From 790405413c09e8379e77e4b926417e3fc869809a Mon Sep 17 00:00:00 2001 From: alexlnkp <79400603+alexlnkp@users.noreply.github.com> Date: Fri, 21 Jul 2023 20:36:54 +0700 Subject: [PATCH 01/14] Tweaked files upload and replace --- formanting.txt | 3 + ...ur formantshift presets here as a txt file | 0 formantshiftcfg/f2m.txt | 2 + formantshiftcfg/m2f.txt | 2 + formantshiftcfg/random.txt | 2 + infer-web.py | 6170 ++++++++++------- installstft.bat | 6 + my_utils.py | 48 +- 8 files changed, 3756 insertions(+), 2477 deletions(-) create mode 100644 formanting.txt create mode 100644 formantshiftcfg/Put your formantshift presets here as a txt file create mode 100644 formantshiftcfg/f2m.txt create mode 100644 formantshiftcfg/m2f.txt create mode 100644 formantshiftcfg/random.txt create mode 100644 installstft.bat diff --git a/formanting.txt b/formanting.txt new file mode 100644 index 0000000..86ee10e --- /dev/null +++ b/formanting.txt @@ -0,0 +1,3 @@ +False +8.0 +1.2 diff --git a/formantshiftcfg/Put your formantshift presets here as a txt file b/formantshiftcfg/Put your formantshift presets here as a txt file new file mode 100644 index 0000000..e69de29 diff --git a/formantshiftcfg/f2m.txt b/formantshiftcfg/f2m.txt new file mode 100644 index 0000000..9b33ee5 --- /dev/null +++ b/formantshiftcfg/f2m.txt @@ -0,0 +1,2 @@ +8.0 +-1.2 \ No newline at end of file diff --git a/formantshiftcfg/m2f.txt b/formantshiftcfg/m2f.txt new file mode 100644 index 0000000..8cd0390 --- /dev/null +++ b/formantshiftcfg/m2f.txt @@ -0,0 +1,2 @@ +8.0 +1.2 \ No newline at end of file diff --git a/formantshiftcfg/random.txt b/formantshiftcfg/random.txt new file mode 100644 index 0000000..3297289 --- /dev/null +++ b/formantshiftcfg/random.txt @@ -0,0 +1,2 @@ +16.0 +9.8 \ No newline at end of file diff --git a/infer-web.py b/infer-web.py index 0a8603f..02b306b 100644 --- a/infer-web.py +++ b/infer-web.py @@ -1,2472 +1,3698 @@ -import os -import shutil -import sys - -import json # Mangio fork using json for preset saving - -now_dir = os.getcwd() -sys.path.append(now_dir) -import traceback, pdb -import warnings - -import numpy as np -import torch - -os.environ["OPENBLAS_NUM_THREADS"] = "1" -os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1" -import logging -import threading -from random import shuffle -from subprocess import Popen -from time import sleep - -import faiss -import ffmpeg -import gradio as gr -import soundfile as sf -from config import Config -from fairseq import checkpoint_utils -from i18n import I18nAuto -from lib.infer_pack.models import ( - SynthesizerTrnMs256NSFsid, - SynthesizerTrnMs256NSFsid_nono, - SynthesizerTrnMs768NSFsid, - SynthesizerTrnMs768NSFsid_nono, -) -from lib.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM -from infer_uvr5 import _audio_pre_, _audio_pre_new -from MDXNet import MDXNetDereverb -from my_utils import load_audio -from train.process_ckpt import change_info, extract_small_model, merge, show_info -from vc_infer_pipeline import VC -from sklearn.cluster import MiniBatchKMeans - -logging.getLogger("numba").setLevel(logging.WARNING) - - -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) - - -config = Config() -i18n = I18nAuto() -i18n.print() -# 判断是否有能用来训练和加速推理的N卡 -ngpu = torch.cuda.device_count() -gpu_infos = [] -mem = [] -if_gpu_ok = False - -if torch.cuda.is_available() or ngpu != 0: - for i in range(ngpu): - gpu_name = torch.cuda.get_device_name(i) - if any( - value in gpu_name.upper() - for value in [ - "10", - "16", - "20", - "30", - "40", - "A2", - "A3", - "A4", - "P4", - "A50", - "500", - "A60", - "70", - "80", - "90", - "M4", - "T4", - "TITAN", - ] - ): - # A10#A100#V100#A40#P40#M40#K80#A4500 - if_gpu_ok = True # 至少有一张能用的N卡 - gpu_infos.append("%s\t%s" % (i, gpu_name)) - mem.append( - int( - torch.cuda.get_device_properties(i).total_memory - / 1024 - / 1024 - / 1024 - + 0.4 - ) - ) -if if_gpu_ok and len(gpu_infos) > 0: - gpu_info = "\n".join(gpu_infos) - default_batch_size = min(mem) // 2 -else: - gpu_info = i18n("很遗憾您这没有能用的显卡来支持您训练") - default_batch_size = 1 -gpus = "-".join([i[0] for i in gpu_infos]) - - -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") or "onnx" in name: - 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, - 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) - audio_max = np.abs(audio).max() / 0.95 - if audio_max > 1: - audio /= audio_max - times = [0, 0, 0] - if not hubert_model: - load_hubert() - if_f0 = cpt.get("f0", 1) - file_index = ( - ( - file_index.strip(" ") - .strip('"') - .strip("\n") - .strip('"') - .strip(" ") - .replace("trained", "added") - ) - if file_index != "" - else file_index2 - ) # 防止小白写错,自动帮他替换掉 - # file_big_npy = ( - # file_big_npy.strip(" ").strip('"').strip("\n").strip('"').strip(" ") - # ) - audio_opt = vc.pipeline( - hubert_model, - net_g, - sid, - audio, - input_audio_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 tgt_sr != resample_sr >= 16000: - tgt_sr = resample_sr - index_info = ( - "Using index:%s." % file_index - if os.path.exists(file_index) - else "Index not used." - ) - return "Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss" % ( - index_info, - times[0], - times[1], - times[2], - ), (tgt_sr, audio_opt) - except: - info = traceback.format_exc() - print(info) - return info, (None, None) - - -def vc_multi( - sid, - dir_path, - opt_root, - paths, - f0_up_key, - f0_method, - file_index, - file_index2, - # file_big_npy, - index_rate, - filter_radius, - resample_sr, - rms_mix_rate, - protect, - format1, - crepe_hop_length, -): - try: - dir_path = ( - dir_path.strip(" ").strip('"').strip("\n").strip('"').strip(" ") - ) # 防止小白拷路径头尾带了空格和"和回车 - opt_root = opt_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ") - os.makedirs(opt_root, exist_ok=True) - try: - if dir_path != "": - paths = [os.path.join(dir_path, name) for name in os.listdir(dir_path)] - else: - paths = [path.name for path in paths] - except: - traceback.print_exc() - paths = [path.name for path in paths] - infos = [] - for path in paths: - info, opt = vc_single( - sid, - path, - f0_up_key, - None, - f0_method, - file_index, - file_index2, - # file_big_npy, - index_rate, - filter_radius, - resample_sr, - rms_mix_rate, - protect, - crepe_hop_length - ) - if "Success" in info: - try: - tgt_sr, audio_opt = opt - if format1 in ["wav", "flac"]: - sf.write( - "%s/%s.%s" % (opt_root, os.path.basename(path), format1), - audio_opt, - tgt_sr, - ) - else: - path = "%s/%s.wav" % (opt_root, os.path.basename(path)) - sf.write( - path, - audio_opt, - tgt_sr, - ) - if os.path.exists(path): - os.system( - "ffmpeg -i %s -vn %s -q:a 2 -y" - % (path, path[:-4] + ".%s" % format1) - ) - except: - info += traceback.format_exc() - infos.append("%s->%s" % (os.path.basename(path), info)) - yield "\n".join(infos) - yield "\n".join(infos) - except: - yield traceback.format_exc() - - -def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format0): - infos = [] - try: - inp_root = inp_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ") - save_root_vocal = ( - save_root_vocal.strip(" ").strip('"').strip("\n").strip('"').strip(" ") - ) - save_root_ins = ( - save_root_ins.strip(" ").strip('"').strip("\n").strip('"').strip(" ") - ) - if model_name == "onnx_dereverb_By_FoxJoy": - pre_fun = MDXNetDereverb(15) - else: - func = _audio_pre_ if "DeEcho" not in model_name else _audio_pre_new - pre_fun = func( - agg=int(agg), - model_path=os.path.join(weight_uvr5_root, model_name + ".pth"), - device=config.device, - is_half=config.is_half, - ) - if inp_root != "": - paths = [os.path.join(inp_root, name) for name in os.listdir(inp_root)] - else: - paths = [path.name for path in paths] - for path in paths: - inp_path = os.path.join(inp_root, path) - need_reformat = 1 - done = 0 - try: - info = ffmpeg.probe(inp_path, cmd="ffprobe") - if ( - info["streams"][0]["channels"] == 2 - and info["streams"][0]["sample_rate"] == "44100" - ): - need_reformat = 0 - pre_fun._path_audio_( - inp_path, save_root_ins, save_root_vocal, format0 - ) - done = 1 - except: - need_reformat = 1 - traceback.print_exc() - if need_reformat == 1: - tmp_path = "%s/%s.reformatted.wav" % (tmp, os.path.basename(inp_path)) - os.system( - "ffmpeg -i %s -vn -acodec pcm_s16le -ac 2 -ar 44100 %s -y" - % (inp_path, tmp_path) - ) - inp_path = tmp_path - try: - if done == 0: - pre_fun._path_audio_( - inp_path, save_root_ins, save_root_vocal, format0 - ) - infos.append("%s->Success" % (os.path.basename(inp_path))) - yield "\n".join(infos) - except: - infos.append( - "%s->%s" % (os.path.basename(inp_path), traceback.format_exc()) - ) - yield "\n".join(infos) - except: - infos.append(traceback.format_exc()) - yield "\n".join(infos) - finally: - try: - if model_name == "onnx_dereverb_By_FoxJoy": - del pre_fun.pred.model - del pre_fun.pred.model_ - else: - del pre_fun.model - del pre_fun - except: - traceback.print_exc() - print("clean_empty_cache") - if torch.cuda.is_available(): - torch.cuda.empty_cache() - yield "\n".join(infos) - - -# 一个选项卡全局只能有一个音色 -def get_vc(sid, to_return_protect0, to_return_protect1): - global n_spk, tgt_sr, net_g, vc, cpt, version - if sid == "" or sid == []: - global hubert_model - if hubert_model is not None: # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的 - print("clean_empty_cache") - del net_g, n_spk, vc, hubert_model, tgt_sr # ,cpt - hubert_model = net_g = n_spk = vc = hubert_model = tgt_sr = None - if torch.cuda.is_available(): - torch.cuda.empty_cache() - ###楼下不这么折腾清理不干净 - if_f0 = cpt.get("f0", 1) - version = cpt.get("version", "v1") - if version == "v1": - if if_f0 == 1: - net_g = SynthesizerTrnMs256NSFsid( - *cpt["config"], is_half=config.is_half - ) - else: - net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) - elif version == "v2": - if if_f0 == 1: - net_g = SynthesizerTrnMs768NSFsid( - *cpt["config"], is_half=config.is_half - ) - else: - net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"]) - del net_g, cpt - if torch.cuda.is_available(): - torch.cuda.empty_cache() - cpt = None - return {"visible": False, "__type__": "update"} - person = "%s/%s" % (weight_root, sid) - print("loading %s" % person) - cpt = torch.load(person, map_location="cpu") - tgt_sr = cpt["config"][-1] - cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk - if_f0 = cpt.get("f0", 1) - if if_f0 == 0: - to_return_protect0 = to_return_protect1 = { - "visible": False, - "value": 0.5, - "__type__": "update", - } - else: - to_return_protect0 = { - "visible": True, - "value": to_return_protect0, - "__type__": "update", - } - to_return_protect1 = { - "visible": True, - "value": to_return_protect1, - "__type__": "update", - } - version = cpt.get("version", "v1") - if version == "v1": - if if_f0 == 1: - net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half) - else: - net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) - elif version == "v2": - if if_f0 == 1: - net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half) - else: - net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"]) - del net_g.enc_q - print(net_g.load_state_dict(cpt["weight"], strict=False)) - net_g.eval().to(config.device) - if config.is_half: - net_g = net_g.half() - else: - net_g = net_g.float() - vc = VC(tgt_sr, config) - n_spk = cpt["config"][-3] - return ( - {"visible": True, "maximum": n_spk, "__type__": "update"}, - to_return_protect0, - to_return_protect1, - ) - - -def change_choices(): - names = [] - for name in os.listdir(weight_root): - if name.endswith(".pth"): - names.append(name) - index_paths = [] - 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() is None: - sleep(0.5) - else: - break - done[0] = True - - -def if_done_multi(done, ps): - while 1: - # poll==None代表进程未结束 - # 只要有一个进程未结束都不停 - flag = 1 - for p in ps: - if p.poll() is None: - flag = 0 - sleep(0.5) - break - if flag == 1: - break - done[0] = True - - -def preprocess_dataset(trainset_dir, exp_dir, sr, n_p): - sr = sr_dict[sr] - os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True) - f = open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "w") - f.close() - cmd = ( - config.python_cmd - + " trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s " - % (trainset_dir, sr, n_p, now_dir, exp_dir) - + str(config.noparallel) - ) - print(cmd) - p = Popen(cmd, shell=True) # , stdin=PIPE, stdout=PIPE,stderr=PIPE,cwd=now_dir - ###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读 - done = [False] - threading.Thread( - target=if_done, - args=( - done, - p, - ), - ).start() - while 1: - with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f: - yield (f.read()) - sleep(1) - if done[0]: - break - with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f: - log = f.read() - print(log) - yield log - - -# but2.click(extract_f0,[gpus6,np7,f0method8,if_f0_3,trainset_dir4],[info2]) -def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, echl): - gpus = gpus.split("-") - os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True) - f = open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "w") - f.close() - if if_f0: - cmd = config.python_cmd + " extract_f0_print.py %s/logs/%s %s %s %s" % ( - now_dir, - exp_dir, - n_p, - f0method, - echl, - ) - print(cmd) - p = Popen(cmd, shell=True, cwd=now_dir) # , stdin=PIPE, stdout=PIPE,stderr=PIPE - ###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读 - done = [False] - threading.Thread( - target=if_done, - args=( - done, - p, - ), - ).start() - while 1: - with open( - "%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r" - ) as f: - yield (f.read()) - sleep(1) - if done[0]: - break - with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f: - log = f.read() - print(log) - yield log - ####对不同part分别开多进程 - """ - n_part=int(sys.argv[1]) - i_part=int(sys.argv[2]) - i_gpu=sys.argv[3] - exp_dir=sys.argv[4] - os.environ["CUDA_VISIBLE_DEVICES"]=str(i_gpu) - """ - leng = len(gpus) - ps = [] - for idx, n_g in enumerate(gpus): - cmd = ( - config.python_cmd - + " extract_feature_print.py %s %s %s %s %s/logs/%s %s" - % ( - config.device, - leng, - idx, - n_g, - now_dir, - exp_dir, - version19, - ) - ) - print(cmd) - p = Popen( - cmd, shell=True, cwd=now_dir - ) # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir - ps.append(p) - ###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读 - done = [False] - threading.Thread( - target=if_done_multi, - args=( - done, - ps, - ), - ).start() - while 1: - with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f: - yield (f.read()) - sleep(1) - if done[0]: - break - with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f: - log = f.read() - print(log) - yield log - - -def change_sr2(sr2, if_f0_3, version19): - path_str = "" if version19 == "v1" else "_v2" - f0_str = "f0" if if_f0_3 else "" - if_pretrained_generator_exist = os.access( - "pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK - ) - if_pretrained_discriminator_exist = os.access( - "pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK - ) - if not if_pretrained_generator_exist: - print( - "pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), - "not exist, will not use pretrained model", - ) - if not if_pretrained_discriminator_exist: - 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_version19(sr2, if_f0_3, version19): - path_str = "" if version19 == "v1" else "_v2" - if sr2 == "32k" and version19 == "v1": - sr2 = "40k" - to_return_sr2 = ( - {"choices": ["40k", "48k"], "__type__": "update", "value": sr2} - if version19 == "v1" - else {"choices": ["40k", "48k", "32k"], "__type__": "update", "value": sr2} - ) - f0_str = "f0" if if_f0_3 else "" - if_pretrained_generator_exist = os.access( - "pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK - ) - if_pretrained_discriminator_exist = os.access( - "pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK - ) - if not if_pretrained_generator_exist: - print( - "pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), - "not exist, will not use pretrained model", - ) - if not if_pretrained_discriminator_exist: - 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 "", - to_return_sr2, - ) - - -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 not if_pretrained_generator_exist: - print( - "pretrained%s/f0G%s.pth" % (path_str, sr2), - "not exist, will not use pretrained model", - ) - if not if_pretrained_discriminator_exist: - print( - "pretrained%s/f0D%s.pth" % (path_str, sr2), - "not exist, will not use pretrained model", - ) - if if_f0_3: - return ( - {"visible": True, "__type__": "update"}, - "pretrained%s/f0G%s.pth" % (path_str, sr2) - if if_pretrained_generator_exist - else "", - "pretrained%s/f0D%s.pth" % (path_str, sr2) - if if_pretrained_discriminator_exist - else "", - ) - 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 "", - ) - - -# 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 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" - % ( - 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 == 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 %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 "\b", - "-pd %s" % pretrained_D15 if pretrained_D15 != "" else "\b", - 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 not os.path.exists(feature_dir): - return "请先进行特征提取!" - listdir_res = list(os.listdir(feature_dir)) - if len(listdir_res) == 0: - return "请先进行特征提取!" - infos = [] - npys = [] - for name in sorted(listdir_res): - phone = np.load("%s/%s" % (feature_dir, name)) - npys.append(phone) - big_npy = np.concatenate(npys, 0) - big_npy_idx = np.arange(big_npy.shape[0]) - np.random.shuffle(big_npy_idx) - big_npy = big_npy[big_npy_idx] - if big_npy.shape[0] > 2e5: - # if(1): - infos.append("Trying doing kmeans %s shape to 10k centers." % big_npy.shape[0]) - yield "\n".join(infos) - try: - big_npy = ( - MiniBatchKMeans( - n_clusters=10000, - verbose=True, - batch_size=256 * config.n_cpu, - compute_labels=False, - init="random", - ) - .fit(big_npy) - .cluster_centers_ - ) - except: - info = traceback.format_exc() - print(info) - infos.append(info) - yield "\n".join(infos) - - np.save("%s/total_fea.npy" % exp_dir, big_npy) - n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39) - infos.append("%s,%s" % (big_npy.shape, n_ivf)) - yield "\n".join(infos) - index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf) - # index = faiss.index_factory(256if version19=="v1"else 768, "IVF%s,PQ128x4fs,RFlat"%n_ivf) - infos.append("training") - yield "\n".join(infos) - index_ivf = faiss.extract_index_ivf(index) # - index_ivf.nprobe = 1 - index.train(big_npy) - faiss.write_index( - index, - "%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index" - % (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19), - ) - # faiss.write_index(index, '%s/trained_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19)) - infos.append("adding") - yield "\n".join(infos) - batch_size_add = 8192 - for i in range(0, big_npy.shape[0], batch_size_add): - index.add(big_npy[i : i + batch_size_add]) - faiss.write_index( - index, - "%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index" - % (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19), - ) - infos.append( - "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 == 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 %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 == 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] - - if big_npy.shape[0] > 2e5: - # if(1): - info = "Trying doing kmeans %s shape to 10k centers." % big_npy.shape[0] - print(info) - yield get_info_str(info) - try: - big_npy = ( - MiniBatchKMeans( - n_clusters=10000, - verbose=True, - batch_size=256 * config.n_cpu, - compute_labels=False, - init="random", - ) - .fit(big_npy) - .cluster_centers_ - ) - except: - info = traceback.format_exc() - print(info) - yield get_info_str(info) - - np.save("%s/total_fea.npy" % model_log_dir, big_npy) - - # n_ivf = big_npy.shape[0] // 39 - n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39) - yield get_info_str("%s,%s" % (big_npy.shape, n_ivf)) - index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf) - yield get_info_str("training index") - index_ivf = faiss.extract_index_ivf(index) # - index_ivf.nprobe = 1 - index.train(big_npy) - faiss.write_index( - index, - "%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index" - % (model_log_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19), - ) - yield get_info_str("adding index") - batch_size_add = 8192 - for i in range(0, big_npy.shape[0], batch_size_add): - index.add(big_npy[i : i + batch_size_add]) - faiss.write_index( - index, - "%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index" - % (model_log_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19), - ) - yield get_info_str( - "成功构建索引, added_IVF%s_Flat_nprobe_%s_%s_%s.index" - % (n_ivf, index_ivf.nprobe, exp_dir1, version19) - ) - yield get_info_str(i18n("全流程结束!")) - - -# ckpt_path2.change(change_info_,[ckpt_path2],[sr__,if_f0__]) -def change_info_(ckpt_path): - if not os.path.exists(ckpt_path.replace(os.path.basename(ckpt_path), "train.log")): - return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"} - try: - with open( - ckpt_path.replace(os.path.basename(ckpt_path), "train.log"), "r" - ) as f: - info = eval(f.read().strip("\n").split("\n")[0].split("\t")[-1]) - sr, f0 = info["sample_rate"], info["if_f0"] - version = "v2" if ("version" in info and info["version"] == "v2") else "v1" - return sr, str(f0), version - except: - traceback.print_exc() - return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"} - - -def export_onnx(ModelPath, ExportedPath): - cpt = torch.load(ModelPath, map_location="cpu") - cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] - vec_channels = 256 if cpt.get("version", "v1") == "v1" else 768 - - test_phone = torch.rand(1, 200, vec_channels) # hidden unit - test_phone_lengths = torch.tensor([200]).long() # hidden unit 长度(貌似没啥用) - test_pitch = torch.randint(size=(1, 200), low=5, high=255) # 基频(单位赫兹) - test_pitchf = torch.rand(1, 200) # nsf基频 - test_ds = torch.LongTensor([0]) # 说话人ID - test_rnd = torch.rand(1, 192, 200) # 噪声(加入随机因子) - - device = "cpu" # 导出时设备(不影响使用模型) - - - net_g = SynthesizerTrnMsNSFsidM( - *cpt["config"], is_half=False, version=cpt.get("version", "v1") - ) # fp32导出(C++要支持fp16必须手动将内存重新排列所以暂时不用fp16) - net_g.load_state_dict(cpt["weight"], strict=False) - input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"] - output_names = [ - "audio", - ] - # net_g.construct_spkmixmap(n_speaker) 多角色混合轨道导出 - torch.onnx.export( - net_g, - ( - test_phone.to(device), - test_phone_lengths.to(device), - test_pitch.to(device), - test_pitchf.to(device), - test_ds.to(device), - test_rnd.to(device), - ), - ExportedPath, - dynamic_axes={ - "phone": [1], - "pitch": [1], - "pitchf": [1], - "rnd": [2], - }, - do_constant_folding=False, - opset_version=13, - verbose=False, - input_names=input_names, - output_names=output_names, - ) - return "Finished" - - -#region Mangio-RVC-Fork CLI App -import re as regex -import scipy.io.wavfile as wavfile - -cli_current_page = "HOME" - -def cli_split_command(com): - exp = r'(?:(?<=\s)|^)"(.*?)"(?=\s|$)|(\S+)' - split_array = regex.findall(exp, com) - split_array = [group[0] if group[0] else group[1] for group in split_array] - return split_array - -def execute_generator_function(genObject): - for _ in genObject: pass - -def cli_infer(com): - # get VC first - com = cli_split_command(com) - model_name = com[0] - source_audio_path = com[1] - output_file_name = com[2] - feature_index_path = com[3] - f0_file = None # Not Implemented Yet - - # Get parameters for inference - speaker_id = int(com[4]) - transposition = float(com[5]) - f0_method = com[6] - crepe_hop_length = int(com[7]) - harvest_median_filter = int(com[8]) - resample = int(com[9]) - mix = float(com[10]) - feature_ratio = float(com[11]) - protection_amnt = float(com[12]) - - print("Mangio-RVC-Fork Infer-CLI: Starting the inference...") - vc_data = get_vc(model_name) - print(vc_data) - print("Mangio-RVC-Fork Infer-CLI: Performing inference...") - conversion_data = vc_single( - speaker_id, - source_audio_path, - transposition, - f0_file, - f0_method, - feature_index_path, - feature_index_path, - feature_ratio, - harvest_median_filter, - resample, - mix, - protection_amnt, - crepe_hop_length, - ) - if "Success." in conversion_data[0]: - print("Mangio-RVC-Fork Infer-CLI: Inference succeeded. Writing to %s/%s..." % ('audio-outputs', output_file_name)) - wavfile.write('%s/%s' % ('audio-outputs', output_file_name), conversion_data[1][0], conversion_data[1][1]) - print("Mangio-RVC-Fork Infer-CLI: Finished! Saved output to %s/%s" % ('audio-outputs', output_file_name)) - else: - print("Mangio-RVC-Fork Infer-CLI: Inference failed. Here's the traceback: ") - print(conversion_data[0]) - -def cli_pre_process(com): - com = cli_split_command(com) - model_name = com[0] - trainset_directory = com[1] - sample_rate = com[2] - num_processes = int(com[3]) - - print("Mangio-RVC-Fork Pre-process: Starting...") - generator = preprocess_dataset( - trainset_directory, - model_name, - sample_rate, - num_processes - ) - execute_generator_function(generator) - print("Mangio-RVC-Fork Pre-process: Finished") - -def cli_extract_feature(com): - com = cli_split_command(com) - model_name = com[0] - gpus = com[1] - num_processes = int(com[2]) - has_pitch_guidance = True if (int(com[3]) == 1) else False - f0_method = com[4] - crepe_hop_length = int(com[5]) - version = com[6] # v1 or v2 - - print("Mangio-RVC-CLI: Extract Feature Has Pitch: " + str(has_pitch_guidance)) - print("Mangio-RVC-CLI: Extract Feature Version: " + str(version)) - print("Mangio-RVC-Fork Feature Extraction: Starting...") - generator = extract_f0_feature( - gpus, - num_processes, - f0_method, - has_pitch_guidance, - model_name, - version, - crepe_hop_length - ) - execute_generator_function(generator) - print("Mangio-RVC-Fork Feature Extraction: Finished") - -def cli_train(com): - com = cli_split_command(com) - model_name = com[0] - sample_rate = com[1] - has_pitch_guidance = True if (int(com[2]) == 1) else False - speaker_id = int(com[3]) - save_epoch_iteration = int(com[4]) - total_epoch = int(com[5]) # 10000 - batch_size = int(com[6]) - gpu_card_slot_numbers = com[7] - if_save_latest = i18n("是") if (int(com[8]) == 1) else i18n("否") - if_cache_gpu = i18n("是") if (int(com[9]) == 1) else i18n("否") - if_save_every_weight = i18n("是") if (int(com[10]) == 1) else i18n("否") - version = com[11] - - pretrained_base = "pretrained/" if version == "v1" else "pretrained_v2/" - - g_pretrained_path = "%sf0G%s.pth" % (pretrained_base, sample_rate) - d_pretrained_path = "%sf0D%s.pth" % (pretrained_base, sample_rate) - - print("Mangio-RVC-Fork Train-CLI: Training...") - click_train( - model_name, - sample_rate, - has_pitch_guidance, - speaker_id, - save_epoch_iteration, - total_epoch, - batch_size, - if_save_latest, - g_pretrained_path, - d_pretrained_path, - gpu_card_slot_numbers, - if_cache_gpu, - if_save_every_weight, - version - ) - -def cli_train_feature(com): - com = cli_split_command(com) - model_name = com[0] - version = com[1] - print("Mangio-RVC-Fork Train Feature Index-CLI: Training... Please wait") - generator = train_index( - model_name, - version - ) - execute_generator_function(generator) - print("Mangio-RVC-Fork Train Feature Index-CLI: Done!") - -def cli_extract_model(com): - com = cli_split_command(com) - model_path = com[0] - save_name = com[1] - sample_rate = com[2] - has_pitch_guidance = com[3] - info = com[4] - version = com[5] - extract_small_model_process = extract_small_model( - model_path, - save_name, - sample_rate, - has_pitch_guidance, - info, - version - ) - if extract_small_model_process == "Success.": - print("Mangio-RVC-Fork Extract Small Model: Success!") - else: - print(str(extract_small_model_process)) - print("Mangio-RVC-Fork Extract Small Model: Failed!") - -def print_page_details(): - if cli_current_page == "HOME": - print(" go home : Takes you back to home with a navigation list.") - print(" go infer : Takes you to inference command execution.\n") - print(" go pre-process : Takes you to training step.1) pre-process command execution.") - print(" go extract-feature : Takes you to training step.2) extract-feature command execution.") - print(" go train : Takes you to training step.3) being or continue training command execution.") - print(" go train-feature : Takes you to the train feature index command execution.\n") - print(" go extract-model : Takes you to the extract small model command execution.") - elif cli_current_page == "INFER": - print(" arg 1) model name with .pth in ./weights: mi-test.pth") - print(" arg 2) source audio path: myFolder\\MySource.wav") - print(" arg 3) output file name to be placed in './audio-outputs': MyTest.wav") - print(" arg 4) feature index file path: logs/mi-test/added_IVF3042_Flat_nprobe_1.index") - print(" arg 5) speaker id: 0") - print(" arg 6) transposition: 0") - print(" arg 7) f0 method: harvest (pm, harvest, crepe, crepe-tiny, hybrid[x,x,x,x], mangio-crepe, mangio-crepe-tiny)") - print(" arg 8) crepe hop length: 160") - print(" arg 9) harvest median filter radius: 3 (0-7)") - print(" arg 10) post resample rate: 0") - print(" arg 11) mix volume envelope: 1") - print(" arg 12) feature index ratio: 0.78 (0-1)") - print(" arg 13) Voiceless Consonant Protection (Less Artifact): 0.33 (Smaller number = more protection. 0.50 means Dont Use.) \n") - print("Example: mi-test.pth saudio/Sidney.wav myTest.wav logs/mi-test/added_index.index 0 -2 harvest 160 3 0 1 0.95 0.33") - elif cli_current_page == "PRE-PROCESS": - print(" arg 1) Model folder name in ./logs: mi-test") - print(" arg 2) Trainset directory: mydataset (or) E:\\my-data-set") - print(" arg 3) Sample rate: 40k (32k, 40k, 48k)") - print(" arg 4) Number of CPU threads to use: 8 \n") - print("Example: mi-test mydataset 40k 24") - elif cli_current_page == "EXTRACT-FEATURE": - print(" arg 1) Model folder name in ./logs: mi-test") - print(" arg 2) Gpu card slot: 0 (0-1-2 if using 3 GPUs)") - print(" arg 3) Number of CPU threads to use: 8") - print(" arg 4) Has Pitch Guidance?: 1 (0 for no, 1 for yes)") - print(" arg 5) f0 Method: harvest (pm, harvest, dio, crepe)") - print(" arg 6) Crepe hop length: 128") - print(" arg 7) Version for pre-trained models: v2 (use either v1 or v2)\n") - print("Example: mi-test 0 24 1 harvest 128 v2") - elif cli_current_page == "TRAIN": - print(" arg 1) Model folder name in ./logs: mi-test") - print(" arg 2) Sample rate: 40k (32k, 40k, 48k)") - print(" arg 3) Has Pitch Guidance?: 1 (0 for no, 1 for yes)") - print(" arg 4) speaker id: 0") - print(" arg 5) Save epoch iteration: 50") - print(" arg 6) Total epochs: 10000") - print(" arg 7) Batch size: 8") - print(" arg 8) Gpu card slot: 0 (0-1-2 if using 3 GPUs)") - print(" arg 9) Save only the latest checkpoint: 0 (0 for no, 1 for yes)") - print(" arg 10) Whether to cache training set to vram: 0 (0 for no, 1 for yes)") - print(" arg 11) Save extracted small model every generation?: 0 (0 for no, 1 for yes)") - print(" arg 12) Model architecture version: v2 (use either v1 or v2)\n") - print("Example: mi-test 40k 1 0 50 10000 8 0 0 0 0 v2") - elif cli_current_page == "TRAIN-FEATURE": - print(" arg 1) Model folder name in ./logs: mi-test") - print(" arg 2) Model architecture version: v2 (use either v1 or v2)\n") - print("Example: mi-test v2") - elif cli_current_page == "EXTRACT-MODEL": - print(" arg 1) Model Path: logs/mi-test/G_168000.pth") - print(" arg 2) Model save name: MyModel") - print(" arg 3) Sample rate: 40k (32k, 40k, 48k)") - print(" arg 4) Has Pitch Guidance?: 1 (0 for no, 1 for yes)") - print(' arg 5) Model information: "My Model"') - print(" arg 6) Model architecture version: v2 (use either v1 or v2)\n") - print('Example: logs/mi-test/G_168000.pth MyModel 40k 1 "Created by Cole Mangio" v2') - print("") - -def change_page(page): - global cli_current_page - cli_current_page = page - return 0 - -def execute_command(com): - if com == "go home": - return change_page("HOME") - elif com == "go infer": - return change_page("INFER") - elif com == "go pre-process": - return change_page("PRE-PROCESS") - elif com == "go extract-feature": - return change_page("EXTRACT-FEATURE") - elif com == "go train": - return change_page("TRAIN") - elif com == "go train-feature": - return change_page("TRAIN-FEATURE") - elif com == "go extract-model": - return change_page("EXTRACT-MODEL") - else: - if com[:3] == "go ": - print("page '%s' does not exist!" % com[3:]) - return 0 - - if cli_current_page == "INFER": - cli_infer(com) - elif cli_current_page == "PRE-PROCESS": - cli_pre_process(com) - elif cli_current_page == "EXTRACT-FEATURE": - cli_extract_feature(com) - elif cli_current_page == "TRAIN": - cli_train(com) - elif cli_current_page == "TRAIN-FEATURE": - cli_train_feature(com) - elif cli_current_page == "EXTRACT-MODEL": - cli_extract_model(com) - -def cli_navigation_loop(): - while True: - print("You are currently in '%s':" % cli_current_page) - print_page_details() - command = input("%s: " % cli_current_page) - try: - execute_command(command) - except: - print(traceback.format_exc()) - -if(config.is_cli): - print("\n\nMangio-RVC-Fork v2 CLI App!\n") - print("Welcome to the CLI version of RVC. Please read the documentation on https://github.com/Mangio621/Mangio-RVC-Fork (README.MD) to understand how to use this app.\n") - cli_navigation_loop() - -#endregion - -#region RVC WebUI App - -def get_presets(): - data = None - with open('../inference-presets.json', 'r') as file: - data = json.load(file) - preset_names = [] - for preset in data['presets']: - preset_names.append(preset['name']) - - return preset_names - -with gr.Blocks(theme=gr.themes.Soft()) as app: - gr.HTML("

The Mangio-RVC-Fork 💻

") - gr.Markdown( - value=i18n( - "本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.
如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录使用需遵守的协议-LICENSE.txt." - ) - ) - with gr.Tabs(): - with gr.TabItem(i18n("模型推理")): - # Inference Preset Row - # with gr.Row(): - # mangio_preset = gr.Dropdown(label="Inference Preset", choices=sorted(get_presets())) - # mangio_preset_name_save = gr.Textbox( - # label="Your preset name" - # ) - # mangio_preset_save_btn = gr.Button('Save Preset', variant="primary") - - # Other RVC stuff - with gr.Row(): - sid0 = gr.Dropdown(label=i18n("推理音色"), choices=sorted(names)) - 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]) - 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,crepe效果好但吃GPU" - ), - choices=["pm", "harvest", "dio", "crepe", "crepe-tiny", "mangio-crepe", "mangio-crepe-tiny", "rmvpe"], # Fork Feature. Add Crepe-Tiny - value="pm", - interactive=True, - ) - crepe_hop_length = gr.Slider( - minimum=1, - maximum=512, - step=1, - label=i18n("crepe_hop_length"), - value=120, - 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=0.75, - interactive=True, - ) - with gr.Column(): - resample_sr0 = gr.Slider( - minimum=0, - maximum=48000, - label=i18n("后处理重采样至最终采样率,0为不进行重采样"), - value=0, - step=1, - interactive=True, - ) - rms_mix_rate0 = gr.Slider( - minimum=0, - maximum=1, - label=i18n("输入源音量包络替换输出音量包络融合比例,越靠近1越使用输出包络"), - value=0.25, - interactive=True, - ) - protect0 = gr.Slider( - minimum=0, - maximum=0.5, - label=i18n( - "保护清辅音和呼吸声,防止电音撕裂等artifact,拉满0.5不开启,调低加大保护力度但可能降低索引效果" - ), - value=0.33, - step=0.01, - interactive=True, - ) - f0_file = gr.File(label=i18n("F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调")) - but0 = gr.Button(i18n("转换"), variant="primary") - with gr.Row(): - vc_output1 = gr.Textbox(label=i18n("输出信息")) - vc_output2 = gr.Audio(label=i18n("输出音频(右下角三个点,点了可以下载)")) - but0.click( - vc_single, - [ - spk_item, - input_audio0, - vc_transform0, - f0_file, - f0method0, - file_index1, - file_index2, - # file_big_npy1, - index_rate1, - filter_radius0, - resample_sr0, - rms_mix_rate0, - protect0, - crepe_hop_length - ], - [vc_output1, vc_output2], - ) - with gr.Group(): - gr.Markdown( - value=i18n("批量转换, 输入待转换音频文件夹, 或上传多个音频文件, 在指定文件夹(默认opt)下输出转换的音频. ") - ) - with gr.Row(): - with gr.Column(): - vc_transform1 = gr.Number( - label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0 - ) - opt_input = gr.Textbox(label=i18n("指定输出文件夹"), value="opt") - f0method1 = gr.Radio( - label=i18n( - "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU" - ), - choices=["pm", "harvest", "crepe", "rmvpe"], - value="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, - ) - 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], - ) - sid0.change( - fn=get_vc, - inputs=[sid0, protect0, protect1], - outputs=[spk_item, protect0, protect1], - ) - with gr.TabItem(i18n("伴奏人声分离&去混响&去回声")): - with gr.Group(): - gr.Markdown( - value=i18n( - "人声伴奏分离批量处理, 使用UVR5模型。
" - "合格的文件夹路径格式举例: E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(去文件管理器地址栏拷就行了)。
" - "模型分为三类:
" - "1、保留人声:不带和声的音频选这个,对主人声保留比HP5更好。内置HP2和HP3两个模型,HP3可能轻微漏伴奏但对主人声保留比HP2稍微好一丁点;
" - "2、仅保留主人声:带和声的音频选这个,对主人声可能有削弱。内置HP5一个模型;
" - "3、去混响、去延迟模型(by FoxJoy):
" - "  (1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择,不能去除单通道混响;
" - " (234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底,DeReverb额外去除混响,可去除单声道混响,但是对高频重的板式混响去不干净。
" - "去混响/去延迟,附:
" - "1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍;
" - "2、MDX-Net-Dereverb模型挺慢的;
" - "3、个人推荐的最干净的配置是先MDX-Net再DeEcho-Aggressive。" - ) - ) - with gr.Row(): - with gr.Column(): - dir_wav_input = gr.Textbox( - label=i18n("输入待处理音频文件夹路径"), - value="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("指定输出主人声文件夹"), value="opt" - ) - opt_ins_root = gr.Textbox( - label=i18n("指定输出非主人声文件夹"), value="opt" - ) - format0 = gr.Radio( - label=i18n("导出文件格式"), - choices=["wav", "flac", "mp3", "m4a"], - value="flac", - interactive=True, - ) - but2 = gr.Button(i18n("转换"), variant="primary") - vc_output4 = gr.Textbox(label=i18n("输出信息")) - but2.click( - uvr, - [ - model_choose, - dir_wav_input, - opt_vocal_root, - wav_inputs, - opt_ins_root, - agg, - format0, - ], - [vc_output4], - ) - with gr.TabItem(i18n("训练")): - gr.Markdown( - value=i18n( - "step1: 填写实验配置. 实验数据放在logs下, 每个实验一个文件夹, 需手工输入实验名路径, 内含实验配置, 日志, 训练得到的模型文件. " - ) - ) - with gr.Row(): - exp_dir1 = gr.Textbox(label=i18n("输入实验名"), value="mi-test") - sr2 = gr.Radio( - label=i18n("目标采样率"), - choices=["40k", "48k"], - value="40k", - interactive=True, - ) - if_f0_3 = gr.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"), - choices=["v1", "v2"], - value="v1", - interactive=True, - visible=True, - ) - np7 = gr.Slider( - minimum=0, - maximum=config.n_cpu, - step=1, - label=i18n("提取音高和处理数据使用的CPU进程数"), - value=int(np.ceil(config.n_cpu / 1.5)), - interactive=True, - ) - with gr.Group(): # 暂时单人的, 后面支持最多4人的#数据处理 - gr.Markdown( - value=i18n( - "step2a: 自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练. " - ) - ) - with gr.Row(): - trainset_dir4 = gr.Textbox( - label=i18n("输入训练文件夹路径"), value="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", "crepe", "mangio-crepe"], # Fork feature: Crepe on f0 extraction for training. - value="harvest", - interactive=True, - ) - extraction_crepe_hop_length = gr.Slider( - minimum=1, - maximum=512, - step=1, - label=i18n("crepe_hop_length"), - value=64, - 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, extraction_crepe_hop_length], - [info2], - ) - with gr.Group(): - gr.Markdown(value=i18n("step3: 填写训练设置, 开始训练模型和索引")) - with gr.Row(): - save_epoch10 = gr.Slider( - minimum=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=10000, - step=1, - label=i18n("总训练轮数total_epoch"), - value=20, - interactive=True, - ) - batch_size12 = gr.Slider( - minimum=1, - maximum=40, - step=1, - label=i18n("每张显卡的batch_size"), - value=default_batch_size, - interactive=True, - ) - if_save_latest13 = gr.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.change( - change_version19, - [sr2, if_f0_3, version19], - [pretrained_G14, pretrained_D15, sr2], - ) - 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, - extraction_crepe_hop_length - ], - info3, - ) - - with gr.TabItem(i18n("ckpt处理")): - with gr.Group(): - gr.Markdown(value=i18n("模型融合, 可用于测试音色融合")) - with gr.Row(): - ckpt_a = gr.Textbox(label=i18n("A模型路径"), value="", interactive=True) - 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=["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="v2", - 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(): - infoOnnx = gr.Label(label="info") - with gr.Row(): - butOnnx = gr.Button(i18n("导出Onnx模型"), variant="primary") - butOnnx.click(export_onnx, [ckpt_dir, onnx_dir], infoOnnx) - - tab_faq = i18n("常见问题解答") - with gr.TabItem(tab_faq): - try: - if tab_faq == "常见问题解答": - with open("docs/faq.md", "r", encoding="utf8") as f: - info = f.read() - else: - with open("docs/faq_en.md", "r", encoding="utf8") as f: - info = f.read() - gr.Markdown(value=info) - except: - gr.Markdown(traceback.format_exc()) - - - #region Mangio Preset Handler Region - def save_preset( - preset_name, - sid0, - vc_transform, - input_audio, - f0method, - crepe_hop_length, - filter_radius, - file_index1, - file_index2, - index_rate, - resample_sr, - rms_mix_rate, - protect, - f0_file - ): - data = None - with open('../inference-presets.json', 'r') as file: - data = json.load(file) - preset_json = { - 'name': preset_name, - 'model': sid0, - 'transpose': vc_transform, - 'audio_file': input_audio, - 'f0_method': f0method, - 'crepe_hop_length': crepe_hop_length, - 'median_filtering': filter_radius, - 'feature_path': file_index1, - 'auto_feature_path': file_index2, - 'search_feature_ratio': index_rate, - 'resample': resample_sr, - 'volume_envelope': rms_mix_rate, - 'protect_voiceless': protect, - 'f0_file_path': f0_file - } - data['presets'].append(preset_json) - with open('../inference-presets.json', 'w') as file: - json.dump(data, file) - file.flush() - print("Saved Preset %s into inference-presets.json!" % preset_name) - - - def on_preset_changed(preset_name): - print("Changed Preset to %s!" % preset_name) - data = None - with open('../inference-presets.json', 'r') as file: - data = json.load(file) - - print("Searching for " + preset_name) - returning_preset = None - for preset in data['presets']: - if(preset['name'] == preset_name): - print("Found a preset") - returning_preset = preset - # return all new input values - return ( - # returning_preset['model'], - # returning_preset['transpose'], - # returning_preset['audio_file'], - # returning_preset['f0_method'], - # returning_preset['crepe_hop_length'], - # returning_preset['median_filtering'], - # returning_preset['feature_path'], - # returning_preset['auto_feature_path'], - # returning_preset['search_feature_ratio'], - # returning_preset['resample'], - # returning_preset['volume_envelope'], - # returning_preset['protect_voiceless'], - # returning_preset['f0_file_path'] - ) - - # Preset State Changes - - # This click calls save_preset that saves the preset into inference-presets.json with the preset name - # mangio_preset_save_btn.click( - # fn=save_preset, - # inputs=[ - # mangio_preset_name_save, - # sid0, - # vc_transform0, - # input_audio0, - # f0method0, - # crepe_hop_length, - # filter_radius0, - # file_index1, - # file_index2, - # index_rate1, - # resample_sr0, - # rms_mix_rate0, - # protect0, - # f0_file - # ], - # outputs=[] - # ) - - # mangio_preset.change( - # on_preset_changed, - # inputs=[ - # # Pass inputs here - # mangio_preset - # ], - # outputs=[ - # # Pass Outputs here. These refer to the gradio elements that we want to directly change - # # sid0, - # # vc_transform0, - # # input_audio0, - # # f0method0, - # # crepe_hop_length, - # # filter_radius0, - # # file_index1, - # # file_index2, - # # index_rate1, - # # resample_sr0, - # # rms_mix_rate0, - # # protect0, - # # f0_file - # ] - # ) - #endregion - - # with gr.TabItem(i18n("招募音高曲线前端编辑器")): - # gr.Markdown(value=i18n("加开发群联系我xxxxx")) - # with gr.TabItem(i18n("点击查看交流、问题反馈群号")): - # gr.Markdown(value=i18n("xxxxx")) - - if config.iscolab or config.paperspace: # Share gradio link for colab and paperspace (FORK FEATURE) - app.queue(concurrency_count=511, max_size=1022).launch(share=True) - else: - app.queue(concurrency_count=511, max_size=1022).launch( - server_name="0.0.0.0", - inbrowser=not config.noautoopen, - server_port=config.listen_port, - quiet=True, - ) - -#endregion +import os +import shutil +import sys +import json # Mangio fork using json for preset saving + +now_dir = os.getcwd() +sys.path.append(now_dir) +import traceback, pdb +import warnings + +import numpy as np +import torch +import re +os.environ["OPENBLAS_NUM_THREADS"] = "1" +os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1" +import logging +import threading +from random import shuffle +from subprocess import Popen +from time import sleep + +import faiss +import ffmpeg +import gradio as gr +import soundfile as sf +from config import Config +from fairseq import checkpoint_utils +from i18n import I18nAuto +from lib.infer_pack.models import ( + SynthesizerTrnMs256NSFsid, + SynthesizerTrnMs256NSFsid_nono, + SynthesizerTrnMs768NSFsid, + SynthesizerTrnMs768NSFsid_nono, +) +from lib.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM +from infer_uvr5 import _audio_pre_, _audio_pre_new +from MDXNet import MDXNetDereverb +from my_utils import load_audio +from train.process_ckpt import change_info, extract_small_model, merge, show_info +from vc_infer_pipeline import VC +from sklearn.cluster import MiniBatchKMeans + +logging.getLogger("numba").setLevel(logging.WARNING) + + +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) + +DoFormant = False +Quefrency = 8.0 +Timbre = 1.2 + +with open('formanting.txt', 'w+') as fsf: + fsf.truncate(0) + + fsf.writelines([str(DoFormant) + '\n', str(Quefrency) + '\n', str(Timbre) + '\n']) + + +config = Config() +i18n = I18nAuto() +i18n.print() +# 判断是否有能用来训练和加速推理的N卡 +ngpu = torch.cuda.device_count() +gpu_infos = [] +mem = [] +if_gpu_ok = False + +if torch.cuda.is_available() or ngpu != 0: + for i in range(ngpu): + gpu_name = torch.cuda.get_device_name(i) + if any( + value in gpu_name.upper() + for value in [ + "10", + "16", + "20", + "30", + "40", + "A2", + "A3", + "A4", + "P4", + "A50", + "500", + "A60", + "70", + "80", + "90", + "M4", + "T4", + "TITAN", + ] + ): + # A10#A100#V100#A40#P40#M40#K80#A4500 + if_gpu_ok = True # 至少有一张能用的N卡 + gpu_infos.append("%s\t%s" % (i, gpu_name)) + mem.append( + int( + torch.cuda.get_device_properties(i).total_memory + / 1024 + / 1024 + / 1024 + + 0.4 + ) + ) +if if_gpu_ok and len(gpu_infos) > 0: + gpu_info = "\n".join(gpu_infos) + default_batch_size = min(mem) // 2 +else: + gpu_info = i18n("很遗憾您这没有能用的显卡来支持您训练") + default_batch_size = 1 +gpus = "-".join([i[0] for i in gpu_infos]) + + +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/" +global audio_root +audio_root = "audios" +global input_audio_path0 +global input_audio_path1 +names = [] +for name in os.listdir(weight_root): + if name.endswith(".pth"): + names.append(name) +index_paths = [] + +global indexes_list +indexes_list=[] + +audio_paths = [] +for root, dirs, files in os.walk(index_root, topdown=False): + for name in files: + if name.endswith(".index") and "trained" not in name: + index_paths.append("%s\\%s" % (root, name)) + +for root, dirs, files in os.walk(audio_root, topdown=False): + for name in files: + + audio_paths.append("%s/%s" % (root, name)) + +uvr5_names = [] +for name in os.listdir(weight_uvr5_root): + if name.endswith(".pth") or "onnx" in name: + uvr5_names.append(name.replace(".pth", "")) + +def check_for_name(): + if len(names) > 0: + return sorted(names)[0] + else: + return '' + +def get_index(): + if check_for_name() != '': + chosen_model=sorted(names)[0].split(".")[0] + logs_path="./logs/"+chosen_model + if os.path.exists(logs_path): + for file in os.listdir(logs_path): + if file.endswith(".index"): + return os.path.join(logs_path, file).replace('\\','/') + return '' + else: + return '' + +def get_indexes(): + for dirpath, dirnames, filenames in os.walk("./logs/"): + for filename in filenames: + if filename.endswith(".index") and "trained" not in filename: + indexes_list.append(os.path.join(dirpath,filename).replace('\\','/')) + if len(indexes_list) > 0: + return indexes_list + else: + return '' + +fshift_presets_list = [] + +def get_fshift_presets(): + fshift_presets_list = [] + for dirpath, dirnames, filenames in os.walk("./formantshiftcfg/"): + for filename in filenames: + if filename.endswith(".txt"): + fshift_presets_list.append(os.path.join(dirpath,filename).replace('\\','/')) + + if len(fshift_presets_list) > 0: + return fshift_presets_list + else: + return '' + + +def get_audios(): + if check_for_name() != '': + audios_path= '"' + os.path.abspath(os.getcwd()) + '/audios/' + if os.path.exists(audios_path): + for file in os.listdir(audios_path): + print(audios_path.join(file) + '"') + return os.path.join(audios_path, file + '"') + return '' + else: + return '' + + +def vc_single( + sid, + input_audio_path0, + input_audio_path1, + f0_up_key, + f0_file, + f0_method, + file_index, + file_index2, + # file_big_npy, + index_rate, + filter_radius, + resample_sr, + rms_mix_rate, + protect, + crepe_hop_length, +): # spk_item, input_audio0, vc_transform0,f0_file,f0method0 + global tgt_sr, net_g, vc, hubert_model, version + if input_audio_path0 is None or input_audio_path0 is None: + return "You need to upload an audio", None + f0_up_key = int(f0_up_key) + try: + if input_audio_path0 == '': + audio = load_audio(input_audio_path1, 16000, DoFormant, Quefrency, Timbre) + + else: + audio = load_audio(input_audio_path0, 16000, DoFormant, Quefrency, Timbre) + + audio_max = np.abs(audio).max() / 0.95 + if audio_max > 1: + audio /= audio_max + times = [0, 0, 0] + if not hubert_model: + load_hubert() + if_f0 = cpt.get("f0", 1) + file_index = ( + ( + file_index.strip(" ") + .strip('"') + .strip("\n") + .strip('"') + .strip(" ") + .replace("trained", "added") + ) + if file_index != "" + else file_index2 + ) # 防止小白写错,自动帮他替换掉 + # file_big_npy = ( + # file_big_npy.strip(" ").strip('"').strip("\n").strip('"').strip(" ") + # ) + audio_opt = vc.pipeline( + hubert_model, + net_g, + sid, + audio, + input_audio_path1, + times, + f0_up_key, + f0_method, + file_index, + # file_big_npy, + index_rate, + if_f0, + filter_radius, + tgt_sr, + resample_sr, + rms_mix_rate, + version, + protect, + crepe_hop_length, + f0_file=f0_file, + ) + if tgt_sr != resample_sr >= 16000: + tgt_sr = resample_sr + index_info = ( + "Using index:%s." % file_index + if os.path.exists(file_index) + else "Index not used." + ) + return "Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss" % ( + index_info, + times[0], + times[1], + times[2], + ), (tgt_sr, audio_opt) + except: + info = traceback.format_exc() + print(info) + return info, (None, None) + + +def vc_multi( + sid, + dir_path, + opt_root, + paths, + f0_up_key, + f0_method, + file_index, + file_index2, + # file_big_npy, + index_rate, + filter_radius, + resample_sr, + rms_mix_rate, + protect, + format1, + crepe_hop_length, +): + try: + dir_path = ( + dir_path.strip(" ").strip('"').strip("\n").strip('"').strip(" ") + ) # 防止小白拷路径头尾带了空格和"和回车 + opt_root = opt_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ") + os.makedirs(opt_root, exist_ok=True) + try: + if dir_path != "": + paths = [os.path.join(dir_path, name) for name in os.listdir(dir_path)] + else: + paths = [path.name for path in paths] + except: + traceback.print_exc() + paths = [path.name for path in paths] + infos = [] + for path in paths: + info, opt = vc_single( + sid, + path, + f0_up_key, + None, + f0_method, + file_index, + file_index2, + # file_big_npy, + index_rate, + filter_radius, + resample_sr, + rms_mix_rate, + protect, + crepe_hop_length + ) + if "Success" in info: + try: + tgt_sr, audio_opt = opt + if format1 in ["wav", "flac", "mp3", "ogg", "aac"]: + sf.write( + "%s/%s.%s" % (opt_root, os.path.basename(path), format1), + audio_opt, + tgt_sr, + ) + else: + path = "%s/%s.wav" % (opt_root, os.path.basename(path)) + sf.write( + path, + audio_opt, + tgt_sr, + ) + if os.path.exists(path): + os.system( + "ffmpeg -i %s -vn %s -q:a 2 -y" + % (path, path[:-4] + ".%s" % format1) + ) + except: + info += traceback.format_exc() + infos.append("%s->%s" % (os.path.basename(path), info)) + yield "\n".join(infos) + yield "\n".join(infos) + except: + yield traceback.format_exc() + + +def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format0): + infos = [] + try: + inp_root = inp_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ") + save_root_vocal = ( + save_root_vocal.strip(" ").strip('"').strip("\n").strip('"').strip(" ") + ) + save_root_ins = ( + save_root_ins.strip(" ").strip('"').strip("\n").strip('"').strip(" ") + ) + if model_name == "onnx_dereverb_By_FoxJoy": + pre_fun = MDXNetDereverb(15) + else: + func = _audio_pre_ if "DeEcho" not in model_name else _audio_pre_new + pre_fun = func( + agg=int(agg), + model_path=os.path.join(weight_uvr5_root, model_name + ".pth"), + device=config.device, + is_half=config.is_half, + ) + if inp_root != "": + paths = [os.path.join(inp_root, name) for name in os.listdir(inp_root)] + else: + paths = [path.name for path in paths] + for path in paths: + inp_path = os.path.join(inp_root, path) + need_reformat = 1 + done = 0 + try: + info = ffmpeg.probe(inp_path, cmd="ffprobe") + if ( + info["streams"][0]["channels"] == 2 + and info["streams"][0]["sample_rate"] == "44100" + ): + need_reformat = 0 + pre_fun._path_audio_( + inp_path, save_root_ins, save_root_vocal, format0 + ) + done = 1 + except: + need_reformat = 1 + traceback.print_exc() + if need_reformat == 1: + tmp_path = "%s/%s.reformatted.wav" % (tmp, os.path.basename(inp_path)) + os.system( + "ffmpeg -i %s -vn -acodec pcm_s16le -ac 2 -ar 44100 %s -y" + % (inp_path, tmp_path) + ) + inp_path = tmp_path + try: + if done == 0: + pre_fun._path_audio_( + inp_path, save_root_ins, save_root_vocal, format0 + ) + infos.append("%s->Success" % (os.path.basename(inp_path))) + yield "\n".join(infos) + except: + infos.append( + "%s->%s" % (os.path.basename(inp_path), traceback.format_exc()) + ) + yield "\n".join(infos) + except: + infos.append(traceback.format_exc()) + yield "\n".join(infos) + finally: + try: + if model_name == "onnx_dereverb_By_FoxJoy": + del pre_fun.pred.model + del pre_fun.pred.model_ + else: + del pre_fun.model + del pre_fun + except: + traceback.print_exc() + print("clean_empty_cache") + if torch.cuda.is_available(): + torch.cuda.empty_cache() + yield "\n".join(infos) + + +# 一个选项卡全局只能有一个音色 +def get_vc(sid, to_return_protect0, to_return_protect1): + global n_spk, tgt_sr, net_g, vc, cpt, version + if sid == "" or sid == []: + global hubert_model + if hubert_model is not None: # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的 + print("clean_empty_cache") + del net_g, n_spk, vc, hubert_model, tgt_sr # ,cpt + hubert_model = net_g = n_spk = vc = hubert_model = tgt_sr = None + if torch.cuda.is_available(): + torch.cuda.empty_cache() + ###楼下不这么折腾清理不干净 + if_f0 = cpt.get("f0", 1) + version = cpt.get("version", "v1") + if version == "v1": + if if_f0 == 1: + net_g = SynthesizerTrnMs256NSFsid( + *cpt["config"], is_half=config.is_half + ) + else: + net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) + elif version == "v2": + if if_f0 == 1: + net_g = SynthesizerTrnMs768NSFsid( + *cpt["config"], is_half=config.is_half + ) + else: + net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"]) + del net_g, cpt + if torch.cuda.is_available(): + torch.cuda.empty_cache() + cpt = None + return {"visible": False, "__type__": "update"} + person = "%s/%s" % (weight_root, sid) + print("loading %s" % person) + cpt = torch.load(person, map_location="cpu") + tgt_sr = cpt["config"][-1] + cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk + if_f0 = cpt.get("f0", 1) + if if_f0 == 0: + to_return_protect0 = to_return_protect1 = { + "visible": False, + "value": 0.5, + "__type__": "update", + } + else: + to_return_protect0 = { + "visible": True, + "value": to_return_protect0, + "__type__": "update", + } + to_return_protect1 = { + "visible": True, + "value": to_return_protect1, + "__type__": "update", + } + version = cpt.get("version", "v1") + if version == "v1": + if if_f0 == 1: + net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half) + else: + net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) + elif version == "v2": + if if_f0 == 1: + net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half) + else: + net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"]) + del net_g.enc_q + print(net_g.load_state_dict(cpt["weight"], strict=False)) + net_g.eval().to(config.device) + if config.is_half: + net_g = net_g.half() + else: + net_g = net_g.float() + vc = VC(tgt_sr, config) + n_spk = cpt["config"][-3] + return ( + {"visible": True, "maximum": n_spk, "__type__": "update"}, + to_return_protect0, + to_return_protect1, + ) + + +def change_choices(): + names = [] + for name in os.listdir(weight_root): + if name.endswith(".pth"): + names.append(name) + index_paths = [] + audio_paths = [] + audios_path=os.path.abspath(os.getcwd()) + "/audios/" + for root, dirs, files in os.walk(index_root, topdown=False): + for name in files: + if name.endswith(".index") and "trained" not in name: + index_paths.append("%s/%s" % (root, name)) + for file in os.listdir(audios_path): + audio_paths.append("%s/%s" % (audio_root, file)) + return {"choices": sorted(names), "__type__": "update"}, {"choices": sorted(index_paths), "__type__": "update"}, {"choices": sorted(audio_paths), "__type__": "update"} + + +def clean(): + return {"value": "", "__type__": "update"} + + +sr_dict = { + "32k": 32000, + "40k": 40000, + "48k": 48000, +} + + +def if_done(done, p): + while 1: + if p.poll() is None: + sleep(0.5) + else: + break + done[0] = True + + +def if_done_multi(done, ps): + while 1: + # poll==None代表进程未结束 + # 只要有一个进程未结束都不停 + flag = 1 + for p in ps: + if p.poll() is None: + flag = 0 + sleep(0.5) + break + if flag == 1: + break + done[0] = True + +def formant_enabled(cbox, qfrency, tmbre, frmntapply, formantpreset, formant_refresh_button): + + if (cbox): + + DoFormant = True + with open('formanting.txt', 'w') as fxxf: + fxxf.truncate(0) + + fxxf.writelines([str(DoFormant) + '\n', str(Quefrency) + '\n', str(Timbre) + '\n']) + #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 + with open('formanting.txt', 'w') as fxf: + fxf.truncate(0) + + fxf.writelines([str(DoFormant) + '\n', str(Quefrency) + '\n', str(Timbre) + '\n']) + #print(f"is checked? - {cbox}\ngot {DoFormant}") + return ( + {"value": False, "__type__": "update"}, + {"visible": False, "__type__": "update"}, + {"visible": False, "__type__": "update"}, + {"visible": False, "__type__": "update"}, + {"visible": False, "__type__": "update"}, + {"visible": False, "__type__": "update"}, + {"visible": False, "__type__": "update"}, + ) + + +def formant_apply(qfrency, tmbre): + Quefrency = qfrency + Timbre = tmbre + DoFormant = True + + with open('formanting.txt', 'w') as fxxxf: + fxxxf.truncate(0) + + fxxxf.writelines([str(DoFormant) + '\n', str(Quefrency) + '\n', str(Timbre) + '\n']) + return ({"value": Quefrency, "__type__": "update"}, {"value": Timbre, "__type__": "update"}) + +def update_fshift_presets(preset, qfrency, tmbre): + + qfrency, tmbre = preset_apply(preset, qfrency, tmbre) + + if (str(preset) != ''): + with open(str(preset), 'r') as p: + content = p.readlines() + qfrency, tmbre = content[0].split('\n')[0], content[1] + + formant_apply(qfrency, tmbre) + else: + pass + return ( + {"choices": get_fshift_presets(), "__type__": "update"}, + {"value": qfrency, "__type__": "update"}, + {"value": tmbre, "__type__": "update"}, + ) + + +def preprocess_dataset(trainset_dir, exp_dir, sr, n_p): + sr = sr_dict[sr] + os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True) + f = open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "w") + f.close() + cmd = ( + config.python_cmd + + " trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s " + % (trainset_dir, sr, n_p, now_dir, exp_dir) + + str(config.noparallel) + ) + print(cmd) + p = Popen(cmd, shell=True) # , stdin=PIPE, stdout=PIPE,stderr=PIPE,cwd=now_dir + ###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读 + done = [False] + threading.Thread( + target=if_done, + args=( + done, + p, + ), + ).start() + while 1: + with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f: + yield (f.read()) + sleep(1) + if done[0]: + break + with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f: + log = f.read() + print(log) + yield log + + +# but2.click(extract_f0,[gpus6,np7,f0method8,if_f0_3,trainset_dir4],[info2]) +def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, echl): + gpus = gpus.split("-") + os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True) + f = open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "w") + f.close() + if if_f0: + cmd = config.python_cmd + " extract_f0_print.py %s/logs/%s %s %s %s" % ( + now_dir, + exp_dir, + n_p, + f0method, + echl, + ) + print(cmd) + p = Popen(cmd, shell=True, cwd=now_dir) # , stdin=PIPE, stdout=PIPE,stderr=PIPE + ###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读 + done = [False] + threading.Thread( + target=if_done, + args=( + done, + p, + ), + ).start() + while 1: + with open( + "%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r" + ) as f: + yield (f.read()) + sleep(1) + if done[0]: + break + with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f: + log = f.read() + print(log) + yield log + ####对不同part分别开多进程 + """ + n_part=int(sys.argv[1]) + i_part=int(sys.argv[2]) + i_gpu=sys.argv[3] + exp_dir=sys.argv[4] + os.environ["CUDA_VISIBLE_DEVICES"]=str(i_gpu) + """ + leng = len(gpus) + ps = [] + for idx, n_g in enumerate(gpus): + cmd = ( + config.python_cmd + + " extract_feature_print.py %s %s %s %s %s/logs/%s %s" + % ( + config.device, + leng, + idx, + n_g, + now_dir, + exp_dir, + version19, + ) + ) + print(cmd) + p = Popen( + cmd, shell=True, cwd=now_dir + ) # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir + ps.append(p) + ###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读 + done = [False] + threading.Thread( + target=if_done_multi, + args=( + done, + ps, + ), + ).start() + while 1: + with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f: + yield (f.read()) + sleep(1) + if done[0]: + break + with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f: + log = f.read() + print(log) + yield log + + +def change_sr2(sr2, if_f0_3, version19): + path_str = "" if version19 == "v1" else "_v2" + f0_str = "f0" if if_f0_3 else "" + if_pretrained_generator_exist = os.access( + "pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK + ) + if_pretrained_discriminator_exist = os.access( + "pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK + ) + if not if_pretrained_generator_exist: + print( + "pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), + "not exist, will not use pretrained model", + ) + if not if_pretrained_discriminator_exist: + 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_version19(sr2, if_f0_3, version19): + path_str = "" if version19 == "v1" else "_v2" + if sr2 == "32k" and version19 == "v1": + sr2 = "40k" + to_return_sr2 = ( + {"choices": ["40k", "48k"], "__type__": "update", "value": sr2} + if version19 == "v1" + else {"choices": ["40k", "48k", "32k"], "__type__": "update", "value": sr2} + ) + f0_str = "f0" if if_f0_3 else "" + if_pretrained_generator_exist = os.access( + "pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK + ) + if_pretrained_discriminator_exist = os.access( + "pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK + ) + if not if_pretrained_generator_exist: + print( + "pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), + "not exist, will not use pretrained model", + ) + if not if_pretrained_discriminator_exist: + 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 "", + to_return_sr2, + ) + + +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 not if_pretrained_generator_exist: + print( + "pretrained%s/f0G%s.pth" % (path_str, sr2), + "not exist, will not use pretrained model", + ) + if not if_pretrained_discriminator_exist: + print( + "pretrained%s/f0D%s.pth" % (path_str, sr2), + "not exist, will not use pretrained model", + ) + if if_f0_3: + return ( + {"visible": True, "__type__": "update"}, + "pretrained%s/f0G%s.pth" % (path_str, sr2) + if if_pretrained_generator_exist + else "", + "pretrained%s/f0D%s.pth" % (path_str, sr2) + if if_pretrained_discriminator_exist + else "", + ) + 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 "", + ) + + +# 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 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" + % ( + 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 == 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 %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 "\b", + "-pd %s" % pretrained_D15 if pretrained_D15 != "" else "\b", + 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 not os.path.exists(feature_dir): + return "请先进行特征提取!" + listdir_res = list(os.listdir(feature_dir)) + if len(listdir_res) == 0: + return "请先进行特征提取!" + infos = [] + npys = [] + for name in sorted(listdir_res): + phone = np.load("%s/%s" % (feature_dir, name)) + npys.append(phone) + big_npy = np.concatenate(npys, 0) + big_npy_idx = np.arange(big_npy.shape[0]) + np.random.shuffle(big_npy_idx) + big_npy = big_npy[big_npy_idx] + if big_npy.shape[0] > 2e5: + # if(1): + infos.append("Trying doing kmeans %s shape to 10k centers." % big_npy.shape[0]) + yield "\n".join(infos) + try: + big_npy = ( + MiniBatchKMeans( + n_clusters=10000, + verbose=True, + batch_size=256 * config.n_cpu, + compute_labels=False, + init="random", + ) + .fit(big_npy) + .cluster_centers_ + ) + except: + info = traceback.format_exc() + print(info) + infos.append(info) + yield "\n".join(infos) + + np.save("%s/total_fea.npy" % exp_dir, big_npy) + n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39) + infos.append("%s,%s" % (big_npy.shape, n_ivf)) + yield "\n".join(infos) + index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf) + # index = faiss.index_factory(256if version19=="v1"else 768, "IVF%s,PQ128x4fs,RFlat"%n_ivf) + infos.append("training") + yield "\n".join(infos) + index_ivf = faiss.extract_index_ivf(index) # + index_ivf.nprobe = 1 + index.train(big_npy) + faiss.write_index( + index, + "%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index" + % (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19), + ) + # faiss.write_index(index, '%s/trained_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19)) + infos.append("adding") + yield "\n".join(infos) + batch_size_add = 8192 + for i in range(0, big_npy.shape[0], batch_size_add): + index.add(big_npy[i : i + batch_size_add]) + faiss.write_index( + index, + "%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index" + % (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19), + ) + infos.append( + "Successful Index Construction,added_IVF%s_Flat_nprobe_%s_%s_%s.index" + % (n_ivf, index_ivf.nprobe, exp_dir1, version19) + ) + # faiss.write_index(index, '%s/added_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19)) + # infos.append("成功构建索引,added_IVF%s_Flat_FastScan_%s.index"%(n_ivf,version19)) + yield "\n".join(infos) + + +# 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 == 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 %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 == 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] + + if big_npy.shape[0] > 2e5: + # if(1): + info = "Trying doing kmeans %s shape to 10k centers." % big_npy.shape[0] + print(info) + yield get_info_str(info) + try: + big_npy = ( + MiniBatchKMeans( + n_clusters=10000, + verbose=True, + batch_size=256 * config.n_cpu, + compute_labels=False, + init="random", + ) + .fit(big_npy) + .cluster_centers_ + ) + except: + info = traceback.format_exc() + print(info) + yield get_info_str(info) + + np.save("%s/total_fea.npy" % model_log_dir, big_npy) + + # n_ivf = big_npy.shape[0] // 39 + n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39) + yield get_info_str("%s,%s" % (big_npy.shape, n_ivf)) + index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf) + yield get_info_str("training index") + index_ivf = faiss.extract_index_ivf(index) # + index_ivf.nprobe = 1 + index.train(big_npy) + faiss.write_index( + index, + "%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index" + % (model_log_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19), + ) + yield get_info_str("adding index") + batch_size_add = 8192 + for i in range(0, big_npy.shape[0], batch_size_add): + index.add(big_npy[i : i + batch_size_add]) + faiss.write_index( + index, + "%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index" + % (model_log_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19), + ) + yield get_info_str( + "成功构建索引, added_IVF%s_Flat_nprobe_%s_%s_%s.index" + % (n_ivf, index_ivf.nprobe, exp_dir1, version19) + ) + yield get_info_str(i18n("全流程结束!")) + + +# ckpt_path2.change(change_info_,[ckpt_path2],[sr__,if_f0__]) +def change_info_(ckpt_path): + if not os.path.exists(ckpt_path.replace(os.path.basename(ckpt_path), "train.log")): + return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"} + try: + with open( + ckpt_path.replace(os.path.basename(ckpt_path), "train.log"), "r" + ) as f: + info = eval(f.read().strip("\n").split("\n")[0].split("\t")[-1]) + sr, f0 = info["sample_rate"], info["if_f0"] + version = "v2" if ("version" in info and info["version"] == "v2") else "v1" + return sr, str(f0), version + except: + traceback.print_exc() + return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"} + + +def export_onnx(ModelPath, ExportedPath): + cpt = torch.load(ModelPath, map_location="cpu") + cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] + vec_channels = 256 if cpt.get("version", "v1") == "v1" else 768 + + test_phone = torch.rand(1, 200, vec_channels) # hidden unit + test_phone_lengths = torch.tensor([200]).long() # hidden unit 长度(貌似没啥用) + test_pitch = torch.randint(size=(1, 200), low=5, high=255) # 基频(单位赫兹) + test_pitchf = torch.rand(1, 200) # nsf基频 + test_ds = torch.LongTensor([0]) # 说话人ID + test_rnd = torch.rand(1, 192, 200) # 噪声(加入随机因子) + + device = "cpu" # 导出时设备(不影响使用模型) + + + net_g = SynthesizerTrnMsNSFsidM( + *cpt["config"], is_half=False, version=cpt.get("version", "v1") + ) # fp32导出(C++要支持fp16必须手动将内存重新排列所以暂时不用fp16) + net_g.load_state_dict(cpt["weight"], strict=False) + input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"] + output_names = [ + "audio", + ] + # net_g.construct_spkmixmap(n_speaker) 多角色混合轨道导出 + torch.onnx.export( + net_g, + ( + test_phone.to(device), + test_phone_lengths.to(device), + test_pitch.to(device), + test_pitchf.to(device), + test_ds.to(device), + test_rnd.to(device), + ), + ExportedPath, + dynamic_axes={ + "phone": [1], + "pitch": [1], + "pitchf": [1], + "rnd": [2], + }, + do_constant_folding=False, + opset_version=13, + verbose=False, + input_names=input_names, + output_names=output_names, + ) + return "Finished" + + +#region Mangio-RVC-Fork CLI App +import re as regex +import scipy.io.wavfile as wavfile + +cli_current_page = "HOME" + +def cli_split_command(com): + exp = r'(?:(?<=\s)|^)"(.*?)"(?=\s|$)|(\S+)' + split_array = regex.findall(exp, com) + split_array = [group[0] if group[0] else group[1] for group in split_array] + return split_array + +def execute_generator_function(genObject): + for _ in genObject: pass + +def cli_infer(com): + # get VC first + com = cli_split_command(com) + model_name = com[0] + source_audio_path = com[1] + output_file_name = com[2] + feature_index_path = com[3] + f0_file = None # Not Implemented Yet + + # Get parameters for inference + speaker_id = int(com[4]) + transposition = float(com[5]) + f0_method = com[6] + crepe_hop_length = int(com[7]) + harvest_median_filter = int(com[8]) + resample = int(com[9]) + mix = float(com[10]) + feature_ratio = float(com[11]) + protection_amnt = float(com[12]) + ##### + + print("Mangio-RVC-Fork Infer-CLI: Starting the inference...") + vc_data = get_vc(model_name) + print(vc_data) + print("Mangio-RVC-Fork Infer-CLI: Performing inference...") + conversion_data = vc_single( + speaker_id, + source_audio_path, + source_audio_path, + transposition, + f0_file, + f0_method, + feature_index_path, + feature_index_path, + feature_ratio, + harvest_median_filter, + resample, + mix, + protection_amnt, + crepe_hop_length, + ) + if "Success." in conversion_data[0]: + print("Mangio-RVC-Fork Infer-CLI: Inference succeeded. Writing to %s/%s..." % ('audio-outputs', output_file_name)) + wavfile.write('%s/%s' % ('audio-outputs', output_file_name), conversion_data[1][0], conversion_data[1][1]) + print("Mangio-RVC-Fork Infer-CLI: Finished! Saved output to %s/%s" % ('audio-outputs', output_file_name)) + else: + print("Mangio-RVC-Fork Infer-CLI: Inference failed. Here's the traceback: ") + print(conversion_data[0]) + +def cli_pre_process(com): + com = cli_split_command(com) + model_name = com[0] + trainset_directory = com[1] + sample_rate = com[2] + num_processes = int(com[3]) + + print("Mangio-RVC-Fork Pre-process: Starting...") + generator = preprocess_dataset( + trainset_directory, + model_name, + sample_rate, + num_processes + ) + execute_generator_function(generator) + print("Mangio-RVC-Fork Pre-process: Finished") + +def cli_extract_feature(com): + com = cli_split_command(com) + model_name = com[0] + gpus = com[1] + num_processes = int(com[2]) + has_pitch_guidance = True if (int(com[3]) == 1) else False + f0_method = com[4] + crepe_hop_length = int(com[5]) + version = com[6] # v1 or v2 + + print("Mangio-RVC-CLI: Extract Feature Has Pitch: " + str(has_pitch_guidance)) + print("Mangio-RVC-CLI: Extract Feature Version: " + str(version)) + print("Mangio-RVC-Fork Feature Extraction: Starting...") + generator = extract_f0_feature( + gpus, + num_processes, + f0_method, + has_pitch_guidance, + model_name, + version, + crepe_hop_length + ) + execute_generator_function(generator) + print("Mangio-RVC-Fork Feature Extraction: Finished") + +def cli_train(com): + com = cli_split_command(com) + model_name = com[0] + sample_rate = com[1] + has_pitch_guidance = True if (int(com[2]) == 1) else False + speaker_id = int(com[3]) + save_epoch_iteration = int(com[4]) + total_epoch = int(com[5]) # 10000 + batch_size = int(com[6]) + gpu_card_slot_numbers = com[7] + if_save_latest = i18n("是") if (int(com[8]) == 1) else i18n("否") + if_cache_gpu = i18n("是") if (int(com[9]) == 1) else i18n("否") + if_save_every_weight = i18n("是") if (int(com[10]) == 1) else i18n("否") + version = com[11] + + pretrained_base = "pretrained/" if version == "v1" else "pretrained_v2/" + + g_pretrained_path = "%sf0G%s.pth" % (pretrained_base, sample_rate) + d_pretrained_path = "%sf0D%s.pth" % (pretrained_base, sample_rate) + + print("Mangio-RVC-Fork Train-CLI: Training...") + click_train( + model_name, + sample_rate, + has_pitch_guidance, + speaker_id, + save_epoch_iteration, + total_epoch, + batch_size, + if_save_latest, + g_pretrained_path, + d_pretrained_path, + gpu_card_slot_numbers, + if_cache_gpu, + if_save_every_weight, + version + ) + +def cli_train_feature(com): + com = cli_split_command(com) + model_name = com[0] + version = com[1] + print("Mangio-RVC-Fork Train Feature Index-CLI: Training... Please wait") + generator = train_index( + model_name, + version + ) + execute_generator_function(generator) + print("Mangio-RVC-Fork Train Feature Index-CLI: Done!") + +def cli_extract_model(com): + com = cli_split_command(com) + model_path = com[0] + save_name = com[1] + sample_rate = com[2] + has_pitch_guidance = com[3] + info = com[4] + version = com[5] + extract_small_model_process = extract_small_model( + model_path, + save_name, + sample_rate, + has_pitch_guidance, + info, + version + ) + if extract_small_model_process == "Success.": + print("Mangio-RVC-Fork Extract Small Model: Success!") + else: + print(str(extract_small_model_process)) + print("Mangio-RVC-Fork Extract Small Model: Failed!") + + +def preset_apply(preset, qfer, tmbr): + if str(preset) != '': + with open(str(preset), 'r') as p: + content = p.readlines() + qfer, tmbr = content[0].split('\n')[0], content[1] + + formant_apply(qfer, tmbr) + else: + pass + return ({"value": qfer, "__type__": "update"}, {"value": tmbr, "__type__": "update"}) + +def print_page_details(): + if cli_current_page == "HOME": + print(" go home : Takes you back to home with a navigation list.") + print(" go infer : Takes you to inference command execution.\n") + print(" go pre-process : Takes you to training step.1) pre-process command execution.") + print(" go extract-feature : Takes you to training step.2) extract-feature command execution.") + print(" go train : Takes you to training step.3) being or continue training command execution.") + print(" go train-feature : Takes you to the train feature index command execution.\n") + print(" go extract-model : Takes you to the extract small model command execution.") + elif cli_current_page == "INFER": + print(" arg 1) model name with .pth in ./weights: mi-test.pth") + print(" arg 2) source audio path: myFolder\\MySource.wav") + print(" arg 3) output file name to be placed in './audio-outputs': MyTest.wav") + print(" arg 4) feature index file path: logs/mi-test/added_IVF3042_Flat_nprobe_1.index") + print(" arg 5) speaker id: 0") + print(" arg 6) transposition: 0") + print(" arg 7) f0 method: harvest (pm, harvest, crepe, crepe-tiny, hybrid[x,x,x,x], mangio-crepe, mangio-crepe-tiny)") + print(" arg 8) crepe hop length: 160") + print(" arg 9) harvest median filter radius: 3 (0-7)") + print(" arg 10) post resample rate: 0") + print(" arg 11) mix volume envelope: 1") + print(" arg 12) feature index ratio: 0.78 (0-1)") + print(" arg 13) Voiceless Consonant Protection (Less Artifact): 0.33 (Smaller number = more protection. 0.50 means Dont Use.) \n") + print("Example: mi-test.pth saudio/Sidney.wav myTest.wav logs/mi-test/added_index.index 0 -2 harvest 160 3 0 1 0.95 0.33") + elif cli_current_page == "PRE-PROCESS": + print(" arg 1) Model folder name in ./logs: mi-test") + print(" arg 2) Trainset directory: mydataset (or) E:\\my-data-set") + print(" arg 3) Sample rate: 40k (32k, 40k, 48k)") + print(" arg 4) Number of CPU threads to use: 8 \n") + print("Example: mi-test mydataset 40k 24") + elif cli_current_page == "EXTRACT-FEATURE": + print(" arg 1) Model folder name in ./logs: mi-test") + print(" arg 2) Gpu card slot: 0 (0-1-2 if using 3 GPUs)") + print(" arg 3) Number of CPU threads to use: 8") + print(" arg 4) Has Pitch Guidance?: 1 (0 for no, 1 for yes)") + print(" arg 5) f0 Method: harvest (pm, harvest, dio, crepe)") + print(" arg 6) Crepe hop length: 128") + print(" arg 7) Version for pre-trained models: v2 (use either v1 or v2)\n") + print("Example: mi-test 0 24 1 harvest 128 v2") + elif cli_current_page == "TRAIN": + print(" arg 1) Model folder name in ./logs: mi-test") + print(" arg 2) Sample rate: 40k (32k, 40k, 48k)") + print(" arg 3) Has Pitch Guidance?: 1 (0 for no, 1 for yes)") + print(" arg 4) speaker id: 0") + print(" arg 5) Save epoch iteration: 50") + print(" arg 6) Total epochs: 10000") + print(" arg 7) Batch size: 8") + print(" arg 8) Gpu card slot: 0 (0-1-2 if using 3 GPUs)") + print(" arg 9) Save only the latest checkpoint: 0 (0 for no, 1 for yes)") + print(" arg 10) Whether to cache training set to vram: 0 (0 for no, 1 for yes)") + print(" arg 11) Save extracted small model every generation?: 0 (0 for no, 1 for yes)") + print(" arg 12) Model architecture version: v2 (use either v1 or v2)\n") + print("Example: mi-test 40k 1 0 50 10000 8 0 0 0 0 v2") + elif cli_current_page == "TRAIN-FEATURE": + print(" arg 1) Model folder name in ./logs: mi-test") + print(" arg 2) Model architecture version: v2 (use either v1 or v2)\n") + print("Example: mi-test v2") + elif cli_current_page == "EXTRACT-MODEL": + print(" arg 1) Model Path: logs/mi-test/G_168000.pth") + print(" arg 2) Model save name: MyModel") + print(" arg 3) Sample rate: 40k (32k, 40k, 48k)") + print(" arg 4) Has Pitch Guidance?: 1 (0 for no, 1 for yes)") + print(' arg 5) Model information: "My Model"') + print(" arg 6) Model architecture version: v2 (use either v1 or v2)\n") + print('Example: logs/mi-test/G_168000.pth MyModel 40k 1 "Created by Cole Mangio" v2') + print("") + +def change_page(page): + global cli_current_page + cli_current_page = page + return 0 + +def execute_command(com): + if com == "go home": + return change_page("HOME") + elif com == "go infer": + return change_page("INFER") + elif com == "go pre-process": + return change_page("PRE-PROCESS") + elif com == "go extract-feature": + return change_page("EXTRACT-FEATURE") + elif com == "go train": + return change_page("TRAIN") + elif com == "go train-feature": + return change_page("TRAIN-FEATURE") + elif com == "go extract-model": + return change_page("EXTRACT-MODEL") + else: + if com[:3] == "go ": + print("page '%s' does not exist!" % com[3:]) + return 0 + + if cli_current_page == "INFER": + cli_infer(com) + elif cli_current_page == "PRE-PROCESS": + cli_pre_process(com) + elif cli_current_page == "EXTRACT-FEATURE": + cli_extract_feature(com) + elif cli_current_page == "TRAIN": + cli_train(com) + elif cli_current_page == "TRAIN-FEATURE": + cli_train_feature(com) + elif cli_current_page == "EXTRACT-MODEL": + cli_extract_model(com) + +def cli_navigation_loop(): + while True: + print("You are currently in '%s':" % cli_current_page) + print_page_details() + command = input("%s: " % cli_current_page) + try: + execute_command(command) + except: + print(traceback.format_exc()) + +if(config.is_cli): + print("\n\nMangio-RVC-Fork v2 CLI App!\n") + print("Welcome to the CLI version of RVC. Please read the documentation on https://github.com/Mangio621/Mangio-RVC-Fork (README.MD) to understand how to use this app.\n") + cli_navigation_loop() + +#endregion + +#region RVC WebUI App + +def get_presets(): + data = None + with open('../inference-presets.json', 'r') as file: + data = json.load(file) + preset_names = [] + for preset in data['presets']: + preset_names.append(preset['name']) + + return preset_names + +def match_index(sid0): + picked = False + #folder = sid0.split('.')[0] + + #folder = re.split(r'. |_', sid0)[0] + folder = sid0.split('.')[0].split('_')[0] + #folder_test = sid0.split('.')[0].split('_')[0].split('-')[0] + parent_dir = "./logs/" + folder + #print(parent_dir) + if os.path.exists(parent_dir): + #print('path exists') + for filename in os.listdir(parent_dir.replace('\\','/')): + if filename.endswith(".index"): + for i in range(len(indexes_list)): + if indexes_list[i] == (os.path.join(("./logs/" + folder), filename).replace('\\','/')): + print('regular index found') + break + else: + if indexes_list[i] == (os.path.join(("./logs/" + folder.lower()), filename).replace('\\','/')): + print('lowered index found') + parent_dir = "./logs/" + folder.lower() + break + #elif (indexes_list[i]).casefold() == ((os.path.join(("./logs/" + folder), filename).replace('\\','/')).casefold()): + # print('8') + # parent_dir = "./logs/" + folder.casefold() + # break + #elif (indexes_list[i]) == ((os.path.join(("./logs/" + folder_test), filename).replace('\\','/'))): + # parent_dir = "./logs/" + folder_test + # print(parent_dir) + # break + #elif (indexes_list[i]) == (os.path.join(("./logs/" + folder_test.lower()), filename).replace('\\','/')): + # parent_dir = "./logs/" + folder_test + # print(parent_dir) + # break + #else: + # #print('couldnt find index') + # continue + + #print('all done') + index_path=os.path.join(parent_dir.replace('\\','/'), filename.replace('\\','/')).replace('\\','/') + #print(index_path) + return (index_path, index_path) + + + else: + #print('nothing found') + return ('', '') + +def choveraudio(): + return '' + + +#Default-GUI +with gr.Blocks(theme='HaleyCH/HaleyCH_Theme') as app: + gr.HTML("

The Mangio-RVC-Fork 💻

") + gr.Markdown( + value=i18n( + "本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.
如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录使用需遵守的协议-LICENSE.txt." + ) + ) + with gr.Tabs(): + + with gr.TabItem(i18n("模型推理")): + # Inference Preset Row + # with gr.Row(): + # mangio_preset = gr.Dropdown(label="Inference Preset", choices=sorted(get_presets())) + # mangio_preset_name_save = gr.Textbox( + # label="Your preset name" + # ) + # mangio_preset_save_btn = gr.Button('Save Preset', variant="primary") + + # Other RVC stuff + with gr.Row(): + + #sid0 = gr.Dropdown(label=i18n("推理音色"), choices=sorted(names), value=check_for_name()) + sid0 = gr.Dropdown(label=i18n("推理音色"), choices=sorted(names), value='') + #input_audio_path2 + + + refresh_button = gr.Button(i18n("Refresh voice list, index path and audio files"), variant="primary") + clean_button = gr.Button(i18n("卸载音色省显存"), variant="primary") + spk_item = gr.Slider( + minimum=0, + maximum=2333, + step=1, + label=i18n("请选择说话人id"), + value=0, + visible=False, + interactive=True, + ) + clean_button.click(fn=clean, inputs=[], outputs=[sid0]) + + with gr.Group(): + gr.Markdown( + value=i18n("男转女推荐+12key, 女转男推荐-12key, 如果音域爆炸导致音色失真也可以自己调整到合适音域. ") + ) + with gr.Row(): + with gr.Column(): + vc_transform0 = gr.Number( + label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0 + ) + input_audio0 = gr.Textbox( + label=i18n("Add audio's name to the path to the audio file to be processed (default is the correct format example) Remove the path to use an audio from the dropdown list:"), + value=os.path.abspath(os.getcwd()).replace('\\', '/') + "/audios/" + "audio.wav", + ) + input_audio1 = gr.Dropdown( + label=i18n("Auto detect audio path and select from the dropdown:"), + choices=sorted(audio_paths), + value=get_audios(), + interactive=True, + ) + input_audio1.change(fn=choveraudio,inputs=[],outputs=[input_audio0]) + f0method0 = gr.Radio( + label=i18n( + "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU" + ), + choices=["pm", "harvest", "dio", "crepe", "crepe-tiny", "mangio-crepe", "mangio-crepe-tiny", "rmvpe"], # Fork Feature. Add Crepe-Tiny + value="rmvpe", + interactive=True, + ) + crepe_hop_length = gr.Slider( + minimum=1, + maximum=512, + step=1, + label=i18n("crepe_hop_length"), + value=120, + interactive=True + ) + filter_radius0 = gr.Slider( + minimum=0, + maximum=7, + label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波,数值为滤波半径,使用可以削弱哑音"), + value=3, + step=1, + interactive=True, + ) + with gr.Column(): + file_index1 = gr.Textbox( + label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"), + value="", + interactive=True, + ) + + file_index2 = gr.Dropdown( + label="3. Path to your added.index file (if it didn't automatically find it.)", + choices=get_indexes(), + value=get_index(), + interactive=True, + allow_custom_value=True, + ) + #sid0.select(fn=match_index, inputs=sid0, outputs=file_index2) + + + + + refresh_button.click( + fn=change_choices, inputs=[], outputs=[sid0, file_index2, input_audio1] + ) + # file_big_npy1 = gr.Textbox( + # label=i18n("特征文件路径"), + # value="E:\\codes\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy", + # interactive=True, + # ) + index_rate1 = gr.Slider( + minimum=0, + maximum=1, + label=i18n("检索特征占比"), + value=0.75, + interactive=True, + ) + with gr.Column(): + resample_sr0 = gr.Slider( + minimum=0, + maximum=48000, + label=i18n("后处理重采样至最终采样率,0为不进行重采样"), + value=0, + step=1, + interactive=True, + ) + rms_mix_rate0 = gr.Slider( + minimum=0, + maximum=1, + label=i18n("输入源音量包络替换输出音量包络融合比例,越靠近1越使用输出包络"), + value=0.25, + interactive=True, + ) + protect0 = gr.Slider( + minimum=0, + maximum=0.5, + label=i18n( + "保护清辅音和呼吸声,防止电音撕裂等artifact,拉满0.5不开启,调低加大保护力度但可能降低索引效果" + ), + value=0.33, + step=0.01, + interactive=True, + ) + formanting = gr.Checkbox( + value=False, + label="[EXPERIMENTAL, WAV ONLY] Formant shift inference audio", + info="Used for male to female and vice-versa conversions", + interactive=True, + visible=True, + ) + + formant_preset = gr.Dropdown( + value='', + choices=get_fshift_presets(), + label="browse presets for formanting", + visible=False, + ) + formant_refresh_button = gr.Button(value='\U0001f504', visible=False,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, + label="Quefrency for formant shifting", + minimum=-16.0, + maximum=16.0, + step=0.1, + visible=False, + interactive=True, + ) + tmbre = gr.Slider( + value=Timbre, + label="Timbre for formant shifting", + minimum=-16.0, + maximum=16.0, + step=0.1, + visible=False, + interactive=True, + ) + + formant_preset.change(fn=preset_apply, inputs=[formant_preset, qfrency, tmbre], outputs=[qfrency, tmbre]) + frmntbut = gr.Button("Apply", variant="primary", visible=False) + formanting.change(fn=formant_enabled,inputs=[formanting,qfrency,tmbre,frmntbut,formant_preset,formant_refresh_button],outputs=[formanting,qfrency,tmbre,frmntbut,formant_preset,formant_refresh_button]) + frmntbut.click(fn=formant_apply,inputs=[qfrency, tmbre], outputs=[qfrency, tmbre]) + formant_refresh_button.click(fn=update_fshift_presets,inputs=[formant_preset, qfrency, tmbre],outputs=[formant_preset, qfrency, tmbre]) + ##formant_refresh_button.click(fn=preset_apply, inputs=[formant_preset, qfrency, tmbre], outputs=[formant_preset, qfrency, tmbre]) + ##formant_refresh_button.click(fn=update_fshift_presets, inputs=[formant_preset, qfrency, tmbre], outputs=[formant_preset, qfrency, tmbre]) + f0_file = gr.File(label=i18n("F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调")) + but0 = gr.Button(i18n("转换"), variant="primary") + with gr.Row(): + vc_output1 = gr.Textbox(label=i18n("输出信息")) + vc_output2 = gr.Audio(label=i18n("输出音频(右下角三个点,点了可以下载)")) + but0.click( + vc_single, + [ + spk_item, + input_audio0, + input_audio1, + vc_transform0, + f0_file, + f0method0, + file_index1, + file_index2, + # file_big_npy1, + index_rate1, + filter_radius0, + resample_sr0, + rms_mix_rate0, + protect0, + crepe_hop_length + ], + [vc_output1, vc_output2], + ) + with gr.Group(): + gr.Markdown( + value=i18n("批量转换, 输入待转换音频文件夹, 或上传多个音频文件, 在指定文件夹(默认opt)下输出转换的音频. ") + ) + with gr.Row(): + with gr.Column(): + vc_transform1 = gr.Number( + label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0 + ) + opt_input = gr.Textbox(label=i18n("指定输出文件夹"), value="opt") + f0method1 = gr.Radio( + label=i18n( + "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU" + ), + choices=["pm", "harvest", "crepe", "rmvpe"], + value="rmvpe", + interactive=True, + ) + filter_radius1 = gr.Slider( + minimum=0, + maximum=7, + label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波,数值为滤波半径,使用可以削弱哑音"), + value=3, + step=1, + interactive=True, + ) + with gr.Column(): + file_index3 = gr.Textbox( + label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"), + value="", + interactive=True, + ) + file_index4 = gr.Dropdown( #file index dropdown for batch + label=i18n("自动检测index路径,下拉式选择(dropdown)"), + choices=get_indexes(), + value=get_index(), + interactive=True, + ) + sid0.select(fn=match_index, inputs=[sid0], outputs=[file_index2, file_index4]) + refresh_button.click( + fn=lambda: change_choices()[1], + inputs=[], + outputs=file_index4, + ) + # file_big_npy2 = gr.Textbox( + # label=i18n("特征文件路径"), + # value="E:\\codes\\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy", + # interactive=True, + # ) + index_rate2 = gr.Slider( + minimum=0, + maximum=1, + label=i18n("检索特征占比"), + value=1, + interactive=True, + ) + with gr.Column(): + resample_sr1 = gr.Slider( + minimum=0, + maximum=48000, + label=i18n("后处理重采样至最终采样率,0为不进行重采样"), + value=0, + step=1, + interactive=True, + ) + rms_mix_rate1 = gr.Slider( + minimum=0, + maximum=1, + label=i18n("输入源音量包络替换输出音量包络融合比例,越靠近1越使用输出包络"), + value=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=os.path.abspath(os.getcwd()).replace('\\', '/') + "/audios/", + ) + inputs = gr.File( + file_count="multiple", label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹") + ) + with gr.Row(): + format1 = gr.Radio( + label=i18n("导出文件格式"), + choices=["wav", "flac", "mp3", "m4a"], + value="flac", + interactive=True, + ) + but1 = gr.Button(i18n("转换"), variant="primary") + vc_output3 = gr.Textbox(label=i18n("输出信息")) + but1.click( + vc_multi, + [ + spk_item, + dir_input, + opt_input, + inputs, + vc_transform1, + f0method1, + file_index3, + file_index4, + # file_big_npy2, + index_rate2, + filter_radius1, + resample_sr1, + rms_mix_rate1, + protect1, + format1, + crepe_hop_length, + ], + [vc_output3], + ) + sid0.change( + fn=get_vc, + inputs=[sid0, protect0, protect1], + outputs=[spk_item, protect0, protect1], + ) + with gr.TabItem(i18n("伴奏人声分离&去混响&去回声")): + with gr.Group(): + gr.Markdown( + value=i18n( + "人声伴奏分离批量处理, 使用UVR5模型。
" + "合格的文件夹路径格式举例: E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(去文件管理器地址栏拷就行了)。
" + "模型分为三类:
" + "1、保留人声:不带和声的音频选这个,对主人声保留比HP5更好。内置HP2和HP3两个模型,HP3可能轻微漏伴奏但对主人声保留比HP2稍微好一丁点;
" + "2、仅保留主人声:带和声的音频选这个,对主人声可能有削弱。内置HP5一个模型;
" + "3、去混响、去延迟模型(by FoxJoy):
" + "  (1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择,不能去除单通道混响;
" + " (234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底,DeReverb额外去除混响,可去除单声道混响,但是对高频重的板式混响去不干净。
" + "去混响/去延迟,附:
" + "1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍;
" + "2、MDX-Net-Dereverb模型挺慢的;
" + "3、个人推荐的最干净的配置是先MDX-Net再DeEcho-Aggressive。" + ) + ) + with gr.Row(): + with gr.Column(): + dir_wav_input = gr.Textbox( + label=i18n("输入待处理音频文件夹路径"), + value=((os.getcwd()).replace('\\', '/') + "/audios/") + ) + wav_inputs = gr.File( + file_count="multiple", label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹") + ) ##### + with gr.Column(): + model_choose = gr.Dropdown(label=i18n("模型"), choices=uvr5_names) + agg = gr.Slider( + minimum=0, + maximum=20, + step=1, + label="人声提取激进程度", + value=10, + interactive=True, + visible=False, # 先不开放调整 + ) + opt_vocal_root = gr.Textbox( + label=i18n("指定输出主人声文件夹"), value="opt" + ) + opt_ins_root = gr.Textbox( + label=i18n("指定输出非主人声文件夹"), value="opt" + ) + format0 = gr.Radio( + label=i18n("导出文件格式"), + choices=["wav", "flac", "mp3", "m4a"], + value="flac", + interactive=True, + ) + but2 = gr.Button(i18n("转换"), variant="primary") + vc_output4 = gr.Textbox(label=i18n("输出信息")) + but2.click( + uvr, + [ + model_choose, + dir_wav_input, + opt_vocal_root, + wav_inputs, + opt_ins_root, + agg, + format0, + ], + [vc_output4], + ) + with gr.TabItem(i18n("训练")): + gr.Markdown( + value=i18n( + "step1: 填写实验配置. 实验数据放在logs下, 每个实验一个文件夹, 需手工输入实验名路径, 内含实验配置, 日志, 训练得到的模型文件. " + ) + ) + with gr.Row(): + exp_dir1 = gr.Textbox(label=i18n("输入实验名"), value="mi-test") + sr2 = gr.Radio( + label=i18n("目标采样率"), + choices=["40k", "48k"], + value="40k", + interactive=True, + ) + if_f0_3 = gr.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"), + choices=["v1", "v2"], + value="v1", + interactive=True, + visible=True, + ) + np7 = gr.Slider( + minimum=0, + maximum=config.n_cpu, + step=1, + label=i18n("提取音高和处理数据使用的CPU进程数"), + value=int(np.ceil(config.n_cpu / 1.5)), + interactive=True, + ) + with gr.Group(): # 暂时单人的, 后面支持最多4人的#数据处理 + gr.Markdown( + value=i18n( + "step2a: 自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练. " + ) + ) + with gr.Row(): + trainset_dir4 = gr.Textbox( + label=i18n("输入训练文件夹路径"), value=os.path.abspath(os.getcwd()) + "\\datasets\\" + ) + spk_id5 = gr.Slider( + minimum=0, + maximum=4, + step=1, + label=i18n("请指定说话人id"), + value=0, + interactive=True, + ) + but1 = gr.Button(i18n("处理数据"), variant="primary") + info1 = gr.Textbox(label=i18n("输出信息"), value="") + but1.click( + preprocess_dataset, [trainset_dir4, exp_dir1, sr2, np7], [info1] + ) + with gr.Group(): + gr.Markdown(value=i18n("step2b: 使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号)")) + with gr.Row(): + with gr.Column(): + gpus6 = gr.Textbox( + label=i18n("以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2"), + value=gpus, + interactive=True, + ) + gpu_info9 = gr.Textbox(label=i18n("显卡信息"), value=gpu_info) + with gr.Column(): + f0method8 = gr.Radio( + label=i18n( + "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢" + ), + choices=["pm", "harvest", "dio", "crepe", "mangio-crepe", "rmvpe"], # Fork feature: Crepe on f0 extraction for training. + value="rmvpe", + interactive=True, + ) + extraction_crepe_hop_length = gr.Slider( + minimum=1, + maximum=512, + step=1, + label=i18n("crepe_hop_length"), + value=64, + interactive=True + ) + 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, extraction_crepe_hop_length], + [info2], + ) + with gr.Group(): + gr.Markdown(value=i18n("step3: 填写训练设置, 开始训练模型和索引")) + with gr.Row(): + save_epoch10 = gr.Slider( + minimum=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=10000, + step=1, + label=i18n("总训练轮数total_epoch"), + value=20, + interactive=True, + ) + batch_size12 = gr.Slider( + minimum=1, + maximum=40, + step=1, + label=i18n("每张显卡的batch_size"), + value=default_batch_size, + interactive=True, + ) + if_save_latest13 = gr.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("是"), + 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("是"), + interactive=True, + ) + with gr.Row(): + pretrained_G14 = gr.Textbox( + lines=2, + label=i18n("加载预训练底模G路径"), + value="pretrained/f0G40k.pth", + interactive=True, + ) + pretrained_D15 = gr.Textbox( + lines=2, + label=i18n("加载预训练底模D路径"), + value="pretrained/f0D40k.pth", + interactive=True, + ) + sr2.change( + change_sr2, + [sr2, if_f0_3, version19], + [pretrained_G14, pretrained_D15], + ) + version19.change( + change_version19, + [sr2, if_f0_3, version19], + [pretrained_G14, pretrained_D15, sr2], + ) + if_f0_3.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, + # extraction_crepe_hop_length + # ], + # info3, + #) + + with gr.TabItem(i18n("ckpt处理")): + with gr.Group(): + gr.Markdown(value=i18n("模型融合, 可用于测试音色融合")) + with gr.Row(): + ckpt_a = gr.Textbox(label=i18n("A模型路径"), value="", interactive=True) + 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=["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( + lines=3, + label=i18n("模型路径"), + value=os.path.abspath(os.getcwd()).replace('\\', '/') + "/logs/[YOUR_MODEL]/G_23333.pth", + interactive=True, + ) + save_name = gr.Textbox( + label=i18n("保存名"), value="", interactive=True + ) + 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="v2", + 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(): + infoOnnx = gr.Label(label="info") + with gr.Row(): + butOnnx = gr.Button(i18n("导出Onnx模型"), variant="primary") + butOnnx.click(export_onnx, [ckpt_dir, onnx_dir], infoOnnx) + + tab_faq = i18n("常见问题解答") + with gr.TabItem(tab_faq): + try: + if tab_faq == "常见问题解答": + with open("docs/faq.md", "r", encoding="utf8") as f: + info = f.read() + else: + with open("docs/faq_en.md", "r", encoding="utf8") as f: + info = f.read() + gr.Markdown(value=info) + except: + gr.Markdown(traceback.format_exc()) + + + #region Mangio Preset Handler Region + def save_preset( + preset_name, + sid0, + vc_transform, + input_audio0, + input_audio1, + f0method, + crepe_hop_length, + filter_radius, + file_index1, + file_index2, + index_rate, + resample_sr, + rms_mix_rate, + protect, + f0_file + ): + data = None + with open('../inference-presets.json', 'r') as file: + data = json.load(file) + preset_json = { + 'name': preset_name, + 'model': sid0, + 'transpose': vc_transform, + 'audio_file': input_audio0, + 'auto_audio_file': input_audio1, + 'f0_method': f0method, + 'crepe_hop_length': crepe_hop_length, + 'median_filtering': filter_radius, + 'feature_path': file_index1, + 'auto_feature_path': file_index2, + 'search_feature_ratio': index_rate, + 'resample': resample_sr, + 'volume_envelope': rms_mix_rate, + 'protect_voiceless': protect, + 'f0_file_path': f0_file + } + data['presets'].append(preset_json) + with open('../inference-presets.json', 'w') as file: + json.dump(data, file) + file.flush() + print("Saved Preset %s into inference-presets.json!" % preset_name) + + + def on_preset_changed(preset_name): + print("Changed Preset to %s!" % preset_name) + data = None + with open('../inference-presets.json', 'r') as file: + data = json.load(file) + + print("Searching for " + preset_name) + returning_preset = None + for preset in data['presets']: + if(preset['name'] == preset_name): + print("Found a preset") + returning_preset = preset + # return all new input values + return ( + # returning_preset['model'], + # returning_preset['transpose'], + # returning_preset['audio_file'], + # returning_preset['f0_method'], + # returning_preset['crepe_hop_length'], + # returning_preset['median_filtering'], + # returning_preset['feature_path'], + # returning_preset['auto_feature_path'], + # returning_preset['search_feature_ratio'], + # returning_preset['resample'], + # returning_preset['volume_envelope'], + # returning_preset['protect_voiceless'], + # returning_preset['f0_file_path'] + ) + + # Preset State Changes + + # This click calls save_preset that saves the preset into inference-presets.json with the preset name + # mangio_preset_save_btn.click( + # fn=save_preset, + # inputs=[ + # mangio_preset_name_save, + # sid0, + # vc_transform0, + # input_audio0, + # f0method0, + # crepe_hop_length, + # filter_radius0, + # file_index1, + # file_index2, + # index_rate1, + # resample_sr0, + # rms_mix_rate0, + # protect0, + # f0_file + # ], + # outputs=[] + # ) + + # mangio_preset.change( + # on_preset_changed, + # inputs=[ + # # Pass inputs here + # mangio_preset + # ], + # outputs=[ + # # Pass Outputs here. These refer to the gradio elements that we want to directly change + # # sid0, + # # vc_transform0, + # # input_audio0, + # # f0method0, + # # crepe_hop_length, + # # filter_radius0, + # # file_index1, + # # file_index2, + # # index_rate1, + # # resample_sr0, + # # rms_mix_rate0, + # # protect0, + # # f0_file + # ] + # ) + #endregion + + # with gr.TabItem(i18n("招募音高曲线前端编辑器")): + # gr.Markdown(value=i18n("加开发群联系我xxxxx")) + # with gr.TabItem(i18n("点击查看交流、问题反馈群号")): + # gr.Markdown(value=i18n("xxxxx")) + + if config.iscolab or config.paperspace: # Share gradio link for colab and paperspace (FORK FEATURE) + app.queue(concurrency_count=511, max_size=1022).launch(share=True) + else: + app.queue(concurrency_count=511, max_size=1022).launch( + server_name="0.0.0.0", + inbrowser=not config.noautoopen, + server_port=config.listen_port, + quiet=True, + ) + +#endregion +''' #End of Default-GUI + +with gr.Blocks(theme='HaleyCH/HaleyCH_Theme') as app: + gr.HTML("

The Mangio-RVC-Fork 💻

") + gr.Markdown( + value=i18n( + "本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.
如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录使用需遵守的协议-LICENSE.txt." + ) + ) + with gr.Tabs(): + + with gr.TabItem(i18n("模型推理")): + # Inference Preset Row + # with gr.Row(): + # mangio_preset = gr.Dropdown(label="Inference Preset", choices=sorted(get_presets())) + # mangio_preset_name_save = gr.Textbox( + # label="Your preset name" + # ) + # mangio_preset_save_btn = gr.Button('Save Preset', variant="primary") + + # Other RVC stuff + with gr.Row(): + + #sid0 = gr.Dropdown(label=i18n("推理音色"), choices=sorted(names), value=check_for_name()) + sid0 = gr.Dropdown(label=i18n("推理音色"), choices=sorted(names), value='') + #input_audio_path2 + + + refresh_button = gr.Button(i18n("Refresh voice list, index path and audio files"), variant="primary") + clean_button = gr.Button(i18n("卸载音色省显存"), variant="primary") + spk_item = gr.Slider( + minimum=0, + maximum=2333, + step=1, + label=i18n("请选择说话人id"), + value=0, + visible=False, + interactive=True, + ) + clean_button.click(fn=clean, inputs=[], outputs=[sid0]) + + with gr.Group(): + gr.Markdown( + value=i18n("男转女推荐+12key, 女转男推荐-12key, 如果音域爆炸导致音色失真也可以自己调整到合适音域. ") + ) + with gr.Row(): + with gr.Column(): + vc_transform0 = gr.Number( + label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0 + ) + input_audio0 = gr.Textbox( + label=i18n("Add audio's name to the path to the audio file to be processed (default is the correct format example) Remove the path to use an audio from the dropdown list:"), + value=os.path.abspath(os.getcwd()).replace('\\', '/') + "/audios/" + "audio.wav", + ) + input_audio1 = gr.Dropdown( + label=i18n("Auto detect audio path and select from the dropdown:"), + choices=sorted(audio_paths), + value=get_audios(), + interactive=True, + ) + input_audio1.change(fn=choveraudio,inputs=[],outputs=[input_audio0]) + with gr.Accordion("F0 Settings"): + f0method0 = gr.Radio( + label=i18n( + "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU" + ), + choices=["pm", "harvest", "dio", "crepe", "crepe-tiny", "mangio-crepe", "mangio-crepe-tiny", "rmvpe"], # Fork Feature. Add Crepe-Tiny + value="rmvpe", + interactive=True, + ) + crepe_hop_length = gr.Slider( + minimum=1, + maximum=512, + step=1, + label=i18n("crepe_hop_length"), + value=120, + interactive=True + ) + filter_radius0 = gr.Slider( + minimum=0, + maximum=7, + label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波,数值为滤波半径,使用可以削弱哑音"), + value=3, + step=1, + interactive=True, + ) + with gr.Column(): + file_index1 = gr.Textbox( + label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"), + value="", + interactive=True, + ) + + file_index2 = gr.Dropdown( + label="3. Path to your added.index file (if it didn't automatically find it.)", + choices=get_indexes(), + value=get_index(), + interactive=True, + allow_custom_value=True, + ) + #sid0.select(fn=match_index, inputs=sid0, outputs=file_index2) + + + + + refresh_button.click( + fn=change_choices, inputs=[], outputs=[sid0, file_index2, input_audio1] + ) + # file_big_npy1 = gr.Textbox( + # label=i18n("特征文件路径"), + # value="E:\\codes\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy", + # interactive=True, + # ) + index_rate1 = gr.Slider( + minimum=0, + maximum=1, + label=i18n("检索特征占比"), + value=0.75, + interactive=True, + ) + formant_preset = gr.Dropdown( + value='', + choices=get_fshift_presets(), + label="Browse Presets for Formanting", + visible=False, + ) + formant_refresh_button = gr.Button(value='\U0001f504', visible=False,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, + label="Quefrency for formant shifting", + minimum=-16.0, + maximum=16.0, + step=0.1, + visible=False, + interactive=True, + ) + tmbre = gr.Slider( + value=Timbre, + label="Timbre for formant shifting", + minimum=-16.0, + maximum=16.0, + step=0.1, + visible=False, + interactive=True, + ) + with gr.Column(): + resample_sr0 = gr.Slider( + minimum=0, + maximum=48000, + label=i18n("后处理重采样至最终采样率,0为不进行重采样"), + value=0, + step=1, + interactive=True, + ) + rms_mix_rate0 = gr.Slider( + minimum=0, + maximum=1, + label=i18n("输入源音量包络替换输出音量包络融合比例,越靠近1越使用输出包络"), + value=0.25, + interactive=True, + ) + protect0 = gr.Slider( + minimum=0, + maximum=0.5, + label=i18n( + "保护清辅音和呼吸声,防止电音撕裂等artifact,拉满0.5不开启,调低加大保护力度但可能降低索引效果" + ), + value=0.33, + step=0.01, + interactive=True, + ) + formanting = gr.Checkbox( + value=False, + label="[EXPERIMENTAL, WAV ONLY] Formant shift inference audio", + info="Used for male to female and vice-versa conversions", + interactive=True, + visible=True, + ) + + + + formant_preset.change(fn=preset_apply,inputs=[formant_preset, qfrency, tmbre],outputs=[qfrency, tmbre]) + frmntbut = gr.Button("Apply", variant="primary", visible=False) + 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=[], outputs=[formant_preset]) + f0_file = gr.File(label=i18n("F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调")) + but0 = gr.Button(i18n("转换"), variant="primary") + with gr.Row(): + vc_output1 = gr.Textbox(label=i18n("输出信息")) + vc_output2 = gr.Audio(label=i18n("输出音频(右下角三个点,点了可以下载)")) + but0.click( + vc_single, + [ + spk_item, + input_audio0, + input_audio1, + vc_transform0, + f0_file, + f0method0, + file_index1, + file_index2, + # file_big_npy1, + index_rate1, + filter_radius0, + resample_sr0, + rms_mix_rate0, + protect0, + crepe_hop_length + ], + [vc_output1, vc_output2], + ) + with gr.Group(): + with gr.Accordion("Batch conversion", open = False): + gr.Markdown( + value=i18n("批量转换, 输入待转换音频文件夹, 或上传多个音频文件, 在指定文件夹(默认opt)下输出转换的音频. ") + ) + with gr.Row(): + with gr.Column(): + vc_transform1 = gr.Number( + label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0 + ) + opt_input = gr.Textbox(label=i18n("指定输出文件夹"), value="opt") + f0method1 = gr.Radio( + label=i18n( + "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU" + ), + choices=["pm", "harvest", "crepe", "rmvpe"], + value="rmvpe", + interactive=True, + ) + filter_radius1 = gr.Slider( + minimum=0, + maximum=7, + label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波,数值为滤波半径,使用可以削弱哑音"), + value=3, + step=1, + interactive=True, + ) + + with gr.Column(): + file_index3 = gr.Textbox( + label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"), + value="", + interactive=True, + ) + file_index4 = gr.Dropdown( #file index dropdown for batch + label=i18n("自动检测index路径,下拉式选择(dropdown)"), + choices=get_indexes(), + value=get_index(), + interactive=True, + ) + sid0.select(fn=match_index, inputs=[sid0], outputs=[file_index2, file_index4]) + refresh_button.click( + fn=lambda: change_choices()[1], + inputs=[], + outputs=file_index4, + ) + # file_big_npy2 = gr.Textbox( + # label=i18n("特征文件路径"), + # value="E:\\codes\\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy", + # interactive=True, + # ) + index_rate2 = gr.Slider( + minimum=0, + maximum=1, + label=i18n("检索特征占比"), + value=1, + interactive=True, + ) + with gr.Column(): + resample_sr1 = gr.Slider( + minimum=0, + maximum=48000, + label=i18n("后处理重采样至最终采样率,0为不进行重采样"), + value=0, + step=1, + interactive=True, + ) + rms_mix_rate1 = gr.Slider( + minimum=0, + maximum=1, + label=i18n("输入源音量包络替换输出音量包络融合比例,越靠近1越使用输出包络"), + value=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=os.path.abspath(os.getcwd()).replace('\\', '/') + "/audios/", + ) + inputs = gr.File( + file_count="multiple", label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹") + ) + with gr.Row(): + format1 = gr.Radio( + label=i18n("导出文件格式"), + choices=["wav", "flac", "mp3", "m4a"], + value="flac", + interactive=True, + ) + but1 = gr.Button(i18n("转换"), variant="primary") + vc_output3 = gr.Textbox(label=i18n("输出信息")) + but1.click( + vc_multi, + [ + spk_item, + dir_input, + opt_input, + inputs, + vc_transform1, + f0method1, + file_index3, + file_index4, + # file_big_npy2, + index_rate2, + filter_radius1, + resample_sr1, + rms_mix_rate1, + protect1, + format1, + crepe_hop_length, + ], + [vc_output3], + ) + sid0.change( + fn=get_vc, + inputs=[sid0, protect0, protect1], + outputs=[spk_item, protect0, protect1], + ) + with gr.TabItem(i18n("伴奏人声分离&去混响&去回声")): + with gr.Group(): + gr.Markdown( + value=i18n( + "人声伴奏分离批量处理, 使用UVR5模型。
" + "合格的文件夹路径格式举例: E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(去文件管理器地址栏拷就行了)。
" + "模型分为三类:
" + "1、保留人声:不带和声的音频选这个,对主人声保留比HP5更好。内置HP2和HP3两个模型,HP3可能轻微漏伴奏但对主人声保留比HP2稍微好一丁点;
" + "2、仅保留主人声:带和声的音频选这个,对主人声可能有削弱。内置HP5一个模型;
" + "3、去混响、去延迟模型(by FoxJoy):
" + "  (1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择,不能去除单通道混响;
" + " (234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底,DeReverb额外去除混响,可去除单声道混响,但是对高频重的板式混响去不干净。
" + "去混响/去延迟,附:
" + "1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍;
" + "2、MDX-Net-Dereverb模型挺慢的;
" + "3、个人推荐的最干净的配置是先MDX-Net再DeEcho-Aggressive。" + ) + ) + with gr.Row(): + with gr.Column(): + dir_wav_input = gr.Textbox( + label=i18n("输入待处理音频文件夹路径"), + value=((os.getcwd()).replace('\\', '/') + "/audios/") + ) + wav_inputs = gr.File( + file_count="multiple", label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹") + ) ##### + with gr.Column(): + model_choose = gr.Dropdown(label=i18n("模型"), choices=uvr5_names) + agg = gr.Slider( + minimum=0, + maximum=20, + step=1, + label="人声提取激进程度", + value=10, + interactive=True, + visible=False, # 先不开放调整 + ) + opt_vocal_root = gr.Textbox( + label=i18n("指定输出主人声文件夹"), value="opt" + ) + opt_ins_root = gr.Textbox( + label=i18n("指定输出非主人声文件夹"), value="opt" + ) + format0 = gr.Radio( + label=i18n("导出文件格式"), + choices=["wav", "flac", "mp3", "m4a"], + value="flac", + interactive=True, + ) + but2 = gr.Button(i18n("转换"), variant="primary") + vc_output4 = gr.Textbox(label=i18n("输出信息")) + but2.click( + uvr, + [ + model_choose, + dir_wav_input, + opt_vocal_root, + wav_inputs, + opt_ins_root, + agg, + format0, + ], + [vc_output4], + ) + with gr.TabItem(i18n("训练")): + gr.Markdown( + value=i18n( + "step1: 填写实验配置. 实验数据放在logs下, 每个实验一个文件夹, 需手工输入实验名路径, 内含实验配置, 日志, 训练得到的模型文件. " + ) + ) + with gr.Row(): + exp_dir1 = gr.Textbox(label=i18n("输入实验名"), value="mi-test") + sr2 = gr.Radio( + label=i18n("目标采样率"), + choices=["40k", "48k"], + value="40k", + interactive=True, + ) + if_f0_3 = gr.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"), + choices=["v1", "v2"], + value="v1", + interactive=True, + visible=True, + ) + np7 = gr.Slider( + minimum=0, + maximum=config.n_cpu, + step=1, + label=i18n("提取音高和处理数据使用的CPU进程数"), + value=int(np.ceil(config.n_cpu / 1.5)), + interactive=True, + ) + with gr.Group(): # 暂时单人的, 后面支持最多4人的#数据处理 + gr.Markdown( + value=i18n( + "step2a: 自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练. " + ) + ) + with gr.Row(): + trainset_dir4 = gr.Textbox( + label=i18n("输入训练文件夹路径"), value=os.path.abspath(os.getcwd()) + "\\datasets\\" + ) + spk_id5 = gr.Slider( + minimum=0, + maximum=4, + step=1, + label=i18n("请指定说话人id"), + value=0, + interactive=True, + ) + but1 = gr.Button(i18n("处理数据"), variant="primary") + info1 = gr.Textbox(label=i18n("输出信息"), value="") + but1.click( + preprocess_dataset, [trainset_dir4, exp_dir1, sr2, np7], [info1] + ) + with gr.Group(): + gr.Markdown(value=i18n("step2b: 使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号)")) + with gr.Row(): + with gr.Column(): + gpus6 = gr.Textbox( + label=i18n("以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2"), + value=gpus, + interactive=True, + ) + gpu_info9 = gr.Textbox(label=i18n("显卡信息"), value=gpu_info) + with gr.Column(): + f0method8 = gr.Radio( + label=i18n( + "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢" + ), + choices=["pm", "harvest", "dio", "crepe", "mangio-crepe", "rmvpe"], # Fork feature: Crepe on f0 extraction for training. + value="rmvpe", + interactive=True, + ) + extraction_crepe_hop_length = gr.Slider( + minimum=1, + maximum=512, + step=1, + label=i18n("crepe_hop_length"), + value=64, + interactive=True + ) + 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, extraction_crepe_hop_length], + [info2], + ) + with gr.Group(): + gr.Markdown(value=i18n("step3: 填写训练设置, 开始训练模型和索引")) + with gr.Row(): + save_epoch10 = gr.Slider( + minimum=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=10000, + step=1, + label=i18n("总训练轮数total_epoch"), + value=20, + interactive=True, + ) + batch_size12 = gr.Slider( + minimum=1, + maximum=40, + step=1, + label=i18n("每张显卡的batch_size"), + value=default_batch_size, + interactive=True, + ) + if_save_latest13 = gr.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("是"), + 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("是"), + interactive=True, + ) + with gr.Row(): + pretrained_G14 = gr.Textbox( + lines=2, + label=i18n("加载预训练底模G路径"), + value="pretrained/f0G40k.pth", + interactive=True, + ) + pretrained_D15 = gr.Textbox( + lines=2, + label=i18n("加载预训练底模D路径"), + value="pretrained/f0D40k.pth", + interactive=True, + ) + sr2.change( + change_sr2, + [sr2, if_f0_3, version19], + [pretrained_G14, pretrained_D15], + ) + version19.change( + change_version19, + [sr2, if_f0_3, version19], + [pretrained_G14, pretrained_D15, sr2], + ) + if_f0_3.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, + # extraction_crepe_hop_length + # ], + # info3, + #) + + with gr.TabItem(i18n("ckpt处理")): + with gr.Group(): + gr.Markdown(value=i18n("模型融合, 可用于测试音色融合")) + with gr.Row(): + ckpt_a = gr.Textbox(label=i18n("A模型路径"), value="", interactive=True) + 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=["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( + lines=3, + label=i18n("模型路径"), + value=os.path.abspath(os.getcwd()).replace('\\', '/') + "/logs/[YOUR_MODEL]/G_23333.pth", + interactive=True, + ) + save_name = gr.Textbox( + label=i18n("保存名"), value="", interactive=True + ) + 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="v2", + 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(): + infoOnnx = gr.Label(label="info") + with gr.Row(): + butOnnx = gr.Button(i18n("导出Onnx模型"), variant="primary") + butOnnx.click(export_onnx, [ckpt_dir, onnx_dir], infoOnnx) + + tab_faq = i18n("常见问题解答") + with gr.TabItem(tab_faq): + try: + if tab_faq == "常见问题解答": + with open("docs/faq.md", "r", encoding="utf8") as f: + info = f.read() + else: + with open("docs/faq_en.md", "r", encoding="utf8") as f: + info = f.read() + gr.Markdown(value=info) + except: + gr.Markdown(traceback.format_exc()) + + + #region Mangio Preset Handler Region + def save_preset( + preset_name, + sid0, + vc_transform, + input_audio0, + input_audio1, + f0method, + crepe_hop_length, + filter_radius, + file_index1, + file_index2, + index_rate, + resample_sr, + rms_mix_rate, + protect, + f0_file + ): + data = None + with open('../inference-presets.json', 'r') as file: + data = json.load(file) + preset_json = { + 'name': preset_name, + 'model': sid0, + 'transpose': vc_transform, + 'audio_file': input_audio0, + 'auto_audio_file': input_audio1, + 'f0_method': f0method, + 'crepe_hop_length': crepe_hop_length, + 'median_filtering': filter_radius, + 'feature_path': file_index1, + 'auto_feature_path': file_index2, + 'search_feature_ratio': index_rate, + 'resample': resample_sr, + 'volume_envelope': rms_mix_rate, + 'protect_voiceless': protect, + 'f0_file_path': f0_file + } + data['presets'].append(preset_json) + with open('../inference-presets.json', 'w') as file: + json.dump(data, file) + file.flush() + print("Saved Preset %s into inference-presets.json!" % preset_name) + + + def on_preset_changed(preset_name): + print("Changed Preset to %s!" % preset_name) + data = None + with open('../inference-presets.json', 'r') as file: + data = json.load(file) + + print("Searching for " + preset_name) + returning_preset = None + for preset in data['presets']: + if(preset['name'] == preset_name): + print("Found a preset") + returning_preset = preset + # return all new input values + return ( + # returning_preset['model'], + # returning_preset['transpose'], + # returning_preset['audio_file'], + # returning_preset['f0_method'], + # returning_preset['crepe_hop_length'], + # returning_preset['median_filtering'], + # returning_preset['feature_path'], + # returning_preset['auto_feature_path'], + # returning_preset['search_feature_ratio'], + # returning_preset['resample'], + # returning_preset['volume_envelope'], + # returning_preset['protect_voiceless'], + # returning_preset['f0_file_path'] + ) + + # Preset State Changes + + # This click calls save_preset that saves the preset into inference-presets.json with the preset name + # mangio_preset_save_btn.click( + # fn=save_preset, + # inputs=[ + # mangio_preset_name_save, + # sid0, + # vc_transform0, + # input_audio0, + # f0method0, + # crepe_hop_length, + # filter_radius0, + # file_index1, + # file_index2, + # index_rate1, + # resample_sr0, + # rms_mix_rate0, + # protect0, + # f0_file + # ], + # outputs=[] + # ) + + # mangio_preset.change( + # on_preset_changed, + # inputs=[ + # # Pass inputs here + # mangio_preset + # ], + # outputs=[ + # # Pass Outputs here. These refer to the gradio elements that we want to directly change + # # sid0, + # # vc_transform0, + # # input_audio0, + # # f0method0, + # # crepe_hop_length, + # # filter_radius0, + # # file_index1, + # # file_index2, + # # index_rate1, + # # resample_sr0, + # # rms_mix_rate0, + # # protect0, + # # f0_file + # ] + # ) + #endregion + + # with gr.TabItem(i18n("招募音高曲线前端编辑器")): + # gr.Markdown(value=i18n("加开发群联系我xxxxx")) + # with gr.TabItem(i18n("点击查看交流、问题反馈群号")): + # gr.Markdown(value=i18n("xxxxx")) + + if config.iscolab or config.paperspace: # Share gradio link for colab and paperspace (FORK FEATURE) + app.queue(concurrency_count=511, max_size=1022).launch(share=True) + else: + app.queue(concurrency_count=511, max_size=1022).launch( + server_name="0.0.0.0", + inbrowser=not config.noautoopen, + server_port=config.listen_port, + quiet=True, + ) + +#endregion +''' \ No newline at end of file diff --git a/installstft.bat b/installstft.bat new file mode 100644 index 0000000..8d57717 --- /dev/null +++ b/installstft.bat @@ -0,0 +1,6 @@ +runtime\python.exe -m pip install stftpitchshift --upgrade +runtime\python.exe -m pip install gradio==3.34.0 --upgrade + + +IF EXIST ".\rmvpe.pt" (echo RMVPE is already installed) ELSE (bitsadmin /transfer "rvmpedwnld" /download /priority FOREGROUND "https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/rmvpe.pt" "%~dp0rmvpe.pt") +pause \ No newline at end of file diff --git a/my_utils.py b/my_utils.py index 776939d..1990d82 100644 --- a/my_utils.py +++ b/my_utils.py @@ -1,8 +1,13 @@ import ffmpeg import numpy as np +#import praatio +#import praatio.praat_scripts +import os +#from os.path import join +#praatEXE = join('.',os.path.abspath(os.getcwd()) + r"\Praat.exe") -def load_audio(file, sr): +def load_audio(file, sr, DoFormant, Quefrency, Timbre): try: # https://github.com/openai/whisper/blob/main/whisper/audio.py#L26 # This launches a subprocess to decode audio while down-mixing and resampling as necessary. @@ -10,11 +15,44 @@ def load_audio(file, sr): file = ( file.strip(" ").strip('"').strip("\n").strip('"').strip(" ") ) # 防止小白拷路径头尾带了空格和"和回车 - out, _ = ( - ffmpeg.input(file, threads=0) - .output("-", format="f32le", acodec="pcm_f32le", ac=1, ar=sr) - .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True) + file_formanted = ( + file.strip(" ").strip('"').strip("\n").strip('"').strip(" ") ) + with open('formanting.txt', 'r') as fvf: + content = fvf.readlines() + if 'True' in content[0].split('\n')[0]: + #print("true") + DoFormant = True + Quefrency, Timbre = content[1].split('\n')[0], content[2].split('\n')[0] + + else: + #print("not true") + DoFormant = False + + if DoFormant: + #os.system(f"stftpitchshift -i {file} -q {Quefrency} -t {Timbre} -o {file_formanted}") + #print('stftpitchshift -i "%s" -p 1.0 --rms -w 128 -v 8 -q %s -t %s -o "%s"' % (file, Quefrency, Timbre, file_formanted)) + print("formanting...") + + os.system('stftpitchshift -i "%s" -q %s -t %s -o "%sFORMANTED"' % (file, Quefrency, Timbre, file_formanted)) + print("formanted!") + #filepraat = (os.path.abspath(os.getcwd()) + '\\' + file).replace('/','\\') + #file_formantedpraat = ('"' + os.path.abspath(os.getcwd()) + '/' + 'formanted'.join(file_formanted) + '"').replace('/','\\') + + out, _ = ( + ffmpeg.input('%sFORMANTED%s' % (file_formanted, '.wav'), threads=0) + .output("-", format="f32le", acodec="pcm_f32le", ac=1, ar=sr) + .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True) + ) + + os.remove('%sFORMANTED%s' % (file_formanted, '.wav')) + else: + + out, _ = ( + ffmpeg.input(file, threads=0) + .output("-", format="f32le", acodec="pcm_f32le", ac=1, ar=sr) + .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True) + ) except Exception as e: raise RuntimeError(f"Failed to load audio: {e}") From 7c778bdca143cf82786c22568236364dc277e5d0 Mon Sep 17 00:00:00 2001 From: alexlnkp <79400603+alexlnkp@users.noreply.github.com> Date: Fri, 21 Jul 2023 20:59:59 +0700 Subject: [PATCH 02/14] Update README.md --- README.md | 79 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 79 insertions(+) diff --git a/README.md b/README.md index 4a57ed3..1457ad3 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,82 @@ +# Features: +- Experimental Formant Shift using StftPitchShift(tried using praat with praatio but to no avail) +- Auto-detect Index path for models selected + Auto-detect paths, no more default values like this: `E:\codes\py39\vits_vc_gpu_train\logs\mi-test-1key\total_fea.npy`, We're getting Root Dir and subfolders using +```python +os.path.abspath(os.getcwd()) +``` +- Audio files dropdown by autodetecting files in `/audios/` folder +- More stable version of Gradio(3.34.0) with support of Themes +- Removed `One Click Training` button in `Training` Tab, since it was glitchy and has confused a lot of users. +- Changed default training settings to be more optimal for newer users. +- Auto-open TensorBoard localhost URL when `tensor-launch.py` is executed +- RMVPE implemented in both inferencing and training (the one in `Training` tab doesn't work properly though, requires some additional work to do) + +# Known bugs: + +- `Unload voice to save GPU memory` button causes a Traceback: + +```python +Traceback (most recent call last): + File "D:\RVC-beta-v2-0618\runtime\lib\site-packages\gradio\routes.py", line 437, in run_predict + output = await app.get_blocks().process_api( + File "D:\RVC-beta-v2-0618\runtime\lib\site-packages\gradio\blocks.py", line 1349, in process_api + data = self.postprocess_data(fn_index, result["prediction"], state) + File "D:\RVC-beta-v2-0618\runtime\lib\site-packages\gradio\blocks.py", line 1252, in postprocess_data + self.validate_outputs(fn_index, predictions) # type: ignore + File "D:\RVC-beta-v2-0618\runtime\lib\site-packages\gradio\blocks.py", line 1227, in validate_outputs + raise ValueError( +ValueError: An event handler (get_vc) didn't receive enough output values (needed: 3, received: 1). +Wanted outputs: + [slider, slider, slider] +Received outputs: + [{'visible': False, '__type__': 'update'}] +``` + +## Installation: + +1. Simply either extract directly or use git clone + +2. Run `installstft.bat`. It'll automatically: + - Upgrade/Downgrade Gradio if its version isn't 3.34.0; + - Install `rmvpe.pt` if it hasn't been already installed; + - Install `StftPitchShift` if it hasn't been already installed; + + + +3. Done! You're good to go and use the RVC-WebUI Tweaked by me for you to use :) + +## Change Gradio Theme: + +- [OPTIONAL] Change Gradio's theme: + 1. Open `infer-web.py` in any code/text editing software (e.g. `notepad++`, `notepad`, `vscode`, etc) + + 2a. Press Ctrl+F and search for `with gr.Blocks(`, select the one that's not fully commented + + 2b. Go to line `1842`, you'll see the `with gr.Blocks(theme='HaleyCH/HaleyCH_Theme') as app:` + + 3. Go to [Gradio Theme Gallery](https://huggingface.co/spaces/gradio/theme-gallery): + + 3.1 Select any theme you like (e.g. [this one](https://huggingface.co/spaces/freddyaboulton/dracula_revamped)) + + 3.2 Look at the top of the page + + ![image](https://github.com/alexlnkp/Mangio-RVC-Tweaks/assets/79400603/59e3e6a9-bdda-4ede-8161-00ee957c1715) + + 3.3 Copy theme variable(in this case, it's `theme='freddyaboulton/dracula_revamped'`) + + 4. Replace `theme='HaleyCH/HaleyCH_Theme'` in `infer-web.py` with any value of a theme from [Gradio Theme Gallery](https://huggingface.co/spaces/gradio/theme-gallery) + +### Current Todo-list: + +- [ ] Fix `Unload voice to save GPU memory` button Traceback +- [ ] Add Accordions so people with Firefox browser get a much more compact GUI rather than [This](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/assets/79400603/67e0cc08-82a2-4dc3-86cf-e23d1dcad9f8). +- [ ] Fix weird way Median Filtering value inputted in a slider is utilized +- [ ] Replace regular refresh buttons with these tiny ones from [AUTOMATIC'S1111 Stable DIffusion](https://github.com/AUTOMATIC1111/stable-diffusion-webui) +![image](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/assets/79400603/fe37051e-4c95-4d30-9254-87d44436bb9e) +- [ ] Add a way to change the Gradio's theme from WebUI itself, like in [AUTOMATIC'S1111 Stable DIffusion](https://github.com/AUTOMATIC1111/stable-diffusion-webui) +![image](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/assets/79400603/7b93e167-564a-41d3-9a76-ded20063cdc3) +- [ ] Implement Praat in the GUI for f0 curve file manipulation and easier usage +

Mangio-RVC-Fork with v2 Support! 💻

A fork of an easy-to-use SVC framework based on VITS with top1 retrieval 💯. In general, this fork provides a CLI interface in addition. And also gives you more f0 methods to use, as well as a personlized 'hybrid' f0 estimation method using nanmedian.

From 0e5d725b340c9d5683c855e8d015da73d154498a Mon Sep 17 00:00:00 2001 From: alexlnkp <79400603+alexlnkp@users.noreply.github.com> Date: Fri, 21 Jul 2023 21:40:27 +0700 Subject: [PATCH 03/14] Update infer-web.py fixed gpu unload traceback :) --- infer-web.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/infer-web.py b/infer-web.py index 02b306b..7811749 100644 --- a/infer-web.py +++ b/infer-web.py @@ -518,7 +518,7 @@ def get_vc(sid, to_return_protect0, to_return_protect1): if torch.cuda.is_available(): torch.cuda.empty_cache() cpt = None - return {"visible": False, "__type__": "update"} + return ({"visible": False, "__type__": "update"}, {"visible": False, "__type__": "update"}, {"visible": False, "__type__": "update"}) person = "%s/%s" % (weight_root, sid) print("loading %s" % person) cpt = torch.load(person, map_location="cpu") @@ -3695,4 +3695,4 @@ with gr.Blocks(theme='HaleyCH/HaleyCH_Theme') as app: ) #endregion -''' \ No newline at end of file +''' From 72539f737ab560f9c7acf9a7529a164166dd540f Mon Sep 17 00:00:00 2001 From: alexlnkp <79400603+alexlnkp@users.noreply.github.com> Date: Fri, 21 Jul 2023 21:42:07 +0700 Subject: [PATCH 04/14] Update README.md --- README.md | 23 +---------------------- 1 file changed, 1 insertion(+), 22 deletions(-) diff --git a/README.md b/README.md index 1457ad3..b0c57fc 100644 --- a/README.md +++ b/README.md @@ -11,27 +11,6 @@ os.path.abspath(os.getcwd()) - Auto-open TensorBoard localhost URL when `tensor-launch.py` is executed - RMVPE implemented in both inferencing and training (the one in `Training` tab doesn't work properly though, requires some additional work to do) -# Known bugs: - -- `Unload voice to save GPU memory` button causes a Traceback: - -```python -Traceback (most recent call last): - File "D:\RVC-beta-v2-0618\runtime\lib\site-packages\gradio\routes.py", line 437, in run_predict - output = await app.get_blocks().process_api( - File "D:\RVC-beta-v2-0618\runtime\lib\site-packages\gradio\blocks.py", line 1349, in process_api - data = self.postprocess_data(fn_index, result["prediction"], state) - File "D:\RVC-beta-v2-0618\runtime\lib\site-packages\gradio\blocks.py", line 1252, in postprocess_data - self.validate_outputs(fn_index, predictions) # type: ignore - File "D:\RVC-beta-v2-0618\runtime\lib\site-packages\gradio\blocks.py", line 1227, in validate_outputs - raise ValueError( -ValueError: An event handler (get_vc) didn't receive enough output values (needed: 3, received: 1). -Wanted outputs: - [slider, slider, slider] -Received outputs: - [{'visible': False, '__type__': 'update'}] -``` - ## Installation: 1. Simply either extract directly or use git clone @@ -68,7 +47,7 @@ Received outputs: ### Current Todo-list: -- [ ] Fix `Unload voice to save GPU memory` button Traceback +- [x] Fix `Unload voice to save GPU memory` button Traceback - [ ] Add Accordions so people with Firefox browser get a much more compact GUI rather than [This](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/assets/79400603/67e0cc08-82a2-4dc3-86cf-e23d1dcad9f8). - [ ] Fix weird way Median Filtering value inputted in a slider is utilized - [ ] Replace regular refresh buttons with these tiny ones from [AUTOMATIC'S1111 Stable DIffusion](https://github.com/AUTOMATIC1111/stable-diffusion-webui) From 4abc7f23ead8ab5d3291b91337c2360c79d8383f Mon Sep 17 00:00:00 2001 From: alexlnkp <79400603+alexlnkp@users.noreply.github.com> Date: Fri, 21 Jul 2023 22:16:29 +0700 Subject: [PATCH 05/14] Update README.md --- README.md | 37 +++++++++++++++++++++++++++++++++++++ 1 file changed, 37 insertions(+) diff --git a/README.md b/README.md index b0c57fc..83710c1 100644 --- a/README.md +++ b/README.md @@ -56,6 +56,43 @@ os.path.abspath(os.getcwd()) ![image](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/assets/79400603/7b93e167-564a-41d3-9a76-ded20063cdc3) - [ ] Implement Praat in the GUI for f0 curve file manipulation and easier usage + +# Screenshots: + +- ## Inference Tab: + +![image](https://github.com/Mangio621/Mangio-RVC-Fork/assets/79400603/107aa15a-4e8d-4f77-a327-45f35a235fcf) + +- ## UVR Tab: + +![image](https://github.com/Mangio621/Mangio-RVC-Fork/assets/79400603/7e57242a-4950-40c8-bf2a-8f77e992af26) + +- ## Training Tab: + +![image](https://github.com/Mangio621/Mangio-RVC-Fork/assets/79400603/a19ce156-5532-4761-aa06-8a537f80c368) + +- ## Ckpt-Processing Tab: + +![image](https://github.com/Mangio621/Mangio-RVC-Fork/assets/79400603/0cdc285e-a184-48f3-92a7-65f6120caf2f) + +The rest of the tabs are left untouched code-wise. + + + +# Formant Shift: + +![image](https://github.com/Mangio621/Mangio-RVC-Fork/assets/79400603/300ebce2-36c7-4761-b1dd-b31403ad2cd1) + +- ### Click `Apply` button every time you change the values for inferencing. + +- ### As the name implies, you can only use `wav` files so far, also it is very slow, so be patient. + +- ### If you added a new `preset.txt` in the `\formantshiftcfg\` folder, click button with refresh emoji + +- ### If the preset you selected somehow got edited, by pressing refresh emoji button you'll update values, by grabbing them from the file + + +

Mangio-RVC-Fork with v2 Support! 💻

A fork of an easy-to-use SVC framework based on VITS with top1 retrieval 💯. In general, this fork provides a CLI interface in addition. And also gives you more f0 methods to use, as well as a personlized 'hybrid' f0 estimation method using nanmedian.

From fb726751a734821b9c1d5ec055873f1e3a353c33 Mon Sep 17 00:00:00 2001 From: alexlnkp <79400603+alexlnkp@users.noreply.github.com> Date: Sat, 22 Jul 2023 20:30:54 +0700 Subject: [PATCH 06/14] Add files via upload forgot to upload it hehe --- tensor-launch.py | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) create mode 100644 tensor-launch.py diff --git a/tensor-launch.py b/tensor-launch.py new file mode 100644 index 0000000..511335a --- /dev/null +++ b/tensor-launch.py @@ -0,0 +1,16 @@ +import threading +import time +from tensorboard import program +import webbrowser + +log_path = "logs" + +if __name__ == "__main__": + tb = program.TensorBoard() + tb.configure(argv=[None, '--logdir', log_path]) + url = tb.launch() + webbrowser.open_new(url) + print(f'Tensorboard can be accessed at: {url}') + + while True: + time.sleep(600) # Keep the main thread running \ No newline at end of file From 21dba2f37bcdd962258cb7a12e19591849e09422 Mon Sep 17 00:00:00 2001 From: alexlnkp <79400603+alexlnkp@users.noreply.github.com> Date: Sun, 23 Jul 2023 03:34:59 +0700 Subject: [PATCH 07/14] Add files via upload fixed broken training due to missing arguments of load_audio function --- extract_f0_print.py | 16 +++++++++++++++- trainset_preprocess_pipeline_print.py | 9 ++++++++- 2 files changed, 23 insertions(+), 2 deletions(-) diff --git a/extract_f0_print.py b/extract_f0_print.py index e175e5e..3290125 100644 --- a/extract_f0_print.py +++ b/extract_f0_print.py @@ -17,6 +17,12 @@ from multiprocessing import Process exp_dir = sys.argv[1] f = open("%s/extract_f0_feature.log" % exp_dir, "a+") +DoFormant = False + +with open('formanting.txt', 'r') as fvf: + content = fvf.readlines() + Quefrency, Timbre = content[1].split('\n')[0], content[2].split('\n')[0] + def printt(strr): print(strr) @@ -199,7 +205,7 @@ class FeatureInput(object): return f0_median_hybrid def compute_f0(self, path, f0_method, crepe_hop_length): - x = load_audio(path, self.fs) + x = load_audio(path, self.fs, DoFormant, Quefrency, Timbre) p_len = x.shape[0] // self.hop if f0_method == "pm": time_step = 160 / 16000 * 1000 @@ -227,6 +233,14 @@ class FeatureInput(object): frame_period=1000 * self.hop / self.fs, ) f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.fs) + elif f0_method == "rmvpe": + if hasattr(self, "model_rmvpe") == False: + from rmvpe import RMVPE + print("loading rmvpe model") + self.model_rmvpe = RMVPE( + "rmvpe.pt", is_half=False, device="cuda:0" + ) + f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03) elif f0_method == "dio": f0, t = pyworld.dio( x.astype(np.double), diff --git a/trainset_preprocess_pipeline_print.py b/trainset_preprocess_pipeline_print.py index f16761a..185cf29 100644 --- a/trainset_preprocess_pipeline_print.py +++ b/trainset_preprocess_pipeline_print.py @@ -17,9 +17,16 @@ import multiprocessing from my_utils import load_audio import tqdm +DoFormant = False +Quefrency = 0.0 +Timbre = 0.0 + mutex = multiprocessing.Lock() f = open("%s/preprocess.log" % exp_dir, "a+") +with open('formanting.txt', 'r') as fvf: + content = fvf.readlines() + Quefrency, Timbre = content[1].split('\n')[0], content[2].split('\n')[0] def println(strr): mutex.acquire() @@ -77,7 +84,7 @@ class PreProcess: def pipeline(self, path, idx0): try: - audio = load_audio(path, self.sr) + audio = load_audio(path, self.sr, DoFormant, Quefrency, Timbre) # zero phased digital filter cause pre-ringing noise... # audio = signal.filtfilt(self.bh, self.ah, audio) audio = signal.lfilter(self.bh, self.ah, audio) From 978eaabee25fb8615ce68bdf388e521e15ccad87 Mon Sep 17 00:00:00 2001 From: alexlnkp <79400603+alexlnkp@users.noreply.github.com> Date: Sun, 23 Jul 2023 05:49:24 +0700 Subject: [PATCH 08/14] Add files via upload fixed training tracebacks and added Stop Training button --- infer-web.py | 52 +++++++++++++++++++++--- train_nsf_sim_cache_sid_load_pretrain.py | 23 ++++++++++- 2 files changed, 68 insertions(+), 7 deletions(-) diff --git a/infer-web.py b/infer-web.py index 7811749..68a2af3 100644 --- a/infer-web.py +++ b/infer-web.py @@ -3,6 +3,8 @@ import shutil import sys import json # Mangio fork using json for preset saving +import signal + now_dir = os.getcwd() sys.path.append(now_dir) import traceback, pdb @@ -73,6 +75,8 @@ gpu_infos = [] mem = [] if_gpu_ok = False +isinterrupted = 0 + if torch.cuda.is_available() or ngpu != 0: for i in range(ngpu): gpu_name = torch.cuda.get_device_name(i) @@ -587,7 +591,7 @@ def change_choices(): def clean(): - return {"value": "", "__type__": "update"} + return ({"value": "", "__type__": "update"}) sr_dict = { @@ -1048,7 +1052,11 @@ def click_train( ) ) print(cmd) + global p p = Popen(cmd, shell=True, cwd=now_dir) + global PID + PID = p.pid + p.wait() return "训练结束, 您可查看控制台训练日志或实验文件夹下的train.log" @@ -1838,6 +1846,24 @@ def choveraudio(): return '' +def stoptraining(mim): + if int(mim) == 1: + + with open("stop.txt", "w+") as tostops: + + + tostops.writelines('stop') + #p.terminate() + #p.kill() + os.kill(PID, signal.SIGTERM) + else: + pass + + return ( + {"visible": False, "__type__": "update"}, + {"visible": True, "__type__": "update"}, + ) + #Default-GUI with gr.Blocks(theme='HaleyCH/HaleyCH_Theme') as app: gr.HTML("

The Mangio-RVC-Fork 💻

") @@ -2415,10 +2441,20 @@ with gr.Blocks(theme='HaleyCH/HaleyCH_Theme') as app: value=gpus, interactive=True, ) - but3 = gr.Button(i18n("训练模型"), variant="primary") + butstop = gr.Button( + "Stop Training", + variant='primary', + visible=False, + ) + but3 = gr.Button(i18n("训练模型"), variant="primary", visible=True) + but3.click(fn=stoptraining, inputs=[gr.Number(value=0, visible=False)], outputs=[but3, butstop]) + butstop.click(fn=stoptraining, inputs=[gr.Number(value=1, visible=False)], outputs=[butstop, but3]) + + but4 = gr.Button(i18n("训练特征索引"), variant="primary") #but5 = gr.Button(i18n("一键训练"), variant="primary") info3 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=10) + but3.click( click_train, [ @@ -2437,9 +2473,13 @@ with gr.Blocks(theme='HaleyCH/HaleyCH_Theme') as app: if_save_every_weights18, version19, ], - info3, + [info3], ) + but4.click(train_index, [exp_dir1, version19], info3) + + + #but5.click( # train1key, # [ @@ -2464,7 +2504,7 @@ with gr.Blocks(theme='HaleyCH/HaleyCH_Theme') as app: # ], # 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")) @@ -2761,7 +2801,7 @@ with gr.Blocks(theme='HaleyCH/HaleyCH_Theme') as app: server_name="0.0.0.0", inbrowser=not config.noautoopen, server_port=config.listen_port, - quiet=True, + quiet=False, ) #endregion @@ -3695,4 +3735,4 @@ with gr.Blocks(theme='HaleyCH/HaleyCH_Theme') as app: ) #endregion -''' +''' \ No newline at end of file diff --git a/train_nsf_sim_cache_sid_load_pretrain.py b/train_nsf_sim_cache_sid_load_pretrain.py index 3394bdd..5bb3526 100644 --- a/train_nsf_sim_cache_sid_load_pretrain.py +++ b/train_nsf_sim_cache_sid_load_pretrain.py @@ -568,7 +568,28 @@ def train_and_evaluate( ), ) ) - + + with open("stop.txt", "r+") as tostop: + content = tostop.read() + if 'stop' in content: + logger.info("Stop Button was pressed. The program is closed.") + if hasattr(net_g, "module"): + ckpt = net_g.module.state_dict() + else: + ckpt = net_g.state_dict() + logger.info( + "saving final ckpt:%s" + % ( + savee( + ckpt, hps.sample_rate, hps.if_f0, hps.name, epoch, hps.version, hps + ) + ) + ) + + tostop.truncate(0) + tostop.writelines("not") + os._exit(2333333) + if rank == 0: logger.info("====> Epoch: {} {}".format(epoch, epoch_recorder.record())) if epoch >= hps.total_epoch and rank == 0: From 263fa69f05012b12acc56c7c7aac6dbfb3e5ad67 Mon Sep 17 00:00:00 2001 From: alexlnkp <79400603+alexlnkp@users.noreply.github.com> Date: Sun, 23 Jul 2023 05:50:22 +0700 Subject: [PATCH 09/14] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 83710c1..95b1410 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,6 @@ # Features: - Experimental Formant Shift using StftPitchShift(tried using praat with praatio but to no avail) +- Added `Stop Training` button when training, no need to restart RVC every time you want to stop the training of a model! - Auto-detect Index path for models selected + Auto-detect paths, no more default values like this: `E:\codes\py39\vits_vc_gpu_train\logs\mi-test-1key\total_fea.npy`, We're getting Root Dir and subfolders using ```python os.path.abspath(os.getcwd()) From 385d7c57f3f78c0bcca7715e68409f5808b3a8cb Mon Sep 17 00:00:00 2001 From: alexlnkp <79400603+alexlnkp@users.noreply.github.com> Date: Sun, 23 Jul 2023 05:54:04 +0700 Subject: [PATCH 10/14] Add files via upload --- stop.txt | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 stop.txt diff --git a/stop.txt b/stop.txt new file mode 100644 index 0000000..e69de29 From 5c6a9066f59e1c423c6598cd0d21a20e1093b23c Mon Sep 17 00:00:00 2001 From: alexlnkp <79400603+alexlnkp@users.noreply.github.com> Date: Sun, 23 Jul 2023 05:59:53 +0700 Subject: [PATCH 11/14] Add files via upload --- requirements.txt | 2 ++ 1 file changed, 2 insertions(+) diff --git a/requirements.txt b/requirements.txt index 8965df5..6ec9214 100644 --- a/requirements.txt +++ b/requirements.txt @@ -45,3 +45,5 @@ httpx==0.23.0 #onnxruntime-gpu torchcrepe==0.0.20 fastapi==0.88 +stftpitchshift==1.5.1 +gradio==3.34.0 From cc14c1746e8df9aab47117d344ba4e66a71195b7 Mon Sep 17 00:00:00 2001 From: alexlnkp <79400603+alexlnkp@users.noreply.github.com> Date: Sun, 23 Jul 2023 06:00:31 +0700 Subject: [PATCH 12/14] Add files via upload --- requirements.txt | 1 - 1 file changed, 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 6ec9214..3103d5e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -46,4 +46,3 @@ httpx==0.23.0 torchcrepe==0.0.20 fastapi==0.88 stftpitchshift==1.5.1 -gradio==3.34.0 From a3a651d4f8638d7a8bede2e63f5daf4cd4d73cd5 Mon Sep 17 00:00:00 2001 From: alexlnkp <79400603+alexlnkp@users.noreply.github.com> Date: Sun, 23 Jul 2023 06:04:36 +0700 Subject: [PATCH 13/14] Add files via upload From 25265bbe301cda9ae3881785e2a6ff2fc22fe6db Mon Sep 17 00:00:00 2001 From: kalomaze <66376113+kalomaze@users.noreply.github.com> Date: Sat, 22 Jul 2023 18:11:29 -0500 Subject: [PATCH 14/14] Removed non-fork files --- installstft.bat | 6 ------ tensor-launch.py | 16 ---------------- 2 files changed, 22 deletions(-) delete mode 100644 installstft.bat delete mode 100644 tensor-launch.py diff --git a/installstft.bat b/installstft.bat deleted file mode 100644 index 8d57717..0000000 --- a/installstft.bat +++ /dev/null @@ -1,6 +0,0 @@ -runtime\python.exe -m pip install stftpitchshift --upgrade -runtime\python.exe -m pip install gradio==3.34.0 --upgrade - - -IF EXIST ".\rmvpe.pt" (echo RMVPE is already installed) ELSE (bitsadmin /transfer "rvmpedwnld" /download /priority FOREGROUND "https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/rmvpe.pt" "%~dp0rmvpe.pt") -pause \ No newline at end of file diff --git a/tensor-launch.py b/tensor-launch.py deleted file mode 100644 index 511335a..0000000 --- a/tensor-launch.py +++ /dev/null @@ -1,16 +0,0 @@ -import threading -import time -from tensorboard import program -import webbrowser - -log_path = "logs" - -if __name__ == "__main__": - tb = program.TensorBoard() - tb.configure(argv=[None, '--logdir', log_path]) - url = tb.launch() - webbrowser.open_new(url) - print(f'Tensorboard can be accessed at: {url}') - - while True: - time.sleep(600) # Keep the main thread running \ No newline at end of file