cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk | |
| hidden_channels = 256 if cpt.get("version","v1")=="v1"else 768#cpt["config"][-2] # hidden_channels，为768Vec做准备 | |
| test_phone = torch.rand(1, 200, hidden_channels) # hidden unit | |
| test_phone_lengths = torch.tensor([200]).long() # hidden unit 长度（貌似没啥用） | |
| test_pitch = torch.randint(size=(1, 200), low=5, high=255) # 基频（单位赫兹） | |
| test_pitchf = torch.rand(1, 200) # nsf基频 | |
| test_ds = torch.LongTensor([0]) # 说话人ID | |
| test_rnd = torch.rand(1, 192, 200) # 噪声（加入随机因子） | |
| device = "cpu" # 导出时设备（不影响使用模型） | |
| net_g = SynthesizerTrnMsNSFsidM( | |
| *cpt["config"], is_half=False,version=cpt.get("version","v1") | |
| ) # fp32导出（C++要支持fp16必须手动将内存重新排列所以暂时不用fp16） | |
| net_g.load_state_dict(cpt["weight"], strict=False) | |
| input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"] | |
| output_names = [ | |
| "audio", | |
| ] | |
| # net_g.construct_spkmixmap(n_speaker) 多角色混合轨道导出 | |
| torch.onnx.export( | |
| net_g, | |
| ( | |
| test_phone.to(device), | |
| test_phone_lengths.to(device), | |
| test_pitch.to(device), | |
| test_pitchf.to(device), | |
| test_ds.to(device), | |
| test_rnd.to(device), | |
| ), | |
| ExportedPath, | |
| dynamic_axes={ | |
| "phone": [1], | |
| "pitch": [1], | |
| "pitchf": [1], | |
| "rnd": [2], | |
| }, | |
| do_constant_folding=False, | |
| opset_version=16, | |
| verbose=False, | |
| input_names=input_names, | |
| output_names=output_names, | |
| ) | |
| return "Finished" | |
| #region RVC WebUI App | |
| def get_presets(): | |
| data = None | |
| with open('../inference-presets.json', 'r') as file: | |
| data = json.load(file) | |
| preset_names = [] | |
| for preset in data['presets']: | |
| preset_names.append(preset['name']) | |
| return preset_names | |
| def change_choices2(): | |
| audio_files=[] | |
| for filename in os.listdir("./audios"): | |
| if filename.endswith(('.wav','.mp3','.ogg','.flac','.m4a','.aac','.mp4')): | |
| audio_files.append(os.path.join('./audios',filename).replace('\\', '/')) | |
| return {"choices": sorted(audio_files), "__type__": "update"}, {"__type__": "update"} | |
| audio_files=[] | |
| for filename in os.listdir("./audios"): | |
| if filename.endswith(('.wav','.mp3','.ogg','.flac','.m4a','.aac','.mp4')): | |
| audio_files.append(os.path.join('./audios',filename).replace('\\', '/')) | |
| def get_index(): | |
| if check_for_name() != '': | |
| chosen_model=sorted(names)[0].split(".")[0] | |
| logs_path="./logs/"+chosen_model | |
| if os.path.exists(logs_path): | |
| for file in os.listdir(logs_path): | |
| if file.endswith(".index"): | |
| return os.path.join(logs_path, file) | |
| return '' | |
| else: | |
| return '' | |
| def get_indexes(): | |
| indexes_list=[] | |
| for dirpath, dirnames, filenames in os.walk("./logs/"): | |
| for filename in filenames: | |
| if filename.endswith(".index"): | |
| indexes_list.append(os.path.join(dirpath,filename)) | |
| if len(indexes_list) > 0: | |
| return indexes_list | |
| else: | |
| return '' | |
| def get_name(): | |
| if len(audio_files) > 0: | |
| return sorted(audio_files)[0] | |
| else: | |
| return '' | |
| def save_to_wav(record_button): | |
| if record_button is None: | |
| pass | |
| else: | |
| path_to_file=record_button | |
| new_name = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")+'.wav' | |
| new_path='./audios/'+new_name | |
| shutil.move(path_to_file,new_path) | |
| return new_path | |
| def save_to_wav2(dropbox): | |
| file_path=dropbox.name | |
| shutil.move(file_path,'./audios') | |
| return os.path.join('./audios',os.path.basename(file_path)) | |
| def match_index(sid0): | |
| folder=sid0.split(".")[0] | |
| parent_dir="./logs/"+folder | |
| if os.path.exists(parent_dir): | |
| for filename in os.listdir(parent_dir): | |
| if filename.endswith(".index"): | |
| index_path=os.path.join(parent_dir,filename) | |
| return index_path | |
| else: | |
| return '' | |
| def check_for_name(): | |
| if len(names) > 0: | |
| return sorted(names)[0] | |
| else: | |
| return '' | |
| def download_from_url(url, model): | |
| if url == '': | |
| return "O URL não pode ficar vazio." | |
| if model =='': | |
| return "Você precisa nomear seu modelo. Por exemplo: Meu modelo" | |
| url = url.strip() | |
| zip_dirs = ["zips", "unzips"] | |
| for directory in zip_dirs: | |
| if os.path.exists(directory): | |
| shutil.rmtree(directory) | |
| os.makedirs("zips", exist_ok=True) | |
| os.makedirs("unzips", exist_ok=True) | |
| zipfile = model + '.zip' | |
| zipfile_path = './zips/' + zipfile | |
| try: | |
| if "drive.google.com" in url: | |
| subprocess.run(["gdown", url, "--fuzzy", "-O", zipfile_path]) | |
| elif "mega.nz" in url: | |
| m = Mega() | |
| m.download_url(url, './zips') | |
| else: | |
| subprocess.run(["wget", url, "-O", zipfile_path]) | |
| for filename in os.listdir("./zips"): | |
| if filename.endswith(".zip"): | |
| zipfile_path = os.path.join("./zips/",filename) | |
| shutil.unpack_archive(zipfile_path, "./unzips", 'zip') | |
| else: | |
| return "No zipfile found." | |
| for root, dirs, files in os.walk('./unzips'): | |
| for file in files: | |
| file_path = os.path.join(root, file) | |
| if file.endswith(".index"): | |
| os.mkdir(f'./logs/{model}') | |
| shutil.copy2(file_path,f'./logs/{model}') | |
| elif "G_" not in file and "D_" not in file and file.endswith(".pth"): | |
| shutil.copy(file_path,f'./weights/{model}.pth') | |
| shutil.rmtree("zips") | |
| shutil.rmtree("unzips") | |
| return "Modelo baixado, você pode voltar para a página de inferência!" | |
| except: | |
| return "ERRO - O download falhou. Verifique se o link é válido." | |
| def success_message(face): | |
| return f'{face.name} foi carregado.', 'None' | |
| def mouth(size, face, voice, faces): | |
| if size == 'Half': | |
| size = 2 | |
| else: | |
| size = 1 | |
| if faces == 'None': | |
| character = face.name | |
| else: | |
| if faces == 'Ben Shapiro': | |
| character = '/content/wav2lip-HD/inputs/ben-shapiro-10.mp4' | |
| elif faces == 'Andrew Tate': | |
| character = '/content/wav2lip-HD/inputs/tate-7.mp4' | |
| command = "python inference.py " \ | |
| "--checkpoint_path checkpoints/wav2lip.pth " \ | |
| f"--face {character} " \ | |
| f"--audio {voice} " \ | |
| "--pads 0 20 0 0 " \ | |
| "--outfile /content/wav2lip-HD/outputs/result.mp4 " \ | |
| "--fps 24 " \ | |
| f"--resize_factor {size}" | |
| process = subprocess.Popen(command, shell=True, cwd='/content/wav2lip-HD/Wav2Lip-master') | |
| stdout, stderr = process.communicate() | |
| return '/content/wav2lip-HD/outputs/result.mp4', 'Animation completed.' | |
| eleven_voices = ['Adam','Antoni','Josh','Arnold','Sam','Bella','Rachel','Domi','Elli'] | |
| eleven_voices_ids=['pNInz6obpgDQGcFmaJgB','ErXwobaYiN019PkySvjV','TxGEqnHWrfWFTfGW9XjX','VR6AewLTigWG4xSOukaG','yoZ06aMxZJJ28mfd3POQ','EXAVITQu4vr4xnSDxMaL','21m00Tcm4TlvDq8ikWAM','AZnzlk1XvdvUeBnXmlld','MF3mGyEYCl7XYWbV9V6O'] | |
| chosen_voice = dict(zip(eleven_voices, eleven_voices_ids)) | |
| def stoptraining(mim): | |
| if int(mim) == 1: | |
| try: | |
| CSVutil('csvdb/stop.csv', 'w+', 'stop', 'True') | |
| os.kill(PID, signal.SIGTERM) | |
| except Exception as e: | |
| print(f"Couldn't click due to {e}") | |
| return ( | |
| {"visible": False, "__type__": "update"}, | |
| {"visible": True, "__type__": "update"}, | |
| ) | |
| def elevenTTS(xiapi, text, id, lang): | |
| if xiapi!= '' and id !='': | |
| choice = chosen_voice[id] | |
| CHUNK_SIZE = 1024 | |
| url = f"https://api.elevenlabs.io/v1/text-to-speech/{choice}" | |
| headers = { | |
| "Accept": "audio/mpeg", | |
| "Content-Type": "application/json", | |
| "xi-api-key": xiapi | |
| } | |
| if lang == 'en': | |
| data = { | |
| "text": text, | |
| "model_id": "eleven_monolingual_v1", | |
| "voice_settings": { | |
| "stability": 0.5, | |
| "similarity_boost": 0.5 | |
| } | |
| } | |
| else: | |
| data = { | |
| "text": text, | |
| "model_id": "eleven_multilingual_v1", | |
| "voice_settings": { | |
| "stability": 0.5, | |
| "similarity_boost": 0.5 | |
| } | |
| } | |
| response = requests.post(url, json=data, headers=headers) | |
| with open('./temp_eleven.mp3', 'wb') as f: | |
| for chunk in response.iter_content(chunk_size=CHUNK_SIZE): | |
| if chunk: | |
| f.write(chunk) | |
| aud_path = save_to_wav('./temp_eleven.mp3') | |
| return aud_path, aud_path | |
| else: | |
| tts = gTTS(text, lang=lang) | |
| tts.save('./temp_gTTS.mp3') | |
| aud_path = save_to_wav('./temp_gTTS.mp3') | |
| return aud_path, aud_path | |
| def ilariaTTS(text, ttsvoice): | |
| vo=language_dict[ttsvoice] | |
| asyncio.run(edge_tts.Communicate(text, vo).save("./temp_ilaria.mp3")) | |
| aud_path = save_to_wav('./temp_ilaria.mp3') | |
| return aud_path, aud_path | |
| def upload_to_dataset(files, dir): | |
| if dir == '': | |
| dir = './dataset' | |
| if not os.path.exists(dir): | |
| os.makedirs(dir) | |
| count = 0 | |
| for file in files: | |
| path=file.name | |
| shutil.copy2(path,dir) | |
| count += 1 | |
| return f' {count} files uploaded to {dir}.' | |
| def zip_downloader(model): | |
| if not os.path.exists(f'./weights/{model}.pth'): | |
| return {"__type__": "update"}, f'Make sure the Voice Name is correct. I could not find {model}.pth' | |
| index_found = False | |
| for file in os.listdir(f'./logs/{model}'): | |
| if file.endswith('.index') and 'added' in file: | |
| log_file = file | |
| index_found = True | |
| if index_found: | |
| return [f'./weights/{model}.pth', f'./logs/{model}/{log_file}'], "Done" | |
| else: | |
| return f'./weights/{model}.pth', "Could not find Index file." | |
| badges = """ | |
| <div style="display: flex"> | |
| <span style="margin-right: 5px"> | |
| [  ](https://discord.gg/aihubbrasil) | |
| </span> | |
| <span style="margin-right: 5px"> | |
| [  ](https://twitter.com/GodoyEbert) | |
| </span> | |
| <span> | |
| [  ](https://github.com/rafaelGodoyEbert) | |
| </span> | |
| <span> | |
| [  ](https://discord.gg/aihub) | |
| </span> | |
| <span> | |
| [  ](https://colab.research.google.com/drive/1M1HRS6IM2sZWevRuzO2AVDgXEkfZklpo?usp=sharing) | |
| </span> | |
| </div> | |
| """ | |
| description = """ | |
| Increva-se no canal do <a href='https://www.youtube.com/@aihubbrasil' target='_blank'>Youtube do AI HUB Brasil</a> e no meu pessoal <a href='https://www.youtube.com/@godoyy' target='_blank'>Godoyy</a> | |
| """ | |
| with gr.Blocks(theme=gr.themes.Default(primary_hue="green", secondary_hue="blue"), title="RVC - AI HUB BRASIL") as app: | |
| gr.Markdown(badges) | |
| gr.Markdown(description) | |
| gr.HTML("<h1> Easy GUI | AI HUB BRASIL</h1>") | |
| with gr.Tabs(): | |
| with gr.TabItem("Inference"): | |
| gr.HTML("<h10> Você pode encontrar mais modelos em AI Hub ou AI Hub Brasil </h10>") | |
| # Inference Preset Row | |
| # with gr.Row(): | |
| # mangio_preset = gr.Dropdown(label="Inference Preset", choices=sorted(get_presets())) | |
| # mangio_preset_name_save = gr.Textbox( | |
| # label="Your preset name" | |
| # ) | |
| # mangio_preset_save_btn = gr.Button('Save Preset', variant="primary") | |
| # Other RVC stuff | |
| with gr.Row(): | |
| sid0 = gr.Dropdown(label="1. Escolha seu modelo", choices=sorted(names), value=check_for_name()) | |
| refresh_button = gr.Button("Atualizar", variant="primary") | |
| if check_for_name() != '': | |
| get_vc(sorted(names)[0]) | |
| vc_transform0 = gr.Number(label="Mude o tom aqui. Se a voz for do mesmo sexo, não é necessario alterar(12 caso seja Masculino para feminino, -12 caso seja ao contrário.", value=0) | |
| #clean_button = gr.Button(i18n("卸载音色省显存"), variant="primary") | |
| spk_item = gr.Slider( | |
| minimum=0, | |
| maximum=2333, | |
| step=1, | |
| label=i18n("请选择说话人id"), | |
| value=0, | |
| visible=False, | |
| interactive=True, | |
| ) | |
| #clean_button.click(fn=clean, inputs=[], outputs=[sid0]) | |
| sid0.change( | |
| fn=get_vc, | |
| inputs=[sid0], | |
| outputs=[spk_item], | |
| ) | |
| but0 = gr.Button("Converter", variant="primary") | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| dropbox = gr.File(label="Arraste seu arquivo de áudio e clique em atualizar.") | |
| with gr.Row(): | |
| record_button=gr.Audio(source="microphone", label="Ou você pode usar seu microfone!", type="filepath") | |
| with gr.Row(): | |
| input_audio0 = gr.Dropdown( | |
| label="2.Escolha o arquivo de áudio", | |
| value="./audios/Poema-do-Cume-Arnold", | |
| choices=audio_files | |
| ) | |
| dropbox.upload(fn=save_to_wav2, inputs=[dropbox], outputs=[input_audio0]) | |
| dropbox.upload(fn=change_choices2, inputs=[], outputs=[input_audio0]) | |
| refresh_button2 = gr.Button("Atualizar", variant="primary", size='sm') | |
| record_button.change(fn=save_to_wav, inputs=[record_button], outputs=[input_audio0]) | |
| record_button.change(fn=change_choices2, inputs=[], outputs=[input_audio0]) | |
| with gr.Row(): | |
| with gr.Accordion('ElevenLabs / Google TTS', open=False): | |
| with gr.Column(): | |
| lang = gr.Radio(label='Chinês e Japonês não funcionam atualmente com a ElevenLabs..',choices=['en','it','es','fr','pt','zh-CN','de','hi','ja'], value='pt') | |
| api_box = gr.Textbox(label="Digite sua chave de API para a ElevenLabs ou deixe em branco para usar o GoogleTTS. (Não é obrigatorio)", value='') | |
| elevenid=gr.Dropdown(label="Voz:", choices=eleven_voices) | |
| with gr.Column(): | |
| tfs = gr.Textbox(label="Digite o seu Texto", interactive=True, value="Isso é um teste.") | |
| tts_button = gr.Button(value="Falar") | |
| tts_button.click(fn=elevenTTS, inputs=[api_box,tfs, elevenid, lang], outputs=[record_button, input_audio0]) | |
| with gr.Row(): | |
| with gr.Accordion('Wav2Lip', open=False, visible=False): | |
| with gr.Row(): | |
| size = gr.Radio(label='Resolution:',choices=['Half','Full']) | |
| face = gr.UploadButton("Upload A Character",type='file') | |
| faces = gr.Dropdown(label="OR Choose one:", choices=['None','Ben Shapiro','Andrew Tate']) | |
| with gr.Row(): | |
| preview = gr.Textbox(label="Status:",interactive=False) | |
| face.upload(fn=success_message,inputs=[face], outputs=[preview, faces]) | |
| with gr.Row(): | |
| animation = gr.Video(type='filepath') | |
| refresh_button2.click(fn=change_choices2, inputs=[], outputs=[input_audio0, animation]) | |
| with gr.Row(): | |
| animate_button = gr.Button('Animate') | |
| with gr.Column(): | |
| vc_output2 = gr.Audio( | |
| label="Resultado final! (Clique nos três pontos para baixar o áudio)", | |
| type='filepath', | |
| interactive=False, | |
| ) | |
| with gr.Accordion('Edge-TTS', open=True): | |
| with gr.Column(): | |
| ilariaid=gr.Dropdown(label="Voz:", choices=ilariavoices, value="Brazilian-Antonio- (Male)") | |
| ilariatext = gr.Textbox(label="Digite o seu Texto", interactive=True, value="Isso é um teste.") | |
| ilariatts_button = gr.Button(value="Falar") | |
| ilariatts_button.click(fn=ilariaTTS, inputs=[ilariatext, ilariaid], outputs=[record_button, input_audio0]) | |
| #with gr.Column(): | |
| with gr.Accordion("Configuração de Index", open=False): | |
| #with gr.Row(): | |
| file_index1 = gr.Dropdown( | |
| label="3. Escolha o arquivo de índice (caso não tenha sido encontrado automaticamente).", | |
| choices=get_indexes(), | |
| value=get_index(), | |
| interactive=True, | |
| ) | |
| sid0.change(fn=match_index, inputs=[sid0],outputs=[file_index1]) | |
| refresh_button.click( | |
| fn=change_choices, inputs=[], outputs=[sid0, file_index1] | |
| ) | |
| # file_big_npy1 = gr.Textbox( | |
| # label=i18n("特征文件路径"), | |
| # value="E:\\codes\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy", | |
| # interactive=True, | |
| # ) | |
| index_rate1 = gr.Slider( | |
| minimum=0, | |
| maximum=1, | |
| label=i18n("Proporção do recurso de pesquisa"), | |
| value=0.66, | |
| interactive=True, | |
| ) | |
| animate_button.click(fn=mouth, inputs=[size, face, vc_output2, faces], outputs=[animation, preview]) | |
| with gr.Accordion("Opções avançadas", open=False): | |
| f0method0 = gr.Radio( | |
| label="Opcional: altere o algoritmo de extração de pitch. Os métodos de extração são classificados da “pior qualidade” para a “melhor qualidade”. Se você não sabe o que está fazendo, saia do rmvpe.", | |
| choices=["pm", "dio", "crepe-tiny", "mangio-crepe-tiny", "crepe", "harvest", "mangio-crepe", "rmvpe"], # Fork Feature. Add Crepe-Tiny | |
| value="rmvpe", | |
| interactive=True, | |
| ) | |
| crepe_hop_length = gr.Slider( | |
| minimum=1, | |
| maximum=512, | |
| step=1, | |
| label="Mangio-Crepe Hop Length. Higher numbers will reduce the chance of extreme pitch changes but lower numbers will increase accuracy. 64-192 is a good range to experiment with.", | |
| value=120, | |
| interactive=True, | |
| visible=False, | |
| ) | |
| f0method0.change(fn=whethercrepeornah, inputs=[f0method0], outputs=[crepe_hop_length]) | |
| filter_radius0 = gr.Slider( | |
| minimum=0, | |
| maximum=7, | |
| label=i18n(">=3, use filtragem mediana no resultado do reconhecimento do tom de colheita, o valor é o raio do filtro, o que pode enfraquecer o som mudo."), | |
| value=3, | |
| step=1, | |
| interactive=True, | |
| ) | |
| resample_sr0 = gr.Slider( | |
| minimum=0, | |
| maximum=48000, | |
| label=i18n("后处理重采样至最终采样率，0为不进行重采样"), | |
| value=0, | |
| step=1, | |
| interactive=True, | |
| visible=False | |
| ) | |
| rms_mix_rate0 = gr.Slider( | |
| minimum=0, | |
| maximum=1, | |
| label=i18n("O envelope do volume da fonte de entrada substitui a taxa de fusão do envelope do volume de saída. Quanto mais próximo estiver de 1, mais envelope de saída será usado."), | |
| value=0.21, | |
| interactive=True, | |
| ) | |
| protect0 = gr.Slider( | |
| minimum=0, | |
| maximum=0.5, | |
| label=i18n("Proteja consoantes surdas e sons respiratórios para evitar artefatos, como quebra de som eletrônico. Não ative quando atingir 0,5. Abaixe-o para aumentar a proteção, mas pode reduzir o efeito de indexação."), | |
| value=0.33, | |
| step=0.01, | |
| interactive=True, | |
| ) | |
| formanting = gr.Checkbox( | |
| value=bool(DoFormant), | |
| label="[EXPERIMENTAL] Áudio de inferência de mudança de formante", | |
| info="Usado para conversões de homem para mulher e vice-versa", | |
| interactive=True, | |
| visible=True, | |
| ) | |
| formant_preset = gr.Dropdown( | |
| value='', | |
| choices=get_fshift_presets(), | |
| label="browse presets for formanting", | |
| visible=bool(DoFormant), | |
| ) | |
| formant_refresh_button = gr.Button( | |
| value='\U0001f504', | |
| visible=bool(DoFormant), | |
| variant='primary', | |
| ) | |
| #formant_refresh_button = ToolButton( elem_id='1') | |
| #create_refresh_button(formant_preset, lambda: {"choices": formant_preset}, "refresh_list_shiftpresets") | |
| qfrency = gr.Slider( | |
| value=Quefrency, | |
| info="Default value is 1.0", | |
| label="Quefrency for formant shifting", | |
| minimum=0.0, | |
| maximum=16.0, | |
| step=0.1, | |
| visible=bool(DoFormant), | |
| interactive=True, | |
| ) | |
| tmbre = gr.Slider( | |
| value=Timbre, | |
| info="Default value is 1.0", | |
| label="Timbre for formant shifting", | |
| minimum=0.0, | |
| maximum=16.0, | |
| step=0.1, | |
| visible=bool(DoFormant), | |
| interactive=True, | |
| ) | |
| formant_preset.change(fn=preset_apply, inputs=[formant_preset, qfrency, tmbre], outputs=[qfrency, tmbre]) | |
| frmntbut = gr.Button("Apply", variant="primary", visible=bool(DoFormant)) | |
| formanting.change(fn=formant_enabled,inputs=[formanting,qfrency,tmbre,frmntbut,formant_preset,formant_refresh_button],outputs=[formanting,qfrency,tmbre,frmntbut,formant_preset,formant_refresh_button]) | |
| frmntbut.click(fn=formant_apply,inputs=[qfrency, tmbre], outputs=[qfrency, tmbre]) | |
| formant_refresh_button.click(fn=update_fshift_presets,inputs=[formant_preset, qfrency, tmbre],outputs=[formant_preset, qfrency, tmbre]) | |
| with gr.Row(): | |
| vc_output1 = gr.Textbox("") | |
| f0_file = gr.File(label=i18n("F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调"), visible=False) | |
| but0.click( | |
| vc_single, | |
| [ | |
| spk_item, | |
| input_audio0, | |
| vc_transform0, | |
| f0_file, | |
| f0method0, | |
| file_index1, | |
| # file_index2, | |
| # file_big_npy1, | |
| index_rate1, | |
| filter_radius0, | |
| resample_sr0, | |
| rms_mix_rate0, | |
| protect0, | |
| crepe_hop_length | |
| ], | |
| [vc_output1, vc_output2], | |
| ) | |
| with gr.Accordion("Batch Conversion",open=False, visible=False): | |
| with gr.Row(): | |
| with gr.Column(): | |
| vc_transform1 = gr.Number( | |
| label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0 | |
| ) | |
| opt_input = gr.Textbox(label=i18n("指定输出文件夹"), value="opt") | |
| f0method1 = gr.Radio( | |
| label=i18n( | |
| "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU" | |
| ), | |
| choices=["pm", "harvest", "crepe", "rmvpe"], | |
| value="rmvpe", | |
| interactive=True, | |
| ) | |
| filter_radius1 = gr.Slider( | |
| minimum=0, | |
| maximum=7, | |
| label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"), | |
| value=3, | |
| step=1, | |
| interactive=True, | |
| ) | |
| with gr.Column(): | |
| file_index3 = gr.Textbox( | |
| label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"), | |
| value="", | |
| interactive=True, | |
| ) | |
| file_index4 = gr.Dropdown( | |
| label=i18n("自动检测index路径,下拉式选择(dropdown)"), | |
| choices=sorted(index_paths), | |
| interactive=True, | |
| ) | |
| refresh_button.click( | |
| fn=lambda: change_choices()[1], | |
| inputs=[], | |
| outputs=file_index4, | |
| ) | |
| # file_big_npy2 = gr.Textbox( | |
| # label=i18n("特征文件路径"), | |
| # value="E:\\codes\\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy", | |
| # interactive=True, | |
| # ) | |
| index_rate2 = gr.Slider( | |
| minimum=0, | |
| maximum=1, | |
| label=i18n("检索特征占比"), | |
| value=1, | |
| interactive=True, | |
| ) | |
| with gr.Column(): | |
| resample_sr1 = gr.Slider( | |
| minimum=0, | |
| maximum=48000, | |
| label=i18n("后处理重采样至最终采样率，0为不进行重采样"), | |
| value=0, | |
| step=1, | |
| interactive=True, | |
| ) | |
| rms_mix_rate1 = gr.Slider( | |
| minimum=0, | |
| maximum=1, | |
| label=i18n("输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"), | |
| value=1, | |
| interactive=True, | |
| ) | |
| protect1 = gr.Slider( | |
| minimum=0, | |
| maximum=0.5, | |
| label=i18n( | |
| "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果" | |
| ), | |
| value=0.33, | |
| step=0.01, | |
| interactive=True, | |
| ) | |
| with gr.Column(): | |
| dir_input = gr.Textbox( | |
| label=i18n("输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)"), | |
| value="E:\codes\py39\\test-20230416b\\todo-songs", | |
| ) | |
| inputs = gr.File( | |
| file_count="multiple", label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹") | |
| ) | |
| with gr.Row(): | |
| format1 = gr.Radio( | |
| label=i18n("导出文件格式"), | |
| choices=["wav", "flac", "mp3", "m4a"], | |
| value="flac", | |
| interactive=True, | |
| ) | |
| but1 = gr.Button(i18n("转换"), variant="primary") | |
| vc_output3 = gr.Textbox(label=i18n("输出信息")) | |
| but1.click( | |
| vc_multi, | |
| [ | |
| spk_item, | |
| dir_input, | |
| opt_input, | |
| inputs, | |
| vc_transform1, | |
| f0method1, | |
| file_index3, | |
| file_index4, | |
| # file_big_npy2, | |
| index_rate2, | |
| filter_radius1, | |
| resample_sr1, | |
| rms_mix_rate1, | |
| protect1, | |
| format1, | |
| crepe_hop_length, | |
| ], | |
| [vc_output3], | |
| ) | |
| but1.click(fn=lambda: easy_uploader.clear()) | |
| with gr.TabItem("Baixar novos modelos"): | |
| with gr.Row(): | |
| url=gr.Textbox(label="Huggingface Link:") | |
| with gr.Row(): | |
| model = gr.Textbox(label="Nome do modelo (Sem espaços):") | |
| download_button=gr.Button("Baixar") | |
| with gr.Row(): | |
| status_bar=gr.Textbox(label="Status do download") | |
| download_button.click(fn=download_from_url, inputs=[url, model], outputs=[status_bar]) | |
| def has_two_files_in_pretrained_folder(): | |
| pretrained_folder = "./pretrained/" | |
| if not os.path.exists(pretrained_folder): | |
| return False | |
| files_in_folder = os.listdir(pretrained_folder) | |
| num_files = len(files_in_folder) | |
| return num_files >= 2 | |
| if has_two_files_in_pretrained_folder(): | |
| print("Pretrained weights are downloaded. Training tab enabled!\n-------------------------------") | |
| with gr.TabItem("Train", visible=False): | |
| with gr.Row(): | |
| with gr.Column(): | |
| exp_dir1 = gr.Textbox(label="Voice Name:", value="My-Voice") | |
| sr2 = gr.Radio( | |
| label=i18n("目标采样率"), | |
| choices=["40k", "48k"], | |
| value="40k", | |
| interactive=True, | |
| visible=False | |
| ) | |
| if_f0_3 = gr.Radio( | |
| label=i18n("模型是否带音高指导(唱歌一定要, 语音可以不要)"), | |
| choices=[True, False], | |
| value=True, | |
| interactive=True, | |
| visible=False | |
| ) | |
| version19 = gr.Radio( | |
| label="RVC version", | |
| choices=["v1", "v2"], | |
| value="v2", | |
| interactive=True, | |
| visible=False, | |
| ) | |
| np7 = gr.Slider( | |
| minimum=0, | |
| maximum=config.n_cpu, | |
| step=1, | |
| label="# of CPUs for data processing (Leave as it is)", | |
| value=config.n_cpu, | |
| interactive=True, | |
| visible=True | |
| ) | |
| trainset_dir4 = gr.Textbox(label="Path to your dataset (audios, not zip):", value="./dataset") | |
| easy_uploader = gr.Files(label='OR Drop your audios here. They will be uploaded in your dataset path above.',file_types=['audio']) | |
| but1 = gr.Button("1. Process The Dataset", variant="primary") | |
| info1 = gr.Textbox(label="Status (wait until it says 'end preprocess'):", value="") | |
| easy_uploader.upload(fn=upload_to_dataset, inputs=[easy_uploader, trainset_dir4], outputs=[info1]) | |
| but1.click( | |
| preprocess_dataset, [trainset_dir4, exp_dir1, sr2, np7], [info1] | |
| ) | |
| with gr.Column(): | |
| spk_id5 = gr.Slider( | |
| minimum=0, | |
| maximum=4, | |
| step=1, | |
| label=i18n("请指定说话人id"), | |
| value=0, | |
| interactive=True, | |
| visible=False | |
| ) | |
| with gr.Accordion('GPU Settings', open=False, visible=False): | |
| gpus6 = gr.Textbox( | |
| label=i18n("以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2"), | |
| value=gpus, | |
| interactive=True, | |
| visible=False | |
| ) | |
| gpu_info9 = gr.Textbox(label=i18n("显卡信息"), value=gpu_info) | |
| f0method8 = gr.Radio( | |
| label=i18n( | |
| "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢" | |
| ), | |
| choices=["harvest","crepe", "mangio-crepe", "rmvpe"], # Fork feature: Crepe on f0 extraction for training. | |
| value="rmvpe", | |
| interactive=True, | |
| ) | |
| extraction_crepe_hop_length = gr.Slider( | |
| minimum=1, | |
| maximum=512, | |
| step=1, | |
| label=i18n("crepe_hop_length"), | |
| value=128, | |
| interactive=True, | |
| visible=False, | |
| ) | |
| f0method8.change(fn=whethercrepeornah, inputs=[f0method8], outputs=[extraction_crepe_hop_length]) | |
| but2 = gr.Button("2. Pitch Extraction", variant="primary") | |
| info2 = gr.Textbox(label="Status(Check the Colab Notebook's cell output):", value="", max_lines=8) | |
| but2.click( | |
| extract_f0_feature, | |
| [gpus6, np7, f0method8, if_f0_3, exp_dir1, version19, extraction_crepe_hop_length], | |
| [info2], | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| total_epoch11 = gr.Slider( | |
| minimum=1, | |
| maximum=5000, | |
| step=10, | |
| label="Total # of training epochs (IF you choose a value too high, your model will sound horribly overtrained.):", | |
| value=250, | |
| interactive=True, | |
| ) | |
| butstop = gr.Button( | |
| "Stop Training", | |
| variant='primary', | |
| visible=False, | |
| ) | |
| but3 = gr.Button("3. Train Model", variant="primary", visible=True) | |
| but3.click(fn=stoptraining, inputs=[gr.Number(value=0, visible=False)], outputs=[but3, butstop]) | |
| butstop.click(fn=stoptraining, inputs=[gr.Number(value=1, visible=False)], outputs=[butstop, but3]) | |
| but4 = gr.Button("4.Train Index", variant="primary") | |
| info3 = gr.Textbox(label="Status(Check the Colab Notebook's cell output):", value="", max_lines=10) | |
| with gr.Accordion("Training Preferences (You can leave these as they are)", open=False): | |
| #gr.Markdown(value=i18n("step3: 填写训练设置, 开始训练模型和索引")) | |
| with gr.Column(): | |
| save_epoch10 = gr.Slider( | |
| minimum=1, | |
| maximum=200, | |
| step=1, | |
| label="Backup every X amount of epochs:", | |
| value=10, | |
| interactive=True, | |
| ) | |
| batch_size12 = gr.Slider( | |
| minimum=1, | |
| maximum=40, | |
| step=1, | |
| label="Batch Size (LEAVE IT unless you know what you're doing!):", | |
| value=default_batch_size, | |
| interactive=True, | |
| ) | |
| if_save_latest13 = gr.Checkbox( | |
| label="Save only the latest '.ckpt' file to save disk space.", | |
| value=True, | |
| interactive=True, | |
| ) | |
| if_cache_gpu17 = gr.Checkbox( | |
| label="Cache all training sets to GPU memory. Caching small datasets (less than 10 minutes) can speed up training, but caching large datasets will consume a lot of GPU memory and may not provide much speed improvement.", | |
| value=False, | |
| interactive=True, | |
| ) | |
| if_save_every_weights18 = gr.Checkbox( | |
| label="Save a small final model to the 'weights' folder at each save point.", | |
| value=True, | |
| interactive=True, | |
| ) | |
| zip_model = gr.Button('5. Download Model') | |
| zipped_model = gr.Files(label='Your Model and Index file can be downloaded here:') | |
| zip_model.click(fn=zip_downloader, inputs=[exp_dir1], outputs=[zipped_model, info3]) | |
| with gr.Group(): | |
| with gr.Accordion("Base Model Locations:", open=False, visible=False): | |
| pretrained_G14 = gr.Textbox( | |
| label=i18n("加载预训练底模G路径"), | |
| value="pretrained_v2/f0G40k.pth", | |
| interactive=True, | |
| ) | |
| pretrained_D15 = gr.Textbox( | |
| label=i18n("加载预训练底模D路径"), | |
| value="pretrained_v2/f0D40k.pth", | |
| interactive=True, | |
| ) | |
| gpus16 = gr.Textbox( | |
| label=i18n("以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2"), | |
| value=gpus, | |
| interactive=True, | |
| ) | |
| sr2.change( | |
| change_sr2, | |
| [sr2, if_f0_3, version19], | |
| [pretrained_G14, pretrained_D15, version19], | |
| ) | |
| version19.change( | |
| change_version19, | |
| [sr2, if_f0_3, version19], | |
| [pretrained_G14, pretrained_D15], | |
| ) | |
| if_f0_3.change( | |
| change_f0, | |
| [if_f0_3, sr2, version19], | |
| [f0method8, pretrained_G14, pretrained_D15], | |
| ) | |
| but5 = gr.Button(i18n("一键训练"), variant="primary", visible=False) | |
| but3.click( | |
| click_train, | |
| [ | |
| exp_dir1, | |
| sr2, | |
| if_f0_3, | |
| spk_id5, | |
| save_epoch10, | |
| total_epoch11, | |
| batch_size12, | |
| if_save_latest13, | |
| pretrained_G14, | |
| pretrained_D15, | |
| gpus16, | |
| if_cache_gpu17, | |
| if_save_every_weights18, | |
| version19, | |
| ], | |
| [ | |
| info3, | |
| butstop, | |
| but3, | |
| ], | |
| ) | |
| but4.click(train_index, [exp_dir1, version19], info3) | |
| but5.click( | |
| train1key, | |
| [ | |
| exp_dir1, | |
| sr2, | |
| if_f0_3, | |
| trainset_dir4, | |
| spk_id5, | |
| np7, | |
| f0method8, | |
| save_epoch10, | |
| total_epoch11, | |
| batch_size12, | |
| if_save_latest13, | |
| pretrained_G14, | |
| pretrained_D15, | |
| gpus16, | |
| if_cache_gpu17, | |
| if_save_every_weights18, | |
| version19, | |
| extraction_crepe_hop_length | |
| ], | |
| info3, | |
| ) | |
| else: | |
| print( | |
| "Pesos pré-treinados não baixados. Desativando guia de treinamento.\n" | |
| "Quer saber como treinar uma voz? Junte-se ao servidor AI HUB no Discord!\nhttps://discord.gg/aihub ou https://discord.gg/aihubbrasil\n" | |
| "-------------------------------\n" | |
| ) | |
| gr.Markdown("<h4> Huggingface port by Ilaria of the Rejekt Easy GUI </h4>") | |
| gr.Markdown( | |
| """ | |
| Feito com 💖 por Ilaria | Traduzido por Rafael Godoy | Suporte no servidor do Discord AI HUB Brasil ou Global | |
| """ | |
| ) | |
| app.queue().launch(share=False, quiet=False) | |
| #endregion |