diff --git a/infer-web.py b/infer-web.py index f4bbd4f..6a238da 100644 --- a/infer-web.py +++ b/infer-web.py @@ -1,1792 +1,1793 @@ -import torch, os, traceback, sys, warnings, shutil, numpy as np - -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 - -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) -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, "weights"), exist_ok=True) -os.environ["TEMP"] = tmp -warnings.filterwarnings("ignore") -torch.manual_seed(114514) -from i18n import I18nAuto -import ffmpeg - -i18n = I18nAuto() -# 判断是否有能用来训练和加速推理的N卡 -ngpu = torch.cuda.device_count() -gpu_infos = [] -mem = [] -if (not torch.cuda.is_available()) or ngpu == 0: - if_gpu_ok = False -else: - if_gpu_ok = False - for i in range(ngpu): - gpu_name = torch.cuda.get_device_name(i) - if ( - "10" in gpu_name - or "16" in gpu_name - or "20" in gpu_name - or "30" in gpu_name - or "40" in gpu_name - or "A2" in gpu_name.upper() - or "A3" in gpu_name.upper() - or "A4" in gpu_name.upper() - or "P4" in gpu_name.upper() - or "A50" in gpu_name.upper() - or "70" in gpu_name - or "80" in gpu_name - or "90" in gpu_name - or "M4" in gpu_name.upper() - or "T4" in gpu_name.upper() - or "TITAN" in gpu_name.upper() - ): # A10#A100#V100#A40#P40#M40#K80#A4500 - if_gpu_ok = True # 至少有一张能用的N卡 - gpu_infos.append("%s\t%s" % (i, gpu_name)) - mem.append( - int( - torch.cuda.get_device_properties(i).total_memory - / 1024 - / 1024 - / 1024 - + 0.4 - ) - ) -if if_gpu_ok == True and len(gpu_infos) > 0: - gpu_info = "\n".join(gpu_infos) - default_batch_size = min(mem) // 2 -else: - gpu_info = 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 infer_pack.models import ( - SynthesizerTrnMs256NSFsid, - SynthesizerTrnMs256NSFsid_nono, - SynthesizerTrnMs768NSFsid, - SynthesizerTrnMs768NSFsid_nono, -) -from scipy.io import wavfile -from fairseq import checkpoint_utils -import gradio as gr -import logging -from vc_infer_pipeline import VC -from config import Config -from infer_uvr5 import _audio_pre_ -from my_utils import load_audio -from train.process_ckpt import show_info, change_info, merge, extract_small_model - -config = Config() -# from trainset_preprocess_pipeline import PreProcess -logging.getLogger("numba").setLevel(logging.WARNING) - - -class ToolButton(gr.Button, gr.components.FormComponent): - """Small button with single emoji as text, fits inside gradio forms""" - - def __init__(self, **kwargs): - super().__init__(variant="tool", **kwargs) - - def get_block_name(self): - return "button" - - -hubert_model = None - - -def load_hubert(): - global hubert_model - models, _, _ = checkpoint_utils.load_model_ensemble_and_task( - ["hubert_base.pt"], - suffix="", - ) - hubert_model = models[0] - hubert_model = hubert_model.to(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" -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)) -uvr5_names = [] -for name in os.listdir(weight_uvr5_root): - if name.endswith(".pth"): - uvr5_names.append(name.replace(".pth", "")) - - -def vc_single( - sid, - input_audio_path, - f0_up_key, - f0_file, - f0_method, - file_index, - file_index2, - # file_big_npy, - index_rate, - filter_radius, - resample_sr, - rms_mix_rate, -): # 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) - 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") - ) - 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_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, - 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, -): - 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_index2, - # file_big_npy, - index_rate, - filter_radius, - resample_sr, - rms_mix_rate, - ) - if "Success" in info: - try: - tgt_sr, audio_opt = opt - wavfile.write( - "%s/%s" % (opt_root, os.path.basename(path)), tgt_sr, audio_opt - ) - except: - info += traceback.format_exc() - infos.append("%s->%s" % (os.path.basename(path), info)) - yield "\n".join(infos) - yield "\n".join(infos) - except: - yield traceback.format_exc() - - -def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg): - infos = [] - try: - inp_root = inp_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ") - save_root_vocal = ( - save_root_vocal.strip(" ").strip('"').strip("\n").strip('"').strip(" ") - ) - save_root_ins = ( - save_root_ins.strip(" ").strip('"').strip("\n").strip('"').strip(" ") - ) - pre_fun = _audio_pre_( - agg=int(agg), - model_path=os.path.join(weight_uvr5_root, model_name + ".pth"), - device=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) - done = 1 - except: - need_reformat = 1 - traceback.print_exc() - if need_reformat == 1: - tmp_path = "%s/%s.reformatted.wav" % (tmp, os.path.basename(inp_path)) - os.system( - "ffmpeg -i %s -vn -acodec pcm_s16le -ac 2 -ar 44100 %s -y" - % (inp_path, tmp_path) - ) - inp_path = tmp_path - try: - if done == 0: - pre_fun._path_audio_(inp_path, save_root_ins, save_root_vocal) - infos.append("%s->Success" % (os.path.basename(inp_path))) - yield "\n".join(infos) - except: - infos.append( - "%s->%s" % (os.path.basename(inp_path), traceback.format_exc()) - ) - yield "\n".join(infos) - except: - infos.append(traceback.format_exc()) - yield "\n".join(infos) - finally: - try: - del pre_fun.model - del pre_fun - except: - traceback.print_exc() - print("clean_empty_cache") - if torch.cuda.is_available(): - torch.cuda.empty_cache() - yield "\n".join(infos) - - -# 一个选项卡全局只能有一个音色 -def get_vc(sid): - global n_spk, tgt_sr, net_g, vc, cpt, 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": True, "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): - 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" % ( - now_dir, - exp_dir, - n_p, - f0method, - ) - print(cmd) - p = Popen(cmd, shell=True, cwd=now_dir) # , stdin=PIPE, stdout=PIPE,stderr=PIPE - ###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读 - done = [False] - threading.Thread( - target=if_done, - args=( - done, - p, - ), - ).start() - while 1: - with open( - "%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r" - ) as f: - yield (f.read()) - sleep(1) - if done[0] == True: - break - with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f: - log = f.read() - print(log) - yield log - ####对不同part分别开多进程 - """ - n_part=int(sys.argv[1]) - i_part=int(sys.argv[2]) - i_gpu=sys.argv[3] - exp_dir=sys.argv[4] - os.environ["CUDA_VISIBLE_DEVICES"]=str(i_gpu) - """ - leng = len(gpus) - ps = [] - for idx, n_g in enumerate(gpus): - cmd = ( - 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): - vis_v = True if sr2 == "40k" else False - if sr2 != "40k": - version19 = "v1" - path_str = "" if version19 == "v1" else "_v2" - version_state = {"visible": vis_v, "__type__": "update"} - if vis_v == False: - version_state["value"] = "v1" - f0_str = "f0" if if_f0_3 else "" - return ( - "pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), - "pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), - version_state, - ) - - -def change_version19(sr2, if_f0_3, version19): - path_str = "" if version19 == "v1" else "_v2" - f0_str = "f0" if if_f0_3 else "" - return "pretrained%s/%sG%s.pth" % ( - path_str, - f0_str, - sr2, - ), "pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2) - - -def change_f0(if_f0_3, sr2, version19): # f0method8,pretrained_G14,pretrained_D15 - path_str = "" if version19 == "v1" else "_v2" - if if_f0_3: - return ( - {"visible": True, "__type__": "update"}, - "pretrained%s/f0G%s.pth" % (path_str, sr2), - "pretrained%s/f0D%s.pth" % (path_str, sr2), - ) - return ( - {"visible": False, "__type__": "update"}, - "pretrained%s/G%s.pth" % (path_str, sr2), - "pretrained%s/D%s.pth" % (path_str, sr2), - ) - - -# but3.click(click_train,[exp_dir1,sr2,if_f0_3,save_epoch10,total_epoch11,batch_size12,if_save_latest13,pretrained_G14,pretrained_D15,gpus16]) -def click_train( - exp_dir1, - sr2, - if_f0_3, - spk_id5, - save_epoch10, - total_epoch11, - batch_size12, - if_save_latest13, - pretrained_G14, - pretrained_D15, - gpus16, - if_cache_gpu17, - if_save_every_weights18, - version19, -): - # 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) - ) - 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 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 -pg %s -pd %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, - pretrained_G14, - pretrained_D15, - 1 if if_save_latest13 == i18n("是") else 0, - 1 if if_cache_gpu17 == i18n("是") else 0, - 1 if if_save_every_weights18 == i18n("是") 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 -pg %s -pd %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, - pretrained_G14, - pretrained_D15, - 1 if if_save_latest13 == i18n("是") else 0, - 1 if if_cache_gpu17 == i18n("是") else 0, - 1 if if_save_every_weights18 == i18n("是") else 0, - version19, - ) - ) - print(cmd) - p = Popen(cmd, shell=True, cwd=now_dir) - p.wait() - return "训练结束, 您可查看控制台训练日志或实验文件夹下的train.log" - - -# but4.click(train_index, [exp_dir1], info3) -def train_index(exp_dir1, 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.index" - % (exp_dir, n_ivf, index_ivf.nprobe, 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.index" - % (exp_dir, n_ivf, index_ivf.nprobe, version19), - ) - infos.append( - "成功构建索引,added_IVF%s_Flat_nprobe_%s_%s.index" - % (n_ivf, index_ivf.nprobe, 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, -): - 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" % ( - model_log_dir, - np7, - f0method8, - ) - 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 -pg %s -pd %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, - pretrained_G14, - pretrained_D15, - 1 if if_save_latest13 == i18n("是") else 0, - 1 if if_cache_gpu17 == i18n("是") else 0, - 1 if if_save_every_weights18 == i18n("是") 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 -pg %s -pd %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, - pretrained_G14, - pretrained_D15, - 1 if if_save_latest13 == i18n("是") else 0, - 1 if if_cache_gpu17 == i18n("是") else 0, - 1 if if_save_every_weights18 == i18n("是") 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.index" - % (model_log_dir, n_ivf, index_ivf.nprobe, 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.index" - % (model_log_dir, n_ivf, index_ivf.nprobe, version19), - ) - yield get_info_str( - "成功构建索引, added_IVF%s_Flat_nprobe_%s_%s.index" - % (n_ivf, index_ivf.nprobe, 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 ( - 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 infer_pack.models_onnx_moess import SynthesizerTrnMs256NSFsidM -from infer_pack.models_onnx import SynthesizerTrnMs256NSFsidO - - -def export_onnx(ModelPath, ExportedPath, MoeVS=True): - hidden_channels = 256 # hidden_channels,为768Vec做准备 - cpt = torch.load(ModelPath, map_location="cpu") - cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk - print(*cpt["config"]) - - test_phone = torch.rand(1, 200, hidden_channels) # hidden unit - test_phone_lengths = torch.tensor([200]).long() # hidden unit 长度(貌似没啥用) - test_pitch = torch.randint(size=(1, 200), low=5, high=255) # 基频(单位赫兹) - test_pitchf = torch.rand(1, 200) # nsf基频 - test_ds = torch.LongTensor([0]) # 说话人ID - test_rnd = torch.rand(1, 192, 200) # 噪声(加入随机因子) - - device = "cpu" # 导出时设备(不影响使用模型) - - if MoeVS: - net_g = SynthesizerTrnMs256NSFsidM( - *cpt["config"], is_half=False - ) # fp32导出(C++要支持fp16必须手动将内存重新排列所以暂时不用fp16) - net_g.load_state_dict(cpt["weight"], strict=False) - input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"] - output_names = [ - "audio", - ] - torch.onnx.export( - net_g, - ( - test_phone.to(device), - test_phone_lengths.to(device), - test_pitch.to(device), - test_pitchf.to(device), - test_ds.to(device), - test_rnd.to(device), - ), - ExportedPath, - dynamic_axes={ - "phone": [1], - "pitch": [1], - "pitchf": [1], - "rnd": [2], - }, - do_constant_folding=False, - opset_version=16, - verbose=False, - input_names=input_names, - output_names=output_names, - ) - else: - net_g = SynthesizerTrnMs256NSFsidO( - *cpt["config"], is_half=False - ) # fp32导出(C++要支持fp16必须手动将内存重新排列所以暂时不用fp16) - net_g.load_state_dict(cpt["weight"], strict=False) - input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds"] - output_names = [ - "audio", - ] - torch.onnx.export( - net_g, - ( - test_phone.to(device), - test_phone_lengths.to(device), - test_pitch.to(device), - test_pitchf.to(device), - test_ds.to(device), - ), - ExportedPath, - dynamic_axes={ - "phone": [1], - "pitch": [1], - "pitchf": [1], - }, - do_constant_folding=False, - opset_version=16, - verbose=False, - input_names=input_names, - output_names=output_names, - ) - return "Finished" - - -with gr.Blocks() as app: - gr.Markdown( - value=i18n( - "本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.
如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录使用需遵守的协议-LICENSE.txt." - ) - ) - with gr.Tabs(): - with gr.TabItem(i18n("模型推理")): - with gr.Row(): - sid0 = gr.Dropdown(label=i18n("推理音色"), choices=sorted(names)) - refresh_button = gr.Button(i18n("刷新音色列表和索引路径"), 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]) - sid0.change( - fn=get_vc, - inputs=[sid0], - outputs=[spk_item], - ) - with gr.Group(): - gr.Markdown( - value=i18n("男转女推荐+12key, 女转男推荐-12key, 如果音域爆炸导致音色失真也可以自己调整到合适音域. ") - ) - with gr.Row(): - with gr.Column(): - vc_transform0 = gr.Number( - label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0 - ) - input_audio0 = gr.Textbox( - label=i18n("输入待处理音频文件路径(默认是正确格式示例)"), - value="E:\\codes\\py39\\test-20230416b\\todo-songs\\冬之花clip1.wav", - ) - f0method0 = gr.Radio( - label=i18n("选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比"), - choices=["pm", "harvest"], - value="pm", - interactive=True, - ) - 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=i18n("自动检测index路径,下拉式选择(dropdown)"), - choices=sorted(index_paths), - interactive=True, - ) - refresh_button.click( - fn=change_choices, inputs=[], outputs=[sid0, file_index2] - ) - # 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=0.76, - 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, - ) - rms_mix_rate0 = gr.Slider( - minimum=0, - maximum=1, - label=i18n("输入源音量包络替换输出音量包络融合比例,越靠近1越使用输出包络"), - value=1, - interactive=True, - ) - f0_file = gr.File(label=i18n("F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调")) - but0 = gr.Button(i18n("转换"), variant="primary") - with gr.Column(): - vc_output1 = gr.Textbox(label=i18n("输出信息")) - vc_output2 = gr.Audio(label=i18n("输出音频(右下角三个点,点了可以下载)")) - but0.click( - vc_single, - [ - spk_item, - input_audio0, - vc_transform0, - f0_file, - f0method0, - file_index1, - file_index2, - # file_big_npy1, - index_rate1, - filter_radius0, - resample_sr0, - rms_mix_rate0, - ], - [vc_output1, vc_output2], - ) - with gr.Group(): - gr.Markdown( - value=i18n("批量转换, 输入待转换音频文件夹, 或上传多个音频文件, 在指定文件夹(默认opt)下输出转换的音频. ") - ) - with gr.Row(): - with gr.Column(): - vc_transform1 = gr.Number( - label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0 - ) - opt_input = gr.Textbox(label=i18n("指定输出文件夹"), value="opt") - f0method1 = gr.Radio( - label=i18n("选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比"), - choices=["pm", "harvest"], - value="pm", - interactive=True, - ) - 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, - ) - # 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, - ) - 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, - ) - 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") - ) - 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, - ], - [vc_output3], - ) - with gr.TabItem(i18n("伴奏人声分离")): - with gr.Group(): - gr.Markdown( - value=i18n( - "人声伴奏分离批量处理, 使用UVR5模型.
不带和声用HP2, 带和声且提取的人声不需要和声用HP5
合格的文件夹路径格式举例: E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(去文件管理器地址栏拷就行了)" - ) - ) - with gr.Row(): - with gr.Column(): - dir_wav_input = gr.Textbox( - label=i18n("输入待处理音频文件夹路径"), - value="E:\\codes\\py39\\test-20230416b\\todo-songs\\todo-songs", - ) - wav_inputs = gr.File( - file_count="multiple", label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹") - ) - with gr.Column(): - model_choose = gr.Dropdown(label=i18n("模型"), choices=uvr5_names) - agg = gr.Slider( - minimum=0, - maximum=20, - step=1, - label="人声提取激进程度", - value=10, - interactive=True, - visible=False, # 先不开放调整 - ) - opt_vocal_root = gr.Textbox( - label=i18n("指定输出人声文件夹"), value="opt" - ) - opt_ins_root = gr.Textbox(label=i18n("指定输出乐器文件夹"), value="opt") - but2 = gr.Button(i18n("转换"), variant="primary") - vc_output4 = gr.Textbox(label=i18n("输出信息")) - but2.click( - uvr, - [ - model_choose, - dir_wav_input, - opt_vocal_root, - wav_inputs, - opt_ins_root, - agg, - ], - [vc_output4], - ) - with gr.TabItem(i18n("训练")): - gr.Markdown( - value=i18n( - "step1: 填写实验配置. 实验数据放在logs下, 每个实验一个文件夹, 需手工输入实验名路径, 内含实验配置, 日志, 训练得到的模型文件. " - ) - ) - with gr.Row(): - exp_dir1 = gr.Textbox(label=i18n("输入实验名"), value="mi-test") - sr2 = gr.Radio( - label=i18n("目标采样率"), - choices=["32k", "40k", "48k"], - value="40k", - interactive=True, - ) - if_f0_3 = gr.Radio( - label=i18n("模型是否带音高指导(唱歌一定要, 语音可以不要)"), - choices=[True, False], - value=True, - interactive=True, - ) - version19 = gr.Radio( - label=i18n("版本(目前仅40k支持了v2)"), - 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=config.n_cpu, - interactive=True, - ) - with gr.Group(): # 暂时单人的, 后面支持最多4人的#数据处理 - gr.Markdown( - value=i18n( - "step2a: 自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练. " - ) - ) - with gr.Row(): - trainset_dir4 = gr.Textbox( - label=i18n("输入训练文件夹路径"), value="E:\\语音音频+标注\\米津玄师\\src" - ) - spk_id5 = gr.Slider( - minimum=0, - maximum=4, - step=1, - label=i18n("请指定说话人id"), - value=0, - interactive=True, - ) - but1 = gr.Button(i18n("处理数据"), variant="primary") - info1 = gr.Textbox(label=i18n("输出信息"), value="") - but1.click( - preprocess_dataset, [trainset_dir4, exp_dir1, sr2, np7], [info1] - ) - with gr.Group(): - gr.Markdown(value=i18n("step2b: 使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号)")) - with gr.Row(): - with gr.Column(): - gpus6 = gr.Textbox( - label=i18n("以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2"), - value=gpus, - interactive=True, - ) - gpu_info9 = gr.Textbox(label=i18n("显卡信息"), value=gpu_info) - with gr.Column(): - f0method8 = gr.Radio( - label=i18n( - "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢" - ), - choices=["pm", "harvest", "dio"], - value="harvest", - interactive=True, - ) - but2 = gr.Button(i18n("特征提取"), variant="primary") - info2 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) - but2.click( - extract_f0_feature, - [gpus6, np7, f0method8, if_f0_3, exp_dir1, version19], - [info2], - ) - with gr.Group(): - gr.Markdown(value=i18n("step3: 填写训练设置, 开始训练模型和索引")) - with gr.Row(): - save_epoch10 = gr.Slider( - minimum=0, - maximum=50, - step=1, - label=i18n("保存频率save_every_epoch"), - value=5, - interactive=True, - ) - total_epoch11 = gr.Slider( - minimum=0, - maximum=1000, - step=1, - label=i18n("总训练轮数total_epoch"), - value=20, - interactive=True, - ) - batch_size12 = gr.Slider( - minimum=1, - maximum=40, - step=1, - label=i18n("每张显卡的batch_size"), - value=default_batch_size, - interactive=True, - ) - if_save_latest13 = gr.Radio( - label=i18n("是否仅保存最新的ckpt文件以节省硬盘空间"), - choices=[i18n("是"), i18n("否")], - value=i18n("否"), - interactive=True, - ) - if_cache_gpu17 = gr.Radio( - label=i18n( - "是否缓存所有训练集至显存. 10min以下小数据可缓存以加速训练, 大数据缓存会炸显存也加不了多少速" - ), - choices=[i18n("是"), i18n("否")], - value=i18n("否"), - interactive=True, - ) - if_save_every_weights18 = gr.Radio( - label=i18n("是否在每次保存时间点将最终小模型保存至weights文件夹"), - choices=[i18n("是"), i18n("否")], - value=i18n("否"), - interactive=True, - ) - with gr.Row(): - pretrained_G14 = gr.Textbox( - label=i18n("加载预训练底模G路径"), - value="pretrained/f0G40k.pth", - interactive=True, - ) - pretrained_D15 = gr.Textbox( - label=i18n("加载预训练底模D路径"), - value="pretrained/f0D40k.pth", - interactive=True, - ) - sr2.change( - change_sr2, - [sr2, if_f0_3, 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], - ) - gpus16 = gr.Textbox( - label=i18n("以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2"), - value=gpus, - interactive=True, - ) - but3 = gr.Button(i18n("训练模型"), variant="primary") - but4 = gr.Button(i18n("训练特征索引"), variant="primary") - but5 = gr.Button(i18n("一键训练"), variant="primary") - info3 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=10) - but3.click( - click_train, - [ - exp_dir1, - sr2, - if_f0_3, - spk_id5, - save_epoch10, - total_epoch11, - batch_size12, - if_save_latest13, - pretrained_G14, - pretrained_D15, - gpus16, - if_cache_gpu17, - if_save_every_weights18, - version19, - ], - info3, - ) - 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, - ], - info3, - ) - - with gr.TabItem(i18n("ckpt处理")): - with gr.Group(): - gr.Markdown(value=i18n("模型融合, 可用于测试音色融合")) - with gr.Row(): - ckpt_a = gr.Textbox(label=i18n("A模型路径"), value="", interactive=True) - ckpt_b = gr.Textbox(label=i18n("B模型路径"), value="", interactive=True) - alpha_a = gr.Slider( - minimum=0, - maximum=1, - label=i18n("A模型权重"), - value=0.5, - interactive=True, - ) - with gr.Row(): - sr_ = gr.Radio( - label=i18n("目标采样率"), - choices=["32k", "40k", "48k"], - value="40k", - interactive=True, - ) - if_f0_ = gr.Radio( - label=i18n("模型是否带音高指导"), - choices=[i18n("是"), i18n("否")], - value=i18n("是"), - interactive=True, - ) - info__ = gr.Textbox( - label=i18n("要置入的模型信息"), value="", max_lines=8, interactive=True - ) - name_to_save0 = gr.Textbox( - label=i18n("保存的模型名不带后缀"), - value="", - max_lines=1, - interactive=True, - ) - 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"), value="", interactive=True - ) - info_ = gr.Textbox( - label=i18n("要改的模型信息"), value="", max_lines=8, interactive=True - ) - name_to_save1 = gr.Textbox( - label=i18n("保存的文件名, 默认空为和源文件同名"), - value="", - max_lines=8, - interactive=True, - ) - with gr.Row(): - but7 = gr.Button(i18n("修改"), variant="primary") - info5 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) - but7.click(change_info, [ckpt_path0, info_, name_to_save1], info5) - with gr.Group(): - gr.Markdown(value=i18n("查看模型信息(仅支持weights文件夹下提取的小模型文件)")) - with gr.Row(): - ckpt_path1 = gr.Textbox( - label=i18n("模型路径"), value="", interactive=True - ) - but8 = gr.Button(i18n("查看"), variant="primary") - info6 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) - but8.click(show_info, [ckpt_path1], info6) - with gr.Group(): - gr.Markdown( - value=i18n( - "模型提取(输入logs文件夹下大文件模型路径),适用于训一半不想训了模型没有自动提取保存小文件模型,或者想测试中间模型的情况" - ) - ) - with gr.Row(): - ckpt_path2 = gr.Textbox( - label=i18n("模型路径"), - value="E:\\codes\\py39\\logs\\mi-test_f0_48k\\G_23333.pth", - interactive=True, - ) - save_name = gr.Textbox( - label=i18n("保存名"), value="", interactive=True - ) - sr__ = gr.Radio( - label=i18n("目标采样率"), - choices=["32k", "40k", "48k"], - value="40k", - interactive=True, - ) - if_f0__ = gr.Radio( - label=i18n("模型是否带音高指导,1是0否"), - choices=["1", "0"], - value="1", - interactive=True, - ) - version_1 = gr.Radio( - label=i18n("模型版本型号"), - choices=["v1", "v2"], - value="v1", - interactive=True, - ) - info___ = gr.Textbox( - label=i18n("要置入的模型信息"), value="", max_lines=8, interactive=True - ) - but9 = gr.Button(i18n("提取"), variant="primary") - info7 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) - ckpt_path2.change( - change_info_, [ckpt_path2], [sr__, if_f0__, 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) - with gr.Row(): - onnx_dir = gr.Textbox( - label=i18n("Onnx输出路径"), value="", interactive=True - ) - with gr.Row(): - moevs = gr.Checkbox(label=i18n("MoeVS模型"), value=True) - infoOnnx = gr.Label(label="Null") - with gr.Row(): - butOnnx = gr.Button(i18n("导出Onnx模型"), variant="primary") - butOnnx.click(export_onnx, [ckpt_dir, onnx_dir, moevs], infoOnnx) - - 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()) - - # 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: - 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=True, - ) +import torch, os, traceback, sys, warnings, shutil, numpy as np + +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 + +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) +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, "weights"), exist_ok=True) +os.environ["TEMP"] = tmp +warnings.filterwarnings("ignore") +torch.manual_seed(114514) +from i18n import I18nAuto +import ffmpeg + +i18n = I18nAuto() +i18n.print() +# 判断是否有能用来训练和加速推理的N卡 +ngpu = torch.cuda.device_count() +gpu_infos = [] +mem = [] +if (not torch.cuda.is_available()) or ngpu == 0: + if_gpu_ok = False +else: + if_gpu_ok = False + for i in range(ngpu): + gpu_name = torch.cuda.get_device_name(i) + if ( + "10" in gpu_name + or "16" in gpu_name + or "20" in gpu_name + or "30" in gpu_name + or "40" in gpu_name + or "A2" in gpu_name.upper() + or "A3" in gpu_name.upper() + or "A4" in gpu_name.upper() + or "P4" in gpu_name.upper() + or "A50" in gpu_name.upper() + or "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 infer_pack.models import ( + SynthesizerTrnMs256NSFsid, + SynthesizerTrnMs256NSFsid_nono, + SynthesizerTrnMs768NSFsid, + SynthesizerTrnMs768NSFsid_nono, +) +from scipy.io import wavfile +from fairseq import checkpoint_utils +import gradio as gr +import logging +from vc_infer_pipeline import VC +from config import Config +from infer_uvr5 import _audio_pre_ +from my_utils import load_audio +from train.process_ckpt import show_info, change_info, merge, extract_small_model + +config = Config() +# from trainset_preprocess_pipeline import PreProcess +logging.getLogger("numba").setLevel(logging.WARNING) + + +class ToolButton(gr.Button, gr.components.FormComponent): + """Small button with single emoji as text, fits inside gradio forms""" + + def __init__(self, **kwargs): + super().__init__(variant="tool", **kwargs) + + def get_block_name(self): + return "button" + + +hubert_model = None + + +def load_hubert(): + global hubert_model + models, _, _ = checkpoint_utils.load_model_ensemble_and_task( + ["hubert_base.pt"], + suffix="", + ) + hubert_model = models[0] + hubert_model = hubert_model.to(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" +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)) +uvr5_names = [] +for name in os.listdir(weight_uvr5_root): + if name.endswith(".pth"): + uvr5_names.append(name.replace(".pth", "")) + + +def vc_single( + sid, + input_audio_path, + f0_up_key, + f0_file, + f0_method, + file_index, + file_index2, + # file_big_npy, + index_rate, + filter_radius, + resample_sr, + rms_mix_rate, +): # 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) + 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") + ) + 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_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, + 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, +): + 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_index2, + # file_big_npy, + index_rate, + filter_radius, + resample_sr, + rms_mix_rate, + ) + if "Success" in info: + try: + tgt_sr, audio_opt = opt + wavfile.write( + "%s/%s" % (opt_root, os.path.basename(path)), tgt_sr, audio_opt + ) + except: + info += traceback.format_exc() + infos.append("%s->%s" % (os.path.basename(path), info)) + yield "\n".join(infos) + yield "\n".join(infos) + except: + yield traceback.format_exc() + + +def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg): + infos = [] + try: + inp_root = inp_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ") + save_root_vocal = ( + save_root_vocal.strip(" ").strip('"').strip("\n").strip('"').strip(" ") + ) + save_root_ins = ( + save_root_ins.strip(" ").strip('"').strip("\n").strip('"').strip(" ") + ) + pre_fun = _audio_pre_( + agg=int(agg), + model_path=os.path.join(weight_uvr5_root, model_name + ".pth"), + device=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) + done = 1 + except: + need_reformat = 1 + traceback.print_exc() + if need_reformat == 1: + tmp_path = "%s/%s.reformatted.wav" % (tmp, os.path.basename(inp_path)) + os.system( + "ffmpeg -i %s -vn -acodec pcm_s16le -ac 2 -ar 44100 %s -y" + % (inp_path, tmp_path) + ) + inp_path = tmp_path + try: + if done == 0: + pre_fun._path_audio_(inp_path, save_root_ins, save_root_vocal) + infos.append("%s->Success" % (os.path.basename(inp_path))) + yield "\n".join(infos) + except: + infos.append( + "%s->%s" % (os.path.basename(inp_path), traceback.format_exc()) + ) + yield "\n".join(infos) + except: + infos.append(traceback.format_exc()) + yield "\n".join(infos) + finally: + try: + del pre_fun.model + del pre_fun + except: + traceback.print_exc() + print("clean_empty_cache") + if torch.cuda.is_available(): + torch.cuda.empty_cache() + yield "\n".join(infos) + + +# 一个选项卡全局只能有一个音色 +def get_vc(sid): + global n_spk, tgt_sr, net_g, vc, cpt, 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": True, "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): + 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" % ( + now_dir, + exp_dir, + n_p, + f0method, + ) + print(cmd) + p = Popen(cmd, shell=True, cwd=now_dir) # , stdin=PIPE, stdout=PIPE,stderr=PIPE + ###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读 + done = [False] + threading.Thread( + target=if_done, + args=( + done, + p, + ), + ).start() + while 1: + with open( + "%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r" + ) as f: + yield (f.read()) + sleep(1) + if done[0] == True: + break + with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f: + log = f.read() + print(log) + yield log + ####对不同part分别开多进程 + """ + n_part=int(sys.argv[1]) + i_part=int(sys.argv[2]) + i_gpu=sys.argv[3] + exp_dir=sys.argv[4] + os.environ["CUDA_VISIBLE_DEVICES"]=str(i_gpu) + """ + leng = len(gpus) + ps = [] + for idx, n_g in enumerate(gpus): + cmd = ( + 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): + vis_v = True if sr2 == "40k" else False + if sr2 != "40k": + version19 = "v1" + path_str = "" if version19 == "v1" else "_v2" + version_state = {"visible": vis_v, "__type__": "update"} + if vis_v == False: + version_state["value"] = "v1" + f0_str = "f0" if if_f0_3 else "" + return ( + "pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), + "pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), + version_state, + ) + + +def change_version19(sr2, if_f0_3, version19): + path_str = "" if version19 == "v1" else "_v2" + f0_str = "f0" if if_f0_3 else "" + return "pretrained%s/%sG%s.pth" % ( + path_str, + f0_str, + sr2, + ), "pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2) + + +def change_f0(if_f0_3, sr2, version19): # f0method8,pretrained_G14,pretrained_D15 + path_str = "" if version19 == "v1" else "_v2" + if if_f0_3: + return ( + {"visible": True, "__type__": "update"}, + "pretrained%s/f0G%s.pth" % (path_str, sr2), + "pretrained%s/f0D%s.pth" % (path_str, sr2), + ) + return ( + {"visible": False, "__type__": "update"}, + "pretrained%s/G%s.pth" % (path_str, sr2), + "pretrained%s/D%s.pth" % (path_str, sr2), + ) + + +# but3.click(click_train,[exp_dir1,sr2,if_f0_3,save_epoch10,total_epoch11,batch_size12,if_save_latest13,pretrained_G14,pretrained_D15,gpus16]) +def click_train( + exp_dir1, + sr2, + if_f0_3, + spk_id5, + save_epoch10, + total_epoch11, + batch_size12, + if_save_latest13, + pretrained_G14, + pretrained_D15, + gpus16, + if_cache_gpu17, + if_save_every_weights18, + version19, +): + # 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) + ) + 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 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 -pg %s -pd %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, + pretrained_G14, + pretrained_D15, + 1 if if_save_latest13 == i18n("是") else 0, + 1 if if_cache_gpu17 == i18n("是") else 0, + 1 if if_save_every_weights18 == i18n("是") 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 -pg %s -pd %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, + pretrained_G14, + pretrained_D15, + 1 if if_save_latest13 == i18n("是") else 0, + 1 if if_cache_gpu17 == i18n("是") else 0, + 1 if if_save_every_weights18 == i18n("是") else 0, + version19, + ) + ) + print(cmd) + p = Popen(cmd, shell=True, cwd=now_dir) + p.wait() + return "训练结束, 您可查看控制台训练日志或实验文件夹下的train.log" + + +# but4.click(train_index, [exp_dir1], info3) +def train_index(exp_dir1, 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.index" + % (exp_dir, n_ivf, index_ivf.nprobe, 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.index" + % (exp_dir, n_ivf, index_ivf.nprobe, version19), + ) + infos.append( + "成功构建索引,added_IVF%s_Flat_nprobe_%s_%s.index" + % (n_ivf, index_ivf.nprobe, 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, +): + 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" % ( + model_log_dir, + np7, + f0method8, + ) + 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 -pg %s -pd %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, + pretrained_G14, + pretrained_D15, + 1 if if_save_latest13 == i18n("是") else 0, + 1 if if_cache_gpu17 == i18n("是") else 0, + 1 if if_save_every_weights18 == i18n("是") 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 -pg %s -pd %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, + pretrained_G14, + pretrained_D15, + 1 if if_save_latest13 == i18n("是") else 0, + 1 if if_cache_gpu17 == i18n("是") else 0, + 1 if if_save_every_weights18 == i18n("是") 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.index" + % (model_log_dir, n_ivf, index_ivf.nprobe, 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.index" + % (model_log_dir, n_ivf, index_ivf.nprobe, version19), + ) + yield get_info_str( + "成功构建索引, added_IVF%s_Flat_nprobe_%s_%s.index" + % (n_ivf, index_ivf.nprobe, 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 ( + 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 infer_pack.models_onnx_moess import SynthesizerTrnMs256NSFsidM +from infer_pack.models_onnx import SynthesizerTrnMs256NSFsidO + + +def export_onnx(ModelPath, ExportedPath, MoeVS=True): + hidden_channels = 256 # hidden_channels,为768Vec做准备 + cpt = torch.load(ModelPath, map_location="cpu") + cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk + print(*cpt["config"]) + + test_phone = torch.rand(1, 200, hidden_channels) # hidden unit + test_phone_lengths = torch.tensor([200]).long() # hidden unit 长度(貌似没啥用) + test_pitch = torch.randint(size=(1, 200), low=5, high=255) # 基频(单位赫兹) + test_pitchf = torch.rand(1, 200) # nsf基频 + test_ds = torch.LongTensor([0]) # 说话人ID + test_rnd = torch.rand(1, 192, 200) # 噪声(加入随机因子) + + device = "cpu" # 导出时设备(不影响使用模型) + + if MoeVS: + net_g = SynthesizerTrnMs256NSFsidM( + *cpt["config"], is_half=False + ) # fp32导出(C++要支持fp16必须手动将内存重新排列所以暂时不用fp16) + net_g.load_state_dict(cpt["weight"], strict=False) + input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"] + output_names = [ + "audio", + ] + torch.onnx.export( + net_g, + ( + test_phone.to(device), + test_phone_lengths.to(device), + test_pitch.to(device), + test_pitchf.to(device), + test_ds.to(device), + test_rnd.to(device), + ), + ExportedPath, + dynamic_axes={ + "phone": [1], + "pitch": [1], + "pitchf": [1], + "rnd": [2], + }, + do_constant_folding=False, + opset_version=16, + verbose=False, + input_names=input_names, + output_names=output_names, + ) + else: + net_g = SynthesizerTrnMs256NSFsidO( + *cpt["config"], is_half=False + ) # fp32导出(C++要支持fp16必须手动将内存重新排列所以暂时不用fp16) + net_g.load_state_dict(cpt["weight"], strict=False) + input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds"] + output_names = [ + "audio", + ] + torch.onnx.export( + net_g, + ( + test_phone.to(device), + test_phone_lengths.to(device), + test_pitch.to(device), + test_pitchf.to(device), + test_ds.to(device), + ), + ExportedPath, + dynamic_axes={ + "phone": [1], + "pitch": [1], + "pitchf": [1], + }, + do_constant_folding=False, + opset_version=16, + verbose=False, + input_names=input_names, + output_names=output_names, + ) + return "Finished" + + +with gr.Blocks() as app: + gr.Markdown( + value=i18n( + "本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.
如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录使用需遵守的协议-LICENSE.txt." + ) + ) + with gr.Tabs(): + with gr.TabItem(i18n("模型推理")): + with gr.Row(): + sid0 = gr.Dropdown(label=i18n("推理音色"), choices=sorted(names)) + refresh_button = gr.Button(i18n("刷新音色列表和索引路径"), 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]) + sid0.change( + fn=get_vc, + inputs=[sid0], + outputs=[spk_item], + ) + with gr.Group(): + gr.Markdown( + value=i18n("男转女推荐+12key, 女转男推荐-12key, 如果音域爆炸导致音色失真也可以自己调整到合适音域. ") + ) + with gr.Row(): + with gr.Column(): + vc_transform0 = gr.Number( + label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0 + ) + input_audio0 = gr.Textbox( + label=i18n("输入待处理音频文件路径(默认是正确格式示例)"), + value="E:\\codes\\py39\\test-20230416b\\todo-songs\\冬之花clip1.wav", + ) + f0method0 = gr.Radio( + label=i18n("选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比"), + choices=["pm", "harvest"], + value="pm", + interactive=True, + ) + 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=i18n("自动检测index路径,下拉式选择(dropdown)"), + choices=sorted(index_paths), + interactive=True, + ) + refresh_button.click( + fn=change_choices, inputs=[], outputs=[sid0, file_index2] + ) + # 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=0.76, + 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, + ) + rms_mix_rate0 = gr.Slider( + minimum=0, + maximum=1, + label=i18n("输入源音量包络替换输出音量包络融合比例,越靠近1越使用输出包络"), + value=1, + interactive=True, + ) + f0_file = gr.File(label=i18n("F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调")) + but0 = gr.Button(i18n("转换"), variant="primary") + with gr.Column(): + vc_output1 = gr.Textbox(label=i18n("输出信息")) + vc_output2 = gr.Audio(label=i18n("输出音频(右下角三个点,点了可以下载)")) + but0.click( + vc_single, + [ + spk_item, + input_audio0, + vc_transform0, + f0_file, + f0method0, + file_index1, + file_index2, + # file_big_npy1, + index_rate1, + filter_radius0, + resample_sr0, + rms_mix_rate0, + ], + [vc_output1, vc_output2], + ) + with gr.Group(): + gr.Markdown( + value=i18n("批量转换, 输入待转换音频文件夹, 或上传多个音频文件, 在指定文件夹(默认opt)下输出转换的音频. ") + ) + with gr.Row(): + with gr.Column(): + vc_transform1 = gr.Number( + label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0 + ) + opt_input = gr.Textbox(label=i18n("指定输出文件夹"), value="opt") + f0method1 = gr.Radio( + label=i18n("选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比"), + choices=["pm", "harvest"], + value="pm", + interactive=True, + ) + 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, + ) + # 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, + ) + 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, + ) + 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") + ) + 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, + ], + [vc_output3], + ) + with gr.TabItem(i18n("伴奏人声分离")): + with gr.Group(): + gr.Markdown( + value=i18n( + "人声伴奏分离批量处理, 使用UVR5模型.
不带和声用HP2, 带和声且提取的人声不需要和声用HP5
合格的文件夹路径格式举例: E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(去文件管理器地址栏拷就行了)" + ) + ) + with gr.Row(): + with gr.Column(): + dir_wav_input = gr.Textbox( + label=i18n("输入待处理音频文件夹路径"), + value="E:\\codes\\py39\\test-20230416b\\todo-songs\\todo-songs", + ) + wav_inputs = gr.File( + file_count="multiple", label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹") + ) + with gr.Column(): + model_choose = gr.Dropdown(label=i18n("模型"), choices=uvr5_names) + agg = gr.Slider( + minimum=0, + maximum=20, + step=1, + label="人声提取激进程度", + value=10, + interactive=True, + visible=False, # 先不开放调整 + ) + opt_vocal_root = gr.Textbox( + label=i18n("指定输出人声文件夹"), value="opt" + ) + opt_ins_root = gr.Textbox(label=i18n("指定输出乐器文件夹"), value="opt") + but2 = gr.Button(i18n("转换"), variant="primary") + vc_output4 = gr.Textbox(label=i18n("输出信息")) + but2.click( + uvr, + [ + model_choose, + dir_wav_input, + opt_vocal_root, + wav_inputs, + opt_ins_root, + agg, + ], + [vc_output4], + ) + with gr.TabItem(i18n("训练")): + gr.Markdown( + value=i18n( + "step1: 填写实验配置. 实验数据放在logs下, 每个实验一个文件夹, 需手工输入实验名路径, 内含实验配置, 日志, 训练得到的模型文件. " + ) + ) + with gr.Row(): + exp_dir1 = gr.Textbox(label=i18n("输入实验名"), value="mi-test") + sr2 = gr.Radio( + label=i18n("目标采样率"), + choices=["32k", "40k", "48k"], + value="40k", + interactive=True, + ) + if_f0_3 = gr.Radio( + label=i18n("模型是否带音高指导(唱歌一定要, 语音可以不要)"), + choices=[True, False], + value=True, + interactive=True, + ) + version19 = gr.Radio( + label=i18n("版本(目前仅40k支持了v2)"), + 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=config.n_cpu, + interactive=True, + ) + with gr.Group(): # 暂时单人的, 后面支持最多4人的#数据处理 + gr.Markdown( + value=i18n( + "step2a: 自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练. " + ) + ) + with gr.Row(): + trainset_dir4 = gr.Textbox( + label=i18n("输入训练文件夹路径"), value="E:\\语音音频+标注\\米津玄师\\src" + ) + spk_id5 = gr.Slider( + minimum=0, + maximum=4, + step=1, + label=i18n("请指定说话人id"), + value=0, + interactive=True, + ) + but1 = gr.Button(i18n("处理数据"), variant="primary") + info1 = gr.Textbox(label=i18n("输出信息"), value="") + but1.click( + preprocess_dataset, [trainset_dir4, exp_dir1, sr2, np7], [info1] + ) + with gr.Group(): + gr.Markdown(value=i18n("step2b: 使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号)")) + with gr.Row(): + with gr.Column(): + gpus6 = gr.Textbox( + label=i18n("以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2"), + value=gpus, + interactive=True, + ) + gpu_info9 = gr.Textbox(label=i18n("显卡信息"), value=gpu_info) + with gr.Column(): + f0method8 = gr.Radio( + label=i18n( + "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢" + ), + choices=["pm", "harvest", "dio"], + value="harvest", + interactive=True, + ) + but2 = gr.Button(i18n("特征提取"), variant="primary") + info2 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) + but2.click( + extract_f0_feature, + [gpus6, np7, f0method8, if_f0_3, exp_dir1, version19], + [info2], + ) + with gr.Group(): + gr.Markdown(value=i18n("step3: 填写训练设置, 开始训练模型和索引")) + with gr.Row(): + save_epoch10 = gr.Slider( + minimum=0, + maximum=50, + step=1, + label=i18n("保存频率save_every_epoch"), + value=5, + interactive=True, + ) + total_epoch11 = gr.Slider( + minimum=0, + maximum=1000, + step=1, + label=i18n("总训练轮数total_epoch"), + value=20, + interactive=True, + ) + batch_size12 = gr.Slider( + minimum=1, + maximum=40, + step=1, + label=i18n("每张显卡的batch_size"), + value=default_batch_size, + interactive=True, + ) + if_save_latest13 = gr.Radio( + label=i18n("是否仅保存最新的ckpt文件以节省硬盘空间"), + choices=[i18n("是"), i18n("否")], + value=i18n("否"), + interactive=True, + ) + if_cache_gpu17 = gr.Radio( + label=i18n( + "是否缓存所有训练集至显存. 10min以下小数据可缓存以加速训练, 大数据缓存会炸显存也加不了多少速" + ), + choices=[i18n("是"), i18n("否")], + value=i18n("否"), + interactive=True, + ) + if_save_every_weights18 = gr.Radio( + label=i18n("是否在每次保存时间点将最终小模型保存至weights文件夹"), + choices=[i18n("是"), i18n("否")], + value=i18n("否"), + interactive=True, + ) + with gr.Row(): + pretrained_G14 = gr.Textbox( + label=i18n("加载预训练底模G路径"), + value="pretrained/f0G40k.pth", + interactive=True, + ) + pretrained_D15 = gr.Textbox( + label=i18n("加载预训练底模D路径"), + value="pretrained/f0D40k.pth", + interactive=True, + ) + sr2.change( + change_sr2, + [sr2, if_f0_3, 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], + ) + gpus16 = gr.Textbox( + label=i18n("以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2"), + value=gpus, + interactive=True, + ) + but3 = gr.Button(i18n("训练模型"), variant="primary") + but4 = gr.Button(i18n("训练特征索引"), variant="primary") + but5 = gr.Button(i18n("一键训练"), variant="primary") + info3 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=10) + but3.click( + click_train, + [ + exp_dir1, + sr2, + if_f0_3, + spk_id5, + save_epoch10, + total_epoch11, + batch_size12, + if_save_latest13, + pretrained_G14, + pretrained_D15, + gpus16, + if_cache_gpu17, + if_save_every_weights18, + version19, + ], + info3, + ) + 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, + ], + info3, + ) + + with gr.TabItem(i18n("ckpt处理")): + with gr.Group(): + gr.Markdown(value=i18n("模型融合, 可用于测试音色融合")) + with gr.Row(): + ckpt_a = gr.Textbox(label=i18n("A模型路径"), value="", interactive=True) + ckpt_b = gr.Textbox(label=i18n("B模型路径"), value="", interactive=True) + alpha_a = gr.Slider( + minimum=0, + maximum=1, + label=i18n("A模型权重"), + value=0.5, + interactive=True, + ) + with gr.Row(): + sr_ = gr.Radio( + label=i18n("目标采样率"), + choices=["32k", "40k", "48k"], + value="40k", + interactive=True, + ) + if_f0_ = gr.Radio( + label=i18n("模型是否带音高指导"), + choices=[i18n("是"), i18n("否")], + value=i18n("是"), + interactive=True, + ) + info__ = gr.Textbox( + label=i18n("要置入的模型信息"), value="", max_lines=8, interactive=True + ) + name_to_save0 = gr.Textbox( + label=i18n("保存的模型名不带后缀"), + value="", + max_lines=1, + interactive=True, + ) + 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"), value="", interactive=True + ) + info_ = gr.Textbox( + label=i18n("要改的模型信息"), value="", max_lines=8, interactive=True + ) + name_to_save1 = gr.Textbox( + label=i18n("保存的文件名, 默认空为和源文件同名"), + value="", + max_lines=8, + interactive=True, + ) + with gr.Row(): + but7 = gr.Button(i18n("修改"), variant="primary") + info5 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) + but7.click(change_info, [ckpt_path0, info_, name_to_save1], info5) + with gr.Group(): + gr.Markdown(value=i18n("查看模型信息(仅支持weights文件夹下提取的小模型文件)")) + with gr.Row(): + ckpt_path1 = gr.Textbox( + label=i18n("模型路径"), value="", interactive=True + ) + but8 = gr.Button(i18n("查看"), variant="primary") + info6 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) + but8.click(show_info, [ckpt_path1], info6) + with gr.Group(): + gr.Markdown( + value=i18n( + "模型提取(输入logs文件夹下大文件模型路径),适用于训一半不想训了模型没有自动提取保存小文件模型,或者想测试中间模型的情况" + ) + ) + with gr.Row(): + ckpt_path2 = gr.Textbox( + label=i18n("模型路径"), + value="E:\\codes\\py39\\logs\\mi-test_f0_48k\\G_23333.pth", + interactive=True, + ) + save_name = gr.Textbox( + label=i18n("保存名"), value="", interactive=True + ) + sr__ = gr.Radio( + label=i18n("目标采样率"), + choices=["32k", "40k", "48k"], + value="40k", + interactive=True, + ) + if_f0__ = gr.Radio( + label=i18n("模型是否带音高指导,1是0否"), + choices=["1", "0"], + value="1", + interactive=True, + ) + version_1 = gr.Radio( + label=i18n("模型版本型号"), + choices=["v1", "v2"], + value="v1", + interactive=True, + ) + info___ = gr.Textbox( + label=i18n("要置入的模型信息"), value="", max_lines=8, interactive=True + ) + but9 = gr.Button(i18n("提取"), variant="primary") + info7 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) + ckpt_path2.change( + change_info_, [ckpt_path2], [sr__, if_f0__, 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) + with gr.Row(): + onnx_dir = gr.Textbox( + label=i18n("Onnx输出路径"), value="", interactive=True + ) + with gr.Row(): + moevs = gr.Checkbox(label=i18n("MoeVS模型"), value=True) + infoOnnx = gr.Label(label="Null") + with gr.Row(): + butOnnx = gr.Button(i18n("导出Onnx模型"), variant="primary") + butOnnx.click(export_onnx, [ckpt_dir, onnx_dir, moevs], infoOnnx) + + 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()) + + # 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: + 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=True, + )